How to Simulate Consciousness Using a Computer System

Copyright and Patent Information

Copyright 2001: Gregory J. Czora, All Rights Reserved

Blue Oak Mountain Technologies and its logo design is a registered servicemark of Blue Oak Mountain Technologies, Inc.

http://www.blueoakmountaintech.com/

Digital Life–Form, DLF, and DLF Simulation Technology are trademarks of

Blue Oak Mountain Technologies®, Inc.

There is a patent pending for DLF Simulation Technology™

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Cover Art

Gary Czora

Editing

Carrie Markley

An Autograph by the Author:

Version 1.1, Copy: 00XX, (download copy)

Table of Contents

Table of Contents ............................................................................................. 3

Chapter 1: The Context of Consciousness ..................................................... 9 Introduction. ................................................................................................ 9

Setting a Philosophical Context ........................................................... 16 Where to Start a Project to Simulate Consciousness? ............................... 24 What is Reality and Consciousness?. ........................................................ 29

The Data of Reality ..............................................................................31 Objects ........................................................................................... 35 Actions ........................................................................................... 37 Energy ............................................................................................38 Relationships. ................................................................................ 40

The Integration of the Parts of Reality ...................................................... 41 Summary ....................................................................................................43

Chapter 2: The “Biology” of Digital Life–forms ........................................ 45 Introduction. .............................................................................................. 45

Philosophy, Biology, and Consciousness ............................................ 46 Biological Life–forms. ............................................................................... 49

Self–Powered Objects. .........................................................................50 What is Unique about Life–forms? ..................................................... 51

Survival Requires Continuous Action ........................................... 52 The Concept of Emergent Properties ................................................... 54

Life Requires Goal–directed Action. ............................................. 55 The Higher Animals and Man....................................................................57

Purposeful Action ................................................................................ 58 Necessitated vs. Neutral Actions ......................................................... 58 Volitional Action ................................................................................. 60 Volition and Concepts. ........................................................................ 63

Man–made Objects ........................................................................ 65 Computer Simulations and Digital Life–forms. ........................................ 65

Layered Models ................................................................................... 67 Layered Models and Layer Substitution .............................................. 68 Layered Models and Context Boundaries. .......................................... 69 Complex Causality as an Emergent Property ...................................... 70 Digital “Biology” .................................................................................71

Summary ....................................................................................................72

Chapter 3: A Consciousness Simulator Design ........................................... 73 Introduction. .............................................................................................. 73 A Design Overview of Digital Life–Forms (DLFs) .................................. 75

Simulating Life and Death in a Computer ........................................... 76 Duplicating Levels of Complex Cause and Effect .............................. 78

What Separates a Living System from the World? ............................. 79 Action Control in Biological Life–forms and DLFs. .......................... 81 Simulating the Higher Cognitive Functions ........................................ 84

Simulating Perceptual Consciousness. ...................................................... 86 The Five C.Events in the DLF Program .............................................. 87 Simulating Sensation ........................................................................... 89

The World ......................................................................................89 Energy Transfer and Sensing ......................................................... 90 Identity Transfer ............................................................................90

Simulating Perception ..........................................................................91 The World as Objects ....................................................................91 Energy and Identity Transfer ......................................................... 92 Sensation ........................................................................................99 Perception .................................................................................... 101

Simulating Evaluation (Feelings) ...................................................... 107 Simulating One Form of Pleasure and Pain ................................. 107 Other Simulated Feelings ............................................................ 116

Automatic Action Selection ............................................................... 120 Memory ............................................................................................. 135 Action ................................................................................................ 139

The Action Driver Class Hierarchy ............................................. 140 Simulated Perceptual Consciousness in Action ....................................... 145

Consciousness: The “Movie” ............................................................ 145 The Transition to Simulating Volitional Consciousness ................... 147

Summary ..................................................................................................148

Chapter 4: Explaining Self–Consciousness ............................................... 151 Introduction ............................................................................................. 151 The Emergence of Simulated Conceptual Consciousness ....................... 154

Topping Off the Layered Model ........................................................ 155 The Survival Value of Concepts ........................................................ 173 How DLFs Form Concepts ................................................................ 178 How Conceptual Level Consciousness Emerges ............................... 182 Boot Strapping More Complex Choices ............................................ 188 Concepts of Causality ........................................................................ 189 Concepts of Consciousness ............................................................... 193

Simulating Volition and Self–Consciousness .......................................... 194 Axiomatic Concepts Make Self–Awareness Possible ....................... 196 Axiomatic Concepts and Full Volitional Control. ............................. 201 Axiomatic Concepts Make Natural Language Possible ....................202 The Spiral Theory of Learning in DLFs ............................................ 207

Two Interesting Scientific Discoveries .................................................... 209 “Nano–Biology” ................................................................................ 209 Perceiving the Identity of Objects......................................................210

Summary ..................................................................................................211

Chapter 5: How to Simulate Consciousness .............................................. 213 Introduction. ............................................................................................ 213

A Few Prerequisite Ideas ................................................................... 227 Differences from the Current AI/AL State of the Art ....................... 245

State of the Art Concepts vs. Objective Concepts ....................... 245 Theoretical Differences ............................................................... 249 The Dynamic Memory of Roger Schank ..................................... 251 The “Animats” of Patti Maes ....................................................... 252 The Unintelligent Robots of Mark Tilden ................................... 254 Conclusions About the Current State of the Art .......................... 254 Design and Operational Differences in the Invention .................255

Biological vs. Digital Life–Forms ........................................................... 259 Computer Systems vs. Teleological Systems .................................... 263

Robotic vs. Goal–Directed Causality .......................................... 268 Computer vs. Teleological Action Definition ............................. 270

The Starting Point for Describing the Invention ............................... 272 A System Design for Simulating Conscious Life–forms ........................ 278

A Computer Network Analogy .......................................................... 278 Substituting Layers ............................................................................ 281

Setting Goals in a Computer Simulation System .................................... 284 What is Goal–Directed Behavior? ..................................................... 284 Interfacing Computer Systems to Value Systems. ............................ 288 Goal–Directed Simulation Logic: Teleologic .................................... 291 How to Write Your Own Goal–Directed Program ............................ 298 How a DLF Differs from the Current State of the Art ...................... 309 Creating More Complex DLFs .......................................................... 314

Adding Perceptual Consciousness to a DLF ........................................... 315 Sensing and Acting in a World. ......................................................... 317

Simulating Perception and the Identification of Objects ............. 318 Evaluating Objects ....................................................................... 336 Actions and Objects ..................................................................... 342 Memories ..................................................................................... 350 Action in a DLF’s World .............................................................354

The Conscious Event Cycle ............................................................... 357 Automatic Survival is at the Foundation of Life ............................... 366

Interacting with Memory ............................................................. 367 Recognition and Purposeful Action ............................................. 367 Automatic and Infallible .............................................................. 372

The Emergence of Volition in a DLF ...................................................... 374 A Simulation System Design to Calculate Concepts ............................... 385

The Nature of Concepts as a Data Type ............................................ 386 Concept Formation as a Calculation Process .................................... 389 Concepts, Memory, and Action Capacity .......................................... 415 How Simulated Conceptual Consciousness Emerges ....................... 421

The Emergence of Simulated Self–Consciousness ................................. 425 The “What if” Capacity of Conceptual Information ......................... 429

The Emergence of Simulated Natural Language in a DLF .....................438 The Role of Concepts in Simulating Language ................................. 439 Decoding Simple Sentences .............................................................. 442 Encoding Simple Sentences ............................................................... 443 Beyond Simple Sentences ................................................................. 445 The Simulation of a Fully Volitional DLF ........................................ 445

A Summary Description of the DLF Simulation System ........................ 449 Innovative Capabilities of the Invention .......................................... 450 The Invention is Useful ..................................................................... 453 Reduction to Practice ......................................................................... 455

Form or Product of the Invention ................................................ 455 General Summary .................................................................................... 458

Appendix A: References .............................................................................. 459 Introduction ............................................................................................. 459 References Lists ....................................................................................... 460

Primary References ............................................................................ 460 Differentiating References ................................................................. 462

Reference Citations from the Chapters. ................................................... 462 References for Chapter 1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 463 References for Chapter 2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 464 References for Chapter 3 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 465 References for Chapter 4 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 465 References for Chapter 5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 467

Index ............................................................................................................. 471

Dedication Page

This book is dedicated to:

My Mom and Dad.

My wife Paula Anton–Czora

William P. Doyle, III, a friend with whom I have spent untold hours discussing ideas on almost every subject imaginable.

Dr. James Spohrer, a research scientist and manager at the IBM Almaden Research Center in San Jose, CA., a scientist who has the objectivity to recognize the value of my ideas, even if they are a bit outside the mainstream.

Preface

I first thought of my idea of how to simulate consciousness using a computer system at about 5 AM one day in March of 1967 while shoveling coal into a boiler at Burgard Vocational High School in Buffalo, NY. Now, 34 years after conceiving the idea, and continuously working on it whenever I had a few spare moments, my invention is becoming a reality: I have finally filed my patent application for it.

In 1967, I was shoveling coal to work my way through college at the State University of NY at Buffalo, majoring in philosophy and minoring in computer science. My choice of epistemology and computer science was an unlikely combination that some thought was ill advised, but one I’m glad I pursued. Ayn Rand® had just published Introduction to Objectivist Epistemology. In contrast to more traditional views, hers explained concept formation as a quasi–mathematical process, and it occurred to me that mathematical processes are just what computers are about, so it should be possible to program a computer system to simulate sense perception and the formation of concepts in order to simulate human thought processes and natural language, thereby making computer systems easier to use. As it turns out, there is a little more to it than that! Many attempts (and years) later, I have discovered that my idea was much harder to implement than I had first thought: It requires much more powerful computers than were available then and much more knowledge than I had at that time in my life; one needs knowledge of computer science, the biology of life, the processes that make consciousness possible, the meta-physical status of both life and consciousness, the difference between mechanism and teleology, how to simulate free will, knowledge of the nature of concepts and of human language, and much more. Knowing just where to begin such a project as this is important too, because the choice of starting points is far from obvious.

This book is an explanation of what the simulation of consciousness involves and how to build such a system. The book is an integration of what I have learned in the intervening years since I first conceived of my idea, and it explains how to apply that knowledge to build a working computer simulation system. Most of the ideas the book contains are proven facts, but a few are my theoretical opinions based on those facts.

I cannot stress enough that the operative word in this book’s title is “simulation.” Computer systems are machines, not life–forms, and they cannot be conscious like people are. Also, all references to Objectivist ideas and all conclusions I have drawn from them are based on my personal understanding of that subject; my comments and conclusions are my own personal opinions and interpretations of Objectivism and its potential applications to computer technology.

9 How to Simulate Consciousness Using a Computer System Blue Oak Mountain Technologies®, Inc.

U.S. Patents Granted 7499893, 8086551

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1 The Context of Consciousness

1.1 Introduction

It is well known in the state of the art that computer hardware and software can be functionally substituted for each other; that hardware and software can run equivalent processes in different forms (the physical vs. the logical). The advantage of the software form of a process is that it is more flexible and easier to change.

The basic premise of this book is that there is no reason that a similar kind of functional substitution cannot be extended to systems that simulate the processes of life– forms, provided that the controlling part of the logical form of the system (the software) is designed to be teleological; that is, it must be designed to cause the system as a whole to be goal–directed as it interacts with reality.

The only currently available technology with the potential to simulate life functions and consciousness is a computer simulation system, but none of the systems extant in the state of the art are up to the task because they are all simply mechanistic automatons.



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1.1 Introduction

If a computer system is to simulate consciousness, it must be designed to be as causally equivalent to a biological life–form as possible, because consciousness is a process that is only found as an attribute of some living things, a survival mechanism that they possess, and it can exist in no other known form. Consciousness is not a mechanistic process, and it cannot simply be run as a computer program like a spreadsheet or a robotics application.

How close the causal equivalence of a simulation can be made to mimic real life processes is an open question that depends on the power of computer technology and many other factors, but clearly, some level of causal equivalence can be achieved with the technology that is available even today. Some minimal life–like behaviors have already been simulated with state of the art Artificial Life (AL) simulation systems and robots, though they are not very good ones because they are purely mechanistic in design.

The main purpose of this book is to explain the context of consciousness from a perspective that is not widely known to most scientists and to provide a description and a reduction to practice explanation of my invention to simulate it. Another purpose is to distinguish my invention from the current state of the art in the fields of Artificial Life (AL) and Artificial Intelligence (AI). The formulation of ideas this book contains has resulted from my project to design a new kind of computer system that simulates consciousness as it is observed to function in higher animals and human beings.

By “simulates consciousness,” I mean a system mimics its causal functions, including higher level conscious functions such as language, reason, and free will. The mechanistic aspects of the computer system will function



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The Context of Consciousness

merely to animate the new form of virtual, teleological entity that will emerge from the design of the system as a whole. The mechanisms of the computer will animate this virtual entity in a manner analogous to the way the mechanisms of physics and chemistry animate biological life–forms.

The interaction with reality of new kinds of data structures, teleological processes, and causal functions in the my simulator’s design will make this virtual entity possible. The virtual entity’s simulated consciousness will emerge from that interaction.

This book is intended as an instruction manual on how to design and build such a computer simulation system. It must be made clear that this project is not an attempt to recreate biological consciousness in a machine, which is impossible. Such a disclaimer is necessary because of the poor understanding most people have of the nature of what life and consciousness are, as opposed to what machines are.

Before I begin the description of my simulator design and explain how to reduce the invention to practice, there are some more fundamental ideas that need to be introduced and explained in order to set the context for the reader and describe the methods I will be using in explaining how the invention works.

First, the term “computer” is a misnomer. The tools that are commonly called computers are defined too narrowly in the current state of the art; this has happened as a result of their early designs, the uses they were put to initially, and the uses for computers that could be foreseen at the time they were named, given the limits of our technology in those early years. Computers are really reality



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1.1 Introduction

simulators ; they are man–made tools which can animate the objects and relationships that make up any aspect of reality as dynamic symbols. In doing so, they represent that reality in an animated, virtual form that mimics the real form.

Reality exists, it just is. Reality consists of entities, each of which is its own unique identity, and it is the identities of entities that exist in various relationships. Of the different types of relationships that occur between entities, causality is particularly important because it is the means by which entities interact dynamically; without causality there can be no action; causality is the identities of entities interacting with each other.

Simulations are man–made analogs of reality that symbolically represent the entities as data structures, and their relationships (including causality), with logic. If accurately designed, and when animated by computer hardware and software, simulations reproduce reality in virtual form; that is, in a world of symbols rather than objects, in the form of information; in other words, simulations reproduce reality in epistemological form (as symbols and logic) rather than how it exists in metaphysical form (as physical objects).

In the current state of the art, all computer based simulations work mechanistically; that is, the data structures and logic they use are designed to simulate mechanistic causality.

For example, the word processing, database, spreadsheet, and accounting programs that are commonly used with computers today are really simulations of paper documents, paper filing systems, paper spreadsheets, and paper accounting systems. Their value derives from the



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fact that in addition to simulating the paper, these programs and the computer hardware they run on, mechanistically simulate some of the calculation and other actions that were originated in human consciousness and that people perform consciously and manually when doing such tasks using real paper. The computers automate and greatly speed up these manual processes.

A more complex example of a mechanistic simulation is that of a major airplane manufacturer which designed a computer simulation of a new airplane; the simulation was used to test the design and instruct the machine tools to make the plane’s parts. The airplane was then assembled, and it was flown without any paper blue prints or physical mock-up having been used. In other words, the mechanistic causality of the plane in flight was simulated by the computer system to validate the design by mimicking reality, and then it was translated into real objects with the only human intervention being to design the airplane, to assemble the plane’s parts, and fly the finished product.

Another example is the first contest in which a computer chess program beat a human chess master. The program is a simulation of a human chess player in mechanistic form; the computer system was used to animate a symbolic chess board using mechanistic causality such that it was able to beat the human player.

In all of these examples, entities and their relationships to each other in reality were represented symbolically; that is, they were converted into information by a human consciousness using data structures and logic as a counterpart to the real objects and relationships that exist



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1.1 Introduction

in the world, in reality, and then they were re–animated using computer hardware. These simulations are the best that the current state of the art has to offer.

But, as you will see later in this book, there is more than one form of causality. This is contrary to most peoples’ understanding because it is a new idea, an idea that was discovered in the later part of the Twentieth Century.

Life–forms, and especially conscious life–forms, are a form of continuous action, action that depends on more complex causality than non–living mechanisms do. While this more complex form of causality itself still depends on mechanistic causality at its root, as does everything in reality, it endows life–forms with new capacities that non–living entities do not possess, such as generating their own energy, sustaining their lives, and regulating their own actions through goal–directed behavior.

There is no reason that this more complex form of causality cannot be symbolically represented and animated in a computer simulation system; the entities involved in living systems are real entities just like their non–living counterparts, and the complex causality involved in their function is still a form of causality none the less. All that is required is that the appropriate data structures and logic be invented to represent this new, complex causal form in order to simulate living systems. Both real life–forms and simulations of them have mechanistic causality as the ultimate motive power that underlies their more complex higher level processes. These complex higher level processes, however, can only be animated by a teleological simulation; that is, they must be goal–directed.



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The second point of context that needs to be made is as follows: If this were an instruction manual on how to design a computer based accounting system, it would have certain prerequisites, including knowledge of counting, arithmetic, some algebra, an integrated knowledge of accounting that included processes such as double–entry bookkeeping, business practices, and why using such a system and its operating principle is important to business success. In other words, such an instruction manual would need to include all the subjects that would be needed to identify the entities of accounting processes and their relationships, especially the causal relationships that would need to be included in the simulation to make it correspond to the actual reality of business accounting.

Likewise as prerequisites, this project requires an integrated knowledge of the nature of reality, causality, consciousness, biology, teleology, epistemology, volition (free will), concept formation, objectivity, logic, human beings, natural language, computer science, and so on. Unlike accounting, however, because these subject areas are so fundamental, they encompass a very large part of human knowledge. Few people have had the perspective to understand the inter–relationships involved and to work with these subjects as an integrated whole until recently, until the philosophy of Objectivism provided that perspective. No other philosophy in history has made possible such a broad integration of human knowledge.

Philosophy is the only field broad enough to do so. Unfortunately, errors perpetuated by other philosophical systems prevented such broad integrations in the past.



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1.1 Introduction

Philosophy cannot specify the content of subjects outside its own boundaries; that is up to science. But philosophy can specify the necessary structure of knowledge by explaining how it is acquired (epistemology), set the boundaries of the possible by explaining the fundamental nature of reality (metaphysics), and identify logical fallacies and illegitimate questions. The last function is especially important because without it, one could waste time attempting to answer questions like: “What will be the long term economic effects of dividing costs by zero?”

1.1.1 Setting a Philosophical Context

Philosophy cannot provide very much of the content of the prerequisites listed in the last section, but it can supply the epistemological rules and guidelines for evaluating the prerequisites’ content, their connection to each other, and to reality. In this regard, philosophy functions in a way analogous to the way structured programming does for creating computer programs which function properly when completed, or as quality management does for creating products that ultimately work as they were intended for the people who buy them.

I know that philosophy has a bad reputation in science because so much of it is mysticism or other forms of subjectivism. However, this is not true of all philosophy. Moreover, all people including scientists have a philosophy, whether they admit it or not: Science cannot function without perception of reality, forming and using concepts, logic, objectivity, and various other methodological processes, all of which come from one



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philosophy or another. The choice is not whether to have a philosophy to guide your work, the choice is: Which one?

The whole point of the scientific method is to help achieve objective results, instead of subjective fantasies. But the scientific method is not enough; more and more in today’s culture science is portrayed as a mere “alternative” to various forms of mysticism or other forms of subjectivism, with the resulting pseudo–sciences such as creationism and environmentalism. Science itself can only survive, and be defended, by an objective philosophy that is ultimately based on our observations about the reality we live in.

In developing the technology for my invention, I have followed the ideas and methods of the philosophy of Objectivism as closely as my own understanding of the subject has allowed. In addition to providing guidelines and connection to reality for this project’s content, the ideas offered are original and differ significantly from traditional approaches; some of the implications of Objectivism’s conclusions provided many new insights as to how to select and conceptualize my ideas.

Objectivism is a philosophy which was originated by Ayn Rand in the 1950’s and has been explained in detail by Dr. Leonard Peikoff, Dr. Harry Binswanger, and others in books and taped lectures, many of which are cited as references for this book. Its ideas, however, are not well known in the scientific community.

I chose Objectivism as my guide because it is the only body of ideas I have ever found that can explain the genesis of the abstract ideas of the science, on which an understanding of consciousness depends, and give them a



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1.1 Introduction

verifiable connection to reality. I do not accept the idea that science is or should be a body of arbitrary, floating abstractions or compartmentalized ideas separated from other knowledge and sense perceptions.

Reality is an integrated whole, a “plenum” as Aristotle once said, and our knowledge of it must be as well, or it is not knowledge.

The subject of this book, however, is neither about philosophy in general, nor is it about Objectivism in particular; rather, its subject is to explain how to build a consciousness simulator. I will introduce and explain only those ideas from Objectivism that are essential to understanding the technology being discussed on these pages. The prerequisite ideas I am referring to are fundamental to understanding the technology of my invention, and they are not available anywhere else so far as I have been able to discover.

I noted earlier that starting a science project with philosophy is not a conventional approach to scientific research, but that philosophy is the discipline that sets the standards for all of our knowledge and one integrates that knowledge as a system, just like biology does for the subject matter of life or computer science does for the subject matter of computer systems. While historically, philosophy has not performed this function very well, a philosophy based on the observation of reality and reason, in other words, one that operates like any other science does, makes such an approach reasonable.

In some ways human knowledge is similar to the data and structure of complex computer systems; it is similar in that our consciousness processes information that has a specific identity and structure and does so by a specific



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means; due to this fact, the identity and means of that processing can be abstracted from the content of consciousness and its interaction with reality, for scientific study and other purposes.

But this technique is only possible if we start with a reality–based approach and methodology in the first place.

If you survey all of human knowledge, you will find that Objectivism is the only philosophical system that starts with reality (as perceived by human senses), and not with God (universal, disembodied consciousness) or a materialistic universe in which consciousness is replaced solely by mechanism. Objectivism provides the structure to build a unified system of knowledge that either consists of direct, perceptual observations, or unbroken chains of inferences based on direct, perceptual observations; no parts of Objectivism are compartmentalized, arbitrary assertions. This epistemological approach is the foundation for the ideas presented in this book.

Objectivism qualifies as a set of guidelines for this project precisely because it is the only philosophy that is neither based on the ideas of Idealism nor Materialism, the two primary schools of philosophy in human history. Idealism starts with the idea that a disembodied consciousness is supernatural (or at least some unknowable metaphysical primary) and Man uses intuition as the means (epistemological method) to identify the “intrinsic essences” of objects in the world, “essences” that supposedly give objects their identity; that is, it starts with the idea that the identity of objects is somehow intrinsic to objects (metaphysically embedded in them and ultimately unknowable by us); that they are available



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1.1 Introduction

to neither human sense perception nor reason. Hence the fuzzy idea of “intuition” as its main means of the identification of the world around us.

Materialism, on the other hand, denies the existence of consciousness outright; materialism is the view that all life is mechanistic (completely reducible to the laws of chemistry and physics), and that consciousness is an illusion, a transparent epiphenomenon, and a process that has no identity itself. Under this view consciousness adds nothing to, and interacts in no way, with our identification of objects in the world around us.

Both of these ideas are false.1

For the reader to understand this issue is of utmost importance because many of the ideas of biology, consciousness, and computer science I will be discussing consist of concepts that are not well known in the culture at large; they are new ideas, and they cannot be understood from the Idealist or Materialist points of view.

Yet most people, unknowingly and implicitly, have accepted one of the other of these views; they have done so because the viewpoints are pervasive in every culture in the world in one form or another. And it is precisely because most people hold either the ideas of Idealism or Materialism unknowingly and implicitly, that I have taken such pains to explain these ideas and their relationship to understanding the content of this book, to explain the key differences in content and method. Unless the reader purposefully thinks about this issue, their subconscious will automatically interpret what I have written on these



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pages from their implicit idealistic or materialistic points of view. The only way to have a choice about this issue, is recognize it as important and think about it explicitly.

For Objectivism, how knowledge is acquired is as important as its content, the what or subject of knowledge. Questioning and thinking about how we know what we know, about our implicit premises, is therefore, paramount to our understanding of any subject, and it is the only effective way to validate what we think.

Unlike either Idealism or Materialism, Objectivism is based on and starts with the observation of reality using sense perception. Abstract ideas are concepts formed by a specific, reality based method and symbolized by words; concepts contain data that are derived only from their ultimate connection to sense perceptions or through an unbroken chain of definitions that are ultimately connected to specific percepts. Using Objectivist methods, concepts are formed using a quasi–mathematical process by which perceptual measurements are compared by human conscious processing, processing that is initiated by choice. Concepts thus formed are based on ranges of perceptual measurements, not intuition or pragmatic guesses.

In other words, in Objectivist epistemology, knowledge is acquired using a specific, limited method of consciousness consisting of both automatic sense perceptions and a process of concept formation that is initiated by choice to produce a hierarchy of abstractions, but one that is connected to percepts at its base. Moreover, this conceptual method is observable by each of us by means of introspection as we perform it, if we choose to focus and look.



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Introduction

Objectivist epistemology explains the integration of the nature and requirements of reality and of the nature and requirements of human consciousness, dynamically interacting with each other. This dynamic interaction between the identities of both reality and consciousness is the specific way consciousness operates as it processes the data of reality, data which are its content.

The idea of using a method to form concepts is a new idea in epistemology identified by Ayn Rand in the 1960’s, and I am explaining it here to be sure it is clearly understood because my invention is built upon it.

You will find no such idea as this in the entire history of philosophy. Prior to Ayn Rand, philosophers had only identified two other alternatives for forming concepts: intrinsicism and subjectivism. As Dr. Peikoff has explained his lecture: “For intrinsicism knowledge is revealed by God or Nature, so no method is necessary; for subjectivism knowledge is simply made up, so no method is possible.”2 Neither of these approaches is appropriate for a science project.

Because Objectivist metaphysics and epistemology are such recent innovations, many readers will be unfamiliar with them. Yet unless readers understand the implications of concept formation using a method, rather than using intuition or subjectivism, they will not be able to completely grasp the new concepts that are explained in this book, such as complex causality and consciousness as a non-mystical process.

Note - I will provide reminders of this fact where appropriate.



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Each reader must retrace the steps connecting these new concepts to reality as a prerequisite to understanding them. That requires following the methods of Objectivist epistemology of reduction and integration. If you are not familiar with these methods, I suggest you obtain and study the references before you begin reading the next chapter.

In summary, the ideas I am referring to that I consider to be prerequisite a proper understanding of this book are the following:

• That reality is the world we perceive, a world of objects with inter–related identities, identities which interact causally, and that causation is of more than one kind.

• That our sense perception is valid, automatic (in a biological sense) and the starting point of all knowledge, and that all abstract knowledge is a series of conceptual relationships built on sense perceptions.

• That consciousness is neither mystical nor mechanical, but a means of survival, a causal process that consists of relating objects in reality to some life–forms’ state of awareness (of mind), a process performed by some life– forms using a complex form of causality of which life itself is an example.

• That some truths about reality are axiomatic, self– evident from perceptual observation, absolute, and inescapable.

• That concepts are volitionally abstracted from percepts by a specific, measurement–based process that is in part mathematical.



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Where to Start a Project to Simulate Consciousness?

• That our conceptual knowledge, which is volitionally inferred using reason from our observations, is true (if inferred without error), is certain, is objective, and is absolute within a specific context.

These ideas are in direct contradiction to the prevailing assumptions of our culture (implicit Idealism and Materialism), and I offer you fair warning of this fact here in the Introduction. Unless you are interested in questioning those assumptions (some of which you probably hold without even realizing it), there is little point to reading any further.

On the other hand, if you find the idea of questioning your assumptions and previously learned ideas inviting and interesting, if you value objective opinions, then by all means read on!

1.2 Where to Start a Project to Simulate Consciousness?

The question of where to start such a project as this is a most important one. Consciousness, like all other phenomenon, does not exist in a vacuum; in order to understand it, one must start with what gives rise to consciousness in the first place, and that context must never be dropped.

As with all other scientific inquiries, the proper starting point is reality as perceived by our senses, and the life– forms that struggle to survive in it using consciousness as a means of survival.

The commonly accepted approach of choosing the game of chess or a problem in pattern recognition or natural language understanding as the starting point to learn to



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build a simulation of consciousness, precludes success right from the beginning: Whatever such systems are, they are not simulations of consciousness; at best they are mechanistic simulations of one or two isolated attributes of consciousness.

Using a method such as this does limit the scope of the problems that must be solved, but the context that provides all the clues as to how consciousness works is then lost as well. Reality is an interconnected whole, a plenum, and our consciousness is limited and cannot take all of reality in as one large piece of information; our knowledge of reality must therefore be contextual, that is, our knowledge must consist of many small pieces of information that are integrated or linked together as a whole; the links are what connect all the contexts that give each piece of knowledge its meaning. If simplifying a problem by breaking it up cuts the problem off from its context and the hierarchical connections to other knowledge, the result is guaranteed to be arbitrary, narrow in scope, and brittle (to paraphrase Randall D. Beer); if one’s goal is to simulate what intelligent life–forms actually do, simulating a disembodied attribute will not accomplish that goal.

In his book, Intelligence as Adaptive Behavior, Beer takes issue with this approach to designing computer systems to simulate living entities as he clearly outlines the basic assumptions of the traditional Artificial Intelligence (AI) position. He cites many of the top Cognitive Psychology and AI researchers and shows how most consider intelligence to be deliberative reasoning, and deliberative reasoning to be a form of computation that can be represented inside an electronic computer. Beer goes on to point out that one researcher (Dennett) clearly



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identified the three underlying assumptions of traditional AI research: “1) Thinking is information processing...2) Information processing is computation (which is symbol manipulation)...3) The semantics of these symbols connects thinking to the external world.”a

Beer sums up his description of the traditional AI view, known as the representationalist position, is as follows: “Under the rubric of this hypothesis, perception is the construction of internal representations of the external environment. Learning is the modification of the existing representations and the accumulation of new ones. Memory is the storage and retrieval of representations. Language is the encoding, exchange, and decoding of representations. Reasoning is the logical manipulation of representations. Taking action is the execution of a representation of the plan of action to be performed.”a

The representationalist position has two major weaknesses according to Beer. First, it depends on the answers to questions traditional philosophers and psychologists have worked on for years without much success. The descriptions of the world that have resulted from such work have “...no absolute existence, and certainly do not reflect an objective reality.”a

But it is just such an “objective reality” that programmers need knowledge of to write their programs. Beer should be given credit for calling into question the shoddy work of most philosophers, especially those since Kant, and the lack of thoroughness on the part of cognitive psychologists for blindly accepting so many philosophical fallacies.



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Beer goes on to point out that the second weakness in the current approach to AI results from the first; since there is no “objective reality” for programmers to use, they try to find one by breaking the problem domain into tiny pieces for which objective data do exist, hoping to build a grand solution from all the smaller ones.

Beer points out correctly that, to date, this approach has produced only a patchwork of very brittle solutions to AI problems, such as expert chess playing programs which cannot be applied outside extremely narrow problem domains. The essential goal of his approach is to reintegrate AI into a whole from the patchwork of tiny subproblems that have resulted from the traditional representationalist approach. Beer believes this will work because the model of biology will provide the “objective reality” that cognitive psychology and philosophy have not.

Even the success of chess playing computer programs does not contradict this fact; chess is a game invented by human beings, and computer programs that play chess are merely mechanistically reacting to the consciousness of human opponents according to the computer’s programming. Chess playing computer programs do not perceive, make choices, calculate, use language, or think as humans do; they mechanistically execute a very narrow range of instructions preprogrammed by humans that calculate chess moves. Other Artificial Intelligence (AI) systems with broader goals have not been as successful.

Note - In a strict sense, these programs do not even calculate. The bits they flip can only be thought of as “calculations” if the human



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consciousness (that created the mechanisms by which they operate in the first place) is included in the context as part of the meaning of the word “calculate”.

Recent successes in the field of Artificial Life (AL) support the viewpoint that context and connection to reality must be maintained. Projects in artificial life have succeeded, in part, because they start with the problems faced by real animals attempting to survive in reality, and because they use the context of the animals’ existence and their specific survival problems to provide the criteria for choosing the goals and implementation of the programs intended to simulate the animals’ actions.

While these successes seem to validate Beer’s conclusions, I don’t think Beer went far enough. As I indicated above, more than mimicking biology is needed in order to have the “objective reality” and the full context that programmers need. In fact, the only reality that will do is the same reality the we perceive every day, including all of its aspects.

The Digital Life–Form™ (abbreviated: DLF™) and also referred to as DLF Simulation Technology™ that is described in this book falls into the AL category in as well, but its scope is much broader than the physical/biological processes simulated by most AL programs, or the natural selection process that is simulated by genetic algorithms. This project is an attempt to simulate an entire organism, including its goal–directed behavior, its consciousness, and its



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connections to reality, by simulating a more complex form of causality on which biological life depends and by which it operates.

Consciousness is an interactive, reality based process that is a means of awareness for some types of life–forms; it is a process that presupposes simpler mechanistic physical and chemical processes, but its goal is awareness of (and action in) reality; the data of reality are its content. A conscious life–form gets and maintains such awareness only by a continuous interaction with reality, by continuously building relationships to the other objects in reality, and by processing and storing the information that results.

In order to describe how to simulate consciousness of reality and animate that process with a computer simulation system, it is first necessary to make perfectly clear to the reader what is meant by “reality” and “consciousness” because these concepts have such fuzzy definitions in our culture.

1.3 What is Reality and Consciousness?

Reality is the world of objects and energy that exists around us; it is the world we and other life–forms see, touch, and act in every day. What you see is what you get. There is no “real” reality at some “higher plain” or in some “supernatural realm.” Reality just is.

The objects of existence are, they are identities of specific kinds, and they are dynamic; their identities interact constantly. The interaction is causality. We human beings are also objects with identities, and we are part of the same causal processes as all other objects; our



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consciousness of reality is one aspect of those processes. Our conscious perception of reality is both the starting point and ultimate source of all human knowledge. Consciousness is the process of awareness; our percepts of the objects we sense in reality are the content of that process, the data.

Consciousness is the series of actions we perform to process the data from our senses in order to have that awareness, and we do so in order to survive; consciousness is our means of survival. We need a means of survival because we are biological organisms, not machines.

Consciousness evolved as a survival mechanism of living organisms. To understand how it works, one must therefore always keep this biological context in mind; there is no mystical component to consciousness. In fact, the existence and validity of consciousness and our senses is axiomatic and self evident to anyone who cares to make the necessary observations.

You need only open your eyes and look, to observe reality for yourself.

Unfortunately, reality is not where most people start their investigation; most people start with ideas, not observations, but other peoples’ ideas. They start with whole bodies of ideas that they have ingested into their minds without questioning or analyzing. The result is confusion: Which are true, the ideas or sense perceptions? Which came first? Yet to know reality, there is only one way to begin learning about it, and that way is to observe it directly with your own senses.



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Existence, Identity, and Consciousness are basic, self– evident axioms which are inescapable because they are presupposed by any attempt to deny them: You may deny them, but you must use them in order to do so.

Such denials are therefore self–contradictions and self– refuting. 3

1.3.1 The Data of Reality

When you or I perceive reality (that is, are conscious of it), we smell, we taste, we hear, we see, and we touch. What we perceive are objects, energy, and those two things interacting. (We, are also objects ourselves that interact using energy with other objects around us, so sometimes this process is recursive.)

The process of consciousness, like any other process, operates by a specific, limited means; at the perceptual level its sensors transduce various forms of energy to produce sense data; that is, the process of consciousness at this level is sensors that transform energy into information.4

Information in this context is one form of energy converted to another; it is the electrical energy of groups of neurons firing at a certain rate converted from light energy, for example, in the case of sight. The boundary of the body of an organism and the energy conversion that must take place to cross it, is also a context boundary: The energy is no longer just energy, but energy that has been processed by a living organism. The energy now has a relationship with the life–form. In addition to being



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energy, it is now also information within the context of that life–form’s systems, information that has survival value to the organism.

A transformation has occurred; the identity of the energy and the identity of the life–form have interacted causally, and a new relationship has been created based on their mutual identities. The sensations that result from that relationship are emergent properties in the organism; they are emergent because they depend on energy from outside the organism and come into existence (or emerge) only when the energy is present at and processed by the senses.

The energy is changed only in form by the conversion process that sensors perform; it is still just energy. The organism, on the other hand, is changed metaphysically, that is, in its fundamental relationship to reality. The reason is that the information the energy carries changes the identity of the organism, changes its attributes or properties. That means, in effect, the energy changes the organism’s capacity to act in its environment. This is because the energy can be further processed once inside the organism; the information the energy carries can be extracted, stored, and used by the organism to take action in reality in the future.

In the case of visual information, these processed data do not symbolize or “represent” the light energy; they are the light energy, just in a different form, a form in which it can be processed by the brain of a life–form to extract the identity information it carries.

As part of the conscious awareness process in higher life– forms, the sense data (sensations) are integrated into the form of percepts, that is, into informational objects, informational objects with the same identities as we see



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around us. The informational objects in the form of percepts are a second level relationship that emerges from sensations with additional processing by the nervous system and constitute the perceptual level of consciousness. The informational objects do not represent reality, they are reality in the form of percepts, which are a form of information, of conscious content.

Humans also have a third level of awareness: conceptual awareness. Humans alone can use percepts (and the implicit measurements they contain) as data to volitionally infer still more relationships between the properties of objects to form concepts. But these data structures (concepts) are less direct than percepts; concepts are the result of many observations and conscious comparisons of ranges of perceptual measurements. They result from a concept formation method performed by choice, and in many cases, long chains of conceptual reasoning employing language. 5

Unlike percepts (which are formed automatically and infallibly), concepts can be formed in error.

Concepts are part of our knowledge about reality that has been produced by another aspect of our conscious processing of percepts to identify relationships. Classes or categories of objects, various types of relationships, and other abstract ideas are part of reality insofar as our minds are part of reality, but they are not reality itself; they would not exist but for human beings. Metaphysically, only objects exist. Classes or categories are part of our knowledge of reality (as content), part of the process of consciousness (as mental data structures), and part of the specific kind of consciousness possessed only by human beings (reason). By analogy, the computer data structures used by an accounting program do not exist in a manual



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accounting system, only pieces of paper do. However, computer science identifies and uses various categories, relationships, and abstract ideas to enable a computer system to process accounting information. In both cases, these entities are the result of the relationship between the processor and the processed data.

Abstract ideas stored in memory by concepts and their definitions are another form of information, but a form that does symbolize or “represent” certain aspects of reality, unlike percepts which are reality. Abstract ideas are stored as concepts and accessed using words in a code of visual–auditory symbols called natural language. Abstract ideas amplify the survival value of percepts by reusing some of them as symbols which broaden the scope of our knowledge and reduce the number of units of information that we must process. The reduction of information units to be processed is one way concepts offer survival value.

To summarize, for the higher life–forms such as humans, reality is the body of things we observe using our perceptual consciousness; it is the world automatically identified by our senses using certain relationships of perceptual form. This automatic knowledge may later be amplified by our choice to form abstract ideas, that is, to form concepts which are symbolized by words and are themselves yet another form of information created by consciousness volitionally processing percepts.

In the world of computer simulation, a similar set of relationships could be created by an appropriately designed simulated consciousness running as part of a digital life–form on a computer system, provided that it



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was given a similar means to convert sense data into information in the form of identifiable objects (percepts) and to form concepts.

Exactly how the process of perception and the volitional process of conception could be designed to work in a computer simulation system will be discussed in detail later; for now, let’s just focus on reality itself (the data of consciousness), to differentiate it from consciousness as a process; to do so, we must allow for the fact that some terms of consciousness, such as “measurement,” “difference,” “information,” and so on must be used as part of that description.

Note - These terms must be used because reality and consciousness are so closely related; consciousness is about reality; it is a series of relationships based on reality. But when we use consciousness to describe and explain itself recursively, the concepts used must have double meanings: One meaning refers to the data of reality itself, and the other refers to the data of reality as processed by consciousness. This is a subtle distinction, and one must be careful to avoid confusion.

Objects

Objects are parts of reality that can be separated or differentiated by a consciousness from other parts of reality due to some difference that can be detected or measured by our senses.



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Note - The differentiation of objects by consciousness is not metaphysical, but epistemological; that is, it is a separation made by a specific means of measurement (foreground from background) and as part of the processing of reality by consciousness, by our perceptual system; the separation of objects is “in the form of information,” not a literal separation of “parts of reality.” Reality is one interconnected whole metaphysically, and cannot be separated into parts, except in the form of information. The sum total of all objects, energy, relationships, and information equals reality, all of existence, which is a plenum. This fact was first identified by Parmenides, then Aristotle, and fully validated by Ayn Rand.

This differentiation by our perceptual system distinguishes the object or objects of our attention (foreground) from a background, which consists of all other objects or energy (i.e. - the rest of reality) and correspond to actual boundaries or other aspects in reality. Objects are their identity; that is, each object possesses a set of attributes or properties that are an integral part of itself to make it what it is, and each of these properties has a measurement or value that results from the way each object interacts with the energy conversion process performed by our perceptual system. Percepts, therefore, contain implicit measurements of the properties of objects.



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Note - There can be no entities, the properties of which, have zero or infinite values; to exist is to have some properties, each with some non– zero value, to have an identity. To have a zero value would imply there was no energy to interact with our sensors, which in turn would imply no object exists. But “non-existence” is not a fact; it is the absence of fact.6

There are two broad categories of objects in existence (excluding man–made objects): Non–living objects and living objects. Non–living objects cannot act unless acted upon by an outside force. In addition, the existence of inanimate objects is unconditional; the form they take is determined strictly by the laws of physics and chemistry, but they exist in one form or another without any action on their part. Living objects, on the other hand, exist conditionally; they must generate their own energy by their own action or they will cease to exist; without continuous action to maintain their survival, life–forms disintegrate and revert back to their inanimate, molecular component parts.7

Actions

Objects interact with each other. As this occurs, their identities change; that is, the measurement values of their properties or the properties themselves are modified over some period of time. The result is that the same objects that have one set of properties and values at some time T1, will have a different set at time T2.



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Only objects can act, and actions are always changes to the identity of some object or objects in reality, or the energy that connects them. Actions independent of objects do not exist, except in the form of information as the measured property of some real object in a conscious process.8

Energy

Energy is the medium that conveys action between objects, that transfers identity and puts the “cause” in causality; it provides the motive power for actions and interconnects all objects.

Motive power is energy in action; it is forces such as gravity moving the water in a river or the fuel burning in an engine to move a car or the nuclear fusion in the sun.

Life is also a form of action, but it is action generated and controlled internal to the life–form itself; the energy of life is self–generated, self–sustaining, and self–regulated. Self–generated energy is accumulated by life–forms by processing external sources of energy such as plants using sunlight to make sugar or by animals eating the bodies of other life–forms. Self–generated energy is part of the very being and fabric of life–forms; it is not stored separately as it is in machines. The use of self–generated energy to act to survive is self–regulated chemically in every cell and by the nervous systems of higher life–forms. All of these functions occur internal to a biological organism, and these properties are the identity of such an organism.

Existence, then, is a plenum that consists of non–living and living objects interacting with energy as their intermediary and is completely interconnected; reality has



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no “holes” of “non–existence” in it. “Non-existence” is not just another kind of thing; there is no such “thing” as “non-existence” between objects, or anywhere else; there is only other objects or energy, everywhere.

Note - “Non-existence” or “nothing” is a relational concept formed by observing space between two or more objects; “non–existence” is a conceptual relationship (an abstract idea), not an object or thing itself. “Non-existence” does not exist physically, but only as an informational placeholder, like the concepts “zero” and “infinity.” 6

Energy interconnects all objects and carries identity from one object to another; it forms the links in a cause and effect chain of events.

Energy always carries the identity of the last object with which it has had contact. So, for example, if light energy from the sun, which carries the identity information (including all the colors of the sun in its spectrum) reflects from a tree, the identity of the light is changed by that interaction (as is the identity of the tree). The light reflected from the tree is predominately of the green wavelength from interaction with the identity of the leaves, and now carries information about the identity of the tree, rather than of the sun. The reflection process is an interactive relationship between the energy and the tree that changes the identity of the energy. Astronomers use this fact every time they take a spectrograph of distant object to determine is composition.



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Relationships

Relationships have two tightly integrated aspects; one is an aspect of reality and the other is part of the content of consciousness, or an aspect of information.

Relationships are implicit in the specific interactions of the identities of two or more objects, such as a tree reflecting sunlight or two billiard balls colliding.

Relationships, as the data of consciousness (as information), are the measurements of the interacting objects as processed by our senses (and their inferred derivatives such as abstract concepts), in other words, as processed by a consciousness. In our minds, relationships are implicit if we are not conscious of them, and explicit if we are.

Relationships identify the ongoing interconnection of objects’ identities.

There are many different types of relationships such as spacial relationships or temporal relationships, cause and effect relationships, and so on. In fact, consciousness itself is a relational process between a life–form and reality.

The relationship of cause and effect in particular is important to the process of simulating consciousness. A cause and effect relationship is the mutual change in the identities of two or more interacting objects (mediated by energy) that is necessitated by what those identities are. Cause and effect is not action–reaction as commonly thought, but rather it is identity interaction.8



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Cause and effect relationships may be simple, such as two billiard balls colliding, or complex, such as the self– generated and self–sustaining processes that maintain the existence of life–forms.

Note - The fact that cause and effect is not action– reaction and that there is more than one kind of cause and effect may be a new idea to you; it will be explained in more detail in the next chapter.

One kind of complex cause and effect is teleological (goal–directed) action; that is, the cause is the means to not only the immediate effect, but the continuation of the cause and effect relationship itself as an on–going process in that particular chain of events. It is in this way that life–forms cause their own future survival; they maintain the cause and effect chain that is known as their life.

1.4 The Integration of the Parts of Reality

Objects, their actions, and energy make up the plenum of reality. Each object is its identity, including a place and a time, and objects are all interconnected by energy.

These are the data of consciousness, real or simulated. The objects of reality reflect energy, sensors in a life– form convert the energy into a different form (sensations and percepts), at which point it emerges as information inside an organism, but it is still real, it is still reality, just in a different form, a form of information.



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1.4 The Integration of the Parts of Reality

The fact that inside an organism energy is information too, that is has an additional attribute added to it by the processing of consciousness, does not invalidate what it contains. Being inside a life–form in no way changes its original attributes or properties; nor does the energy’s transduction and processing make it any less real. The identity data the energy carries is conserved just as it is for a picture transmitted over a TV network, only its form changes during transmission. Its content remains the same.

The difference for biology is that the information the energy carries has meaning, survival value to the organism that is conscious of it. As content in the mind of an organism, the energy simply has a different relationship to reality than it had outside the organism, only its form has changed.

If energy processed by consciousness were to be changed in arbitrary ways, the resulting content would be rendered useless for survival. Biologically, such a process could not persist in the long–term.

The sensed information is processed by consciousness, and some of it is stored in memory. The information is evaluated by the organism and actions are selected either automatically or by choice depending on the type of life– form. The actions are implemented by the life–form and objects in reality (including the life–form itself) are affected. The cycle then repeats if the life–form survives. This is the process of consciousness operating as a causal process, as a survival mechanism.



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1.5 Summary

In this chapter, I have set the philosophical context for understanding the nature of life and consciousness based on my understanding of the consequences of some of the important premises of biology and the philosophy of Objectivism. Grasping this perspective is a necessary prerequisite to designing a consciousness simulator because consciousness is such a fundamental aspect of reality, and only philosophy is a broad enough subject to encompass and set the guidelines required to understand it properly.

I have chosen Objectivism as the basis for explaining the technology for the simulation of consciousness because in my opinion it is the only philosophy that is connected to the facts of reality and, therefore, able to explain what consciousness is metaphysically and how it works epistemologically. Understanding how consciousness works is a prerequisite to simulating it on a computer system.

In the next chapter, I will set the biological context for DLF Simulation Technology, which is the other key prerequisite. I will discuss life–forms and how they function in more detail, and describe the aspects of their identities that are crucial to understanding how to simulate consciousness. This is also prerequisite information because consciousness exists in nature only as a property of life–forms.



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1.5 Summary



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2

The “Biology” of Digital Life–Forms

2.1 Introduction

In the previous chapter, I described the philosophical framework for understanding the nature of consciousness. I explained that consciousness can only be understood in its complete context; it can only be understood in terms of how it fits into our knowledge of reality in general and biology in particular: That is, consciousness can only be understood as a basic axiom in metaphysics and a survival mechanism in biology, an attribute possessed by some kinds of life–forms.

With that context as a foundation, we need to look closer at exactly what this means to the theory of how biology explains consciousness in higher animals and human beings.



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Introduction

Philosophy, Biology, and Consciousness

The new method for acquiring objective knowledge identified by Ayn Rand, concept formation based on the comparison of perceptual measurements, that I introduced as part of a brief explanation of Objectivist epistemology will ultimately have an effect on all of the sciences; but for the topic of this book, its consequences for biology and computer science are the most relevant. Rand’s method has consequences for our understanding of the causality of life, and it also makes it possible to connect natural language to percepts in order to objectively demonstrate the connections between words and the objects we observe in reality.

For this chapter, however, two consequences pertaining to the causality of life are especially important: One is the complex nature of causality as it applies to biology, and the other is the role of complex causality as it applies to the function of consciousness as a survival mechanism.

It is necessary to explain these two consequences before I can go on to explain how to simulate consciousness in a computer system: I must first provide you with a more detailed context for life–forms and how they operate in reality. Having that context is prerequisite to being able to write the computer code for the causal relationships required to create a virtual environment in which consciousness can be simulated.

But the how of simulating consciousness cannot be understood, without knowing what consciousness is in more detail, and how it operates in biology.



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There are two erroneous ideas that are implicit in our culture (and probably in your subconscious), and they must be challenged if you are to ever grasp the context required to understand the ideas I am presenting. The two ideas are:

• That the only form of causality is mechanistic causality.

• The idea that consciousness can exist independently of a living, physical body.

Most people never consider that there may be more than one form of causal relationship; they intuitively accept the mechanistic concept of causality as billiard balls colliding as the only way causality can exist in nature.

But this view does not explain the biology of life very well. Life is not so simple, as most biologists know.

While life–forms ultimately depend on a form of mechanistic causality, that particular causal relationship is not sufficient to explain the existence or function of life– forms because it cannot explain goal–directed behavior. Just as you could not explain the operation of the word processing program I am using to write this book only by means of the electrical principles that enable my computer to operate, you cannot explain the operation of the behavior of life–forms by means of mechanistic causality alone. Other information is required.

The wide acceptance of the second idea, that consciousness can exist apart from a life–form, is due to the mysticism that pervades every culture in the world; most people accept the idea that consciousness can exist on its own apart from a biological body because that is what they were taught, and most people have never



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2.1 Introduction

questioned what they were taught, but just accept great bodies of ideas whole, never chewing and digesting them. As I pointed out in the previous chapter, most people start their thinking with other peoples’ ideas, not their own observations of and inferences from reality.

Such people are the implicit idealists I referred to in the previous chapter, who use intuition and the intrinsicist approach to form their concepts, most of them unknowingly. However, the idea of consciousness independent of a living body, is false. It is fallacy identified in Objectivist literature as the Primacy of Consciousness, a fallacy that is the result of using a defective means of concept formation.1

Consciousness does not exist apart from life–forms; consciousness is a process performed by the living bodies of life–forms, a series of actions, some automatic and some volitional, by which life–forms interact with the objects around them. Consciousness is a property or attribute of life–forms, a survival mechanism, not an object that can exist on its own. Actions cannot exist apart from the objects performing them, and consciousness is an action of a life–form;2 it is a process that a life–form performs by interacting with reality as part of its life processes.

So, in order to understand how consciousness works, you must first understand how life works from the point of view of causality; you must be able to distinguish the kind of causal relationships by which life operates from those by which inanimate objects operate. And, you must understand that consciousness is not itself a primary



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existent, but an action that is an attribute of one. You must understand that consciousness is caused by life– forms and life–forms alone.

2.2 Biological Life–forms

Nearly every culture in the world is built around some form of Idealism and Mysticism, usually a religion, though there are many other forms of it.

Western science rejects Mysticism and Idealism in favor of Subjectivism and Materialism, which attempt to explain all phenomenon, including life–forms and all their actions, in simple mechanistic models. Materialism includes the ideas that consciousness does not exist and that concepts are simply pragmatic, arbitrary symbols for classifying data, symbols that may or may not correspond to reality. Scientists rarely question the mechanistic formulation of causality when it is applied to life therefore, because given their subjective approach to forming concepts, to do so is neither practical nor necessary. The mechanistic, pragmatic approach seems to work just fine in experiments and scientific papers for most scientists when thinking professionally.

Most people (including many scientists in their personal lives) accept without question the premise that conscious life–forms are either supernatural or mechanical. Their acceptance of one or the other of these views is a consequence of their implicit philosophical premises of Idealism or Materialism.

The latter view means that life–forms are natural machines that work on the simple principle of “billiard ball” causality just like their man–made “cousins.” The



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Biological Life–forms

apparently opposing views of Idealism and Materialism are a false alternative, and they are the direct result of the non–objective ways the people who hold them form their concepts. They are a false alternative that results from forming concepts by using either idealistic, intrinsicist intuition or materialistic, pragmatic subjectivism, as opposed to the direct observation of reality, measurement comparison, and objective logical conclusions inferred from observation.

Self–PoweredObjects

Non–living objects do not move unless acted upon by an outside force. Some objects, however, such as stars or solar systems or the evaporation/rainfall cycle or fire seem to have a “life of their own” because of the way certain chains of cause and effect enable them to operate continuously over extended periods of time.

Other objects such as certain chemical processes that involve DNA or RNA molecules are not only able to operate for extended periods, but are even capable of self–repair. That does not mean that they are alive, however; it simply means that these molecules are more complex causally than other molecules.

Both of these facts have been validated by direct observation and scientific experiment.

Life falls into a separate category altogether. According to Ayn Rand, life is “self–generated, self–sustaining action.” In other words, life is more than mechanistic, “billiard ball” causality; it is teleological because it involves goals, actions taken to achieve them, and a means of self– regulation to govern the actions. To understand what that



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means in a non–mechanistic sense, you must first understand that there is more than one kind of cause and effect.

What is Unique about Life–forms?

What makes life processes unique is the form of causality by which they operate. Dr. Harry Binswanger explains how this form of causality works in his book entitled The Biological Basis of Teleological Concepts.

One of the facts explained in this book is that living matter is fundamentally different from non-living matter, a fact supported by the science of biology. Dr. Binswanger discusses Ayn Rand’s definition of life as “self-generated self-sustaining action” and emphasizes the conditional nature of living matter. He also proves why this definition is true by showing how the concept of conditionality as applied to life–forms is connected to biology and observable facts.

For example, at one point Dr. Binswanger quotes biologist Walter Bock to emphasize the distinguishing features of life–forms:

“1. Living organisms take in materials and energy from their environment;

2. They use the appropriate materials and energy for self– maintenance, self–repair, and self–reproduction;

3. Once they have died, they cannot be reconstituted– failure is irreversible.”



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These three properties make life–forms significantly different from inanimate objects and are what Ayn Rand means by life as self–generated, self–sustaining action.3

This is not a intuitive or subjective assertion, but a fact based on observation and inference; it is connected to reality; it is objective . To prove this fact to yourself, check each assertion in the following paragraphs with your own observations of reality and conceptual inferences as you read them.

Survival Requires Continuous Action

If living matter stops acting to gain what it needs to survive, it does not merely become inert like non-living matter, it literally decays, disintegrates and ceases to exist.

The constant need to act to sustain life in the face of alternatives is the basis for the concept of “value” according to Dr. Binswanger. Living things are goal– directed because they need values, and it is through goal– directed actions that values are attained.

Machines do not face the alternative of life or death and hence the concepts of value, goal–directedness, and self- generated action do not apply to them, except in the context of the human values machines help attain. This extends to machines in the current state of the art that are copies of living things which are embodiments of the human value of wanting a machine to act like an animal or a person. If machines that simulate life–forms are to be truly life-like therefore, they must be able to select their own “values” and act to attain them to whatever degree that is technically possible.



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To illustrate this point, Dr. Binswanger quotes biologist Albert Lehninger as follows: “A living cell is inherently an unstable and improbable organization; it maintains the beautifully complex and specific orderliness of its fragile structure only by the constant use of energy. When the supply of energy is cut off, the complex structure of the cell tends to degrade to a random and disorganized state.”4

To further illustrate the difference between living and non-living matter, specifically with respect to self- generated action, Dr. Binswanger quotes an article called Living and Lifeless Machines by R.O. Kapp.

Of the source of energy for living matter, Kapp says: “Let us now turn our attention to the fuel. In a motor car this is petrol and it is stored in a tank. In the human body it is chiefly glycogen and is stored in the muscles, having been converted from glucose in the liver. The chemical processes during muscle activity include the combination of muscle protein with sodium. This protein is therefore another part of the fuel. So both the glycogen and the protein serve the double function of being fuel and being constituents of those muscle fibres that are at one moment moving parts and at another components of the frame. The living body is analogous to a motor car in which the chassis, brakes, cylinders, pistons, connecting rods, valves and bearings all contained combustible material, some of which was burnt whenever the driver put his foot on the accelerator.”5

The living cell is the basic unit of life; it is the dividing line between the living and non–living. The living cell depends on mechanistic causality in the form of physics



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and chemistry for its existence, but the cell itself is a more complex organization that requires more than physics and chemistry to explain how it functions.

All higher life–forms are composed of billions of specialized cells, each having evolved to perform some function to keep the organism, and hence the cells, alive and the chain of life unbroken.

In life–forms with automatic means of survival, it is the cells of a living organism that control its actions through the pleasure/pain mechanism, not the other way around. Such life–forms are not capable of self–destructive behavior. The goal of survival is built into their DNA.

But to say life is complex is not to explain how it works. In order to explain life, several new ideas are required.

2.2.3 The Concept of Emergent Properties

An emergent property is a property of an object that cannot necessarily be predicted from looking at its parts. In his book, Dr. Binswanger used the example of two hemispheres that will not roll separately, but will roll when glued together: “Rolling is thus an emergent form of action completely determined by the individual separate properties of the parts and their arrangement.”6

The concept of emergent properties is essential in explaining the complex processes that occur in life– forms, both real and artificial.



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Life Requires Goal–directed Action

In his analysis, Dr. Binswanger shows that it is the conditional nature of life–forms that drives their behavior. Life–forms must act in order to survive; if they do not, they die, decay, and cease to exist. This means, in effect, that life–forms are inherently goal–directed . Their goal is continued survival, which their actions are intended to achieve.

In the context of their life as a whole, all actions that life– forms take can have one of two consequences: They can cause life–forms to survive or not. Actions that cause survival can be said to be of value to life–forms, to be the cause of their continued actions because they achieve the goal of survival; likewise, actions that cause the death of a life–form are not of value to it because they are self– destructive.

As Ayn Rand said: “Life is the standard of value.”7 The consequences of actions measured by this standard are always either values or disvalues.

This statement is validated by the observations and conclusions described in these paragraphs and the references to this book; they are its context, the links that explain what life is and why it is what it is. They explain how the concept of life is tied to reality. If you have been reducing the concepts you have been reading to your own percepts by following the links between their definitions and comparing them with your own observations, you will see why the statement is true. It is objective, a fact of reality.



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Biological Life–forms

Another way to put the point that life is the standard of value is that actions that lead to survival provide life– forms with values; all other actions, lead to death because they do not provide values. Some actions therefore, have value–significance because life–forms need them to survive. And, because they need values to survive, life– forms can be said to have gaining and keeping values as the goal of their actions; in other words, they are goal– directed. 8

It is the survival potential of objects and actions, therefore, that determines their value to a life–form. This fact is the very source and need of the concept “value” in the first place. If there were no life and death alternative, there would be no need to distinguish value from disvalue. Think of your own values. How much “value” would they have to you if you were dead?

The success of life–forms in achieving a value with any given action is not guaranteed. Their actions can fail. Failed actions do not cause survival, only successful ones do.

This situation leads to a complex, spiral form of causality identified by Dr. Binswanger that operates only for life– forms: They act to survive, and if they are successful, that action causes their survival, in which case they continue to exist to take further actions and the cause and effect chain continues; in other words, life–forms cause their own future existence by their successful actions to attain values; failure breaks the causal chain and death is the result. Whereas inanimate objects just exist, with no action required to maintain their existence.9



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These facts are the additional information mentioned earlier that is required to understand how life operates, and how life–forms are different from non–living objects. (I suggest you now take the time to think, to integrate these facts with your own experience, because they are absolutely prerequisite to understanding what follows.)

To distinguish the complex, spiral form of causality of a goal–directed agent’s behavior from the cyclical behavior of inanimate objects, Dr. Binswanger has three tests. In order to be goal–directed, an agent’s action must be:

• Self–generated (with some kind of fuel being an integral part of the agent’s structure),

• Its goal must have a value significance (cause survival),

• And the action must be caused by the goal’s value significance to the agent.10

In other words, a goal–directed action must be an action a life–form originates using its own energy as opposed to something that just happens to it. The life–form must need the value that will result from the action for its survival, and the ultimate cause of the action must be the fact that the same action has caused the life–form’ s survival in the past, thus enabling the life–form to stay alive to take another action in the present.

2.3 The Higher Animals and Man

In the higher animals and man, goal–directed behavior becomes even more complex, and two more layers of causality are needed to explain it.



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The Higher Animals and Man

Purposeful Action

Purposeful action is a still more complex form of goal– directed action. According to Dr. Binswanger, purposeful action is “a conscious action caused by the agent’s desire for some anticipated consequence of his action.”

In other words, purposeful action is an action of consciousness because the ability to feel a desire is a property of consciousness, and it presupposes a conscious life–form in order to exist. This means that there are two lower levels of causality required to explain purposeful action: The level of mechanistic causality by which the physics and chemistry of life operates and the level of goal–directed causality by which individual living cells and the various subsystems of life–forms operate.10

Consciousness and purposeful action as a property of complex cellular life–forms, is a more complex causal level that depends on these other two, and these ideas explain the more varied behavior of higher animals as opposed to that of simpler ones.

Necessitated vs. Neutral Actions

The only actions that are necessitated for life–forms are those required for their survival. The reason: Without survival, the life–forms cease to exist.

However, once a life–form has acquired a large enough supply of its essential values, it has in effect, bought itself some time when no actions may be required immediately for it to survive, or it may take any of several alternative actions with no significant effect on its survival status.



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Hence, it may be said that some actions of life–forms are neutral, neither values or disvalues to them, but always within some specific context.

For example, a squirrel that has stored a large supply of acorns may not feel driven by its instinct to search for food for a while and can engage in neutral actions, such as sleeping in its nest in some tree for a while or playing with another squirrel. Or, for a human who has enough money and is in good health, buying apples instead of oranges at the supermarket is a neutral action because it makes no difference to the person’s survival status which type of fruit he eats this week.

Note - Strictly speaking, no action is completely neutral to survival; every action a life–form takes has some positive or negative effect on its life taken as a whole, is a value or a dis– value. However, within the context of narrower time scales and situations, some actions can be neutral to survival.

The point is that in higher life–forms at least, though all are caused, not all actions are necessitated. Some actions may be caused by factors completely internal to a life– form when selected from several alternative actions that are possible at a given time, none with consequences that will have any major effect on the life–form’s survival.

This explanation of the behavior of life–forms is not possible using only simple, mechanistic causality.



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2.3.3 VolitionalAction

Volitional (free will) action is willful action by a conscious life–form; specifically, it is a capacity possessed only by human beings, the capability to choose certain actions from alternatives. Chosen actions are not necessitated actions.

All life–forms except human beings have automatic means of survival in the form of instincts or other means; in other words, they act automatically to survive.

Note - In this context, “automatically” means by instinct or other biological system, not a form of mechanistic automation.

Whether life–forms are scavenging for food, nest building, or reproducing, the intended outcomes are known in advance: They are gaining the values of food, shelter, and continued survival of the species. These values and the actions to attain them are programmed into the DNA of plants and animals. They have no choice but to follow that biological programming, though learning can modify it within a narrow range. This is true of all known life–forms, except humans.11

Note - Experiments that supposedly show animals forming “concepts,” using “symbols,” and using “language” are irrelevant, and whatever they show, it is not thinking. The ability to associate some perceived objects with other perceived objects is a capacity shared by humans and many higher animals. Though the



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exact differences may not yet be clear between the associations animals make and human first level concepts, there is strong evidence they are not the same: Humans go on to form more and more abstract ideas; some of these abstractions are more general until an ultimate genera is reached (e.g. - chair, table, couch, lamp, furniture, household item, man–made object, object, entity); others are more specific (e.g. - animal, fish, trout, brook trout); still others are formed by complex combinations (e.g. - lamp, toaster, electricity, power company, hydro–electric project, dam, fish ladder, trout, reproduction, spawning); the choices are endless. Animals are not capable of forming these kinds of conceptual relationships or mastering human language to manipulate them. Researchers have spent thousands of hours and many years attempting to show how animals use human language, yet not a single conversation with one has ever been heard, nor has an animal originated theory ever been published.12

According to Ayn Rand, volition is a property only Man possesses and it comes down to the choice to focus or not, which means to set thinking as a purpose or not. Man has the capacity to reason, but that capacity is not automatic; it must be activated by the choice to focus consciousness on some content and process it by putting forth mental effort.



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Human beings do not choose actions or motives or ideas; these depend on previous conscious content or the method used to acquire that content and are not primary. Of all life–forms, only men have the capacity to form concepts and manipulate them to identify the world they perceive with their senses in symbolic form.13

The ability to focus means to expend mental energy on some content to change it or connect it to some other content. Humans have the ability to do something animals apparently do not: Humans can manipulate the contents of their consciousness if they so choose, but they are not required to do so. The capacity is defined by human DNA, but using it is not automatic.14

One of the ways humans can manipulate the content of their consciousness is to use one percept (such as a word or a variable) to symbolize or stand for one or more others. This ability is not free; it requires effort and energy; it requires the choice to focus conscious processes. It is not a necessitated action because it is accomplished using mental actions which are optional. (How such actions work will be explained in detail in Chapter 4.)

Indirectly, the ability to choose is caused by the previous actions a person took to cause his survival in the past; a person must first exist to choose or do anything in the present. But the actual choices made are not necessitated by previous conscious events. Like each step of a long journey, the choice to step forward must be made with renewed effort. In life–forms, only survival actions are necessitated.



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In this sense, for humans some mental actions become first causes, when they are executed physically; some mental actions are not necessitated actions, but simply potential alternatives; they are optional, and they only cause changes in the outside world if an optional physical action is selected.

But the effort to take make choices and take certain actions does not have to be expended.15

2.3.4 Volition and Concepts

The means of survival of human beings is not instinct, but reason, or in the words of Dr. Binswanger: “rational, purposefully directed cognition.”

Reason requires concepts to function, and concepts are formed by a specific method of using one’s consciousness. The method entails a process of selecting common properties of perceived objects or using other, pre–existing concepts as the data for defining a new concept, and this always occurs in a given context. The new concept is formed by a process of measurement omission; that is, the measurements of the properties of objects or other concepts are limited to specific ranges of values and used to form the definition of a new concept. The result (if this is done without error) is a complex hierarchy of knowledge about reality, knowledge that the automatic consciousness of other animals cannot form.16

Volition is required because there is no way to perform this process automatically. Its results are not predictable.



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Automatic processes are necessitated actions and depend on either knowing their intended outcome in advance or knowing a path to find that outcome by means of some implicit reference, such as with instincts in animals or electronic control theory (cybernetics) in machines.

Concepts and reason identify new knowledge for which neither the content nor how to find it is known. If the outcome or the path to new knowledge was known in advance, there would be no need to perform the process of producing it in the first place. Volition is one of the things that makes new knowledge possible because it is caused, but not necessitated action, and can it can therefore lead to the unpredictable.

Volition is yet another level of causal complexity that exists in one kind of conscious life–form: Man. Volition is a capacity that results from man’s particular kind of consciousness, one that must make choices to form concepts to identify reality - so he can think - so he can act - so he can survive to cause his own future.

Volition presupposes the capacity for purposeful behavior which man shares with higher animals; this capacity in turn presupposes the capacity for goal–directed behavior, of life and its spiral causality of continuously attaining values; life itself presupposes the mechanistic causality that explains the dynamic nature of the rest of existence.

All of these levels are required to explain the behavior of human life–forms, and new properties emerge at each of them. All of the concepts in the explanation are linked by the objective method used to form them from observations of reality, and from the other concepts



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implied by their chain of definitions, an unbroken chain that forms the links between their contexts and connects all of them to reality.

Man–made Objects

One of the consequences of power of volition in human beings is man–made objects; these objects are not natural insofar as they would not exist in nature apart from man’s choices. Their identity depends on human knowledge, which means it depends on human volition.

Natural objects are what they are as a result of whatever chain of mechanistic cause and effect happened to create whatever configuration of reality they happen to be. Moreover, they could not have been different from what they are. They are part of reality; natural objects are what they are, period.

Man–made objects, on the other hand, can be designed to fit any human purpose. So long as they are consistent with the laws of nature, it makes no difference (except to humans) what attributes they have. Human purpose is determined by human knowledge, and human knowledge is determined by thinking (or the lack of it); if a man thinks or not is determined by his own choice, and so are any man–made objects he may design.

Symbol systems are one type of man–made object.17

2.4 Computer Simulations and Digital Life–forms

Life and consciousness, as natural objects, can only exist in nature the way they are found in nature – as properties of life–forms.



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2.4 Computer Simulations and Digital Life–forms

One highly flexible type of man–made object is the computer simulation program. It is flexible because it consists primarily of information; that is, computer based objects functioning not as physical objects, but as symbols or virtual placeholders for real objects, informational placeholders substituting for parts of reality. Symbol systems can be manipulated and reconfigured much easier than the objects they represent in reality, and without the risk or expense that can sometimes occur when real objects interact.

Simulations are performed routinely for inanimate objects in many fields of research and increasingly for life–forms in the relatively new field of Artificial Life (AL). In the current state of the art, most simulation programs simulate natural selection using genetic algorithms, ecologies of large groups of life–forms, and the automatic behaviors of life–forms navigating around various objects in their environment, gathering simulated food, and so on. Simple learning is also simulated.

Even though programs in the current state of the art simulate life to some extent, none that I have seen or read about is based explicitly on the multi–level kind of causality described in this book. Yet, to successfully simulate a higher life–form, that is precisely what is required.

A computer simulation of life and consciousness must mimic the essential causal nature of the higher life–forms if it is to have similar functionality.



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2.4.1 Layered Models

A useful tool to understand systems with multiple levels is a layered model.

Computer systems are frequently explained in terms of such models. For example, the bottom layer of the model is the computer hardware, the next layer is the operating system, the third layer could be an application program, and a forth layer the functions an application program performs on a user’s data.

Likewise, computer simulations of life–forms and consciousness can be explained using a layered model. As in the previous example, the computer hardware is the first layer and the operating system the second layer.

In the simulation system I will explain in detail in the next chapter, the third layer is a programming language development environment. In the case of the simulation system I have invented, in one implementation the third layer is the Prograph™ object oriented computer language from Pictorius, Inc., though that may be changed to the Java™ or some other environment such as C++ in the future.

The forth layer is the simulation program (the Digital Life–Form (DLF) Program), which is a prototype program I have written in Prograph. The DLF Program that simulates reality, digital life–forms (DLFs), and their interaction with it.

The design of the DLF Program also contains some additional layers that simulate the levels of complex causality by which life–forms operate, namely goal–



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Computer Simulations and Digital Life–forms

directed behavior, perception of the simulated reality, purposeful behavior, volitional behavior, and concept formation.

Layered Models and Layer Substitution

One of the interesting aspects of layered models is the ability to substitute layers.

For example, some computer languages can run their programs on different hardware platforms unmodified. So if you think of a layered model with one of these programs as the fourth layer, it is possible to substitute different computer hardware (layer one), a different operating system (layer two), the appropriate version of the language environment for the new operating system (layer three), but still retain the same application program (layer four). For instance, the Java programming environment can run on computers that use any of the popular PC operating systems. You could, therefore, substitute any popular type of PC for layer one, substitute the corresponding operating system and Java environment software for layers two and three, and still run the same Java program in layer four.

This is, in essence, what DLF Simulation Technology does to simulate consciousness. The idea is to substitute mechanistic computer hardware and several layers of software for their mechanistic biological counterparts. Then simulate the complex causality of life–forms in the upper layers of the simulation system. The digital life– forms in the system will have their virtual teleological processes animated by the mechanistic causality of the



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computer simulation system in a manner that is analogous to the way in which biological life–forms are animated by the mechanistic causality of chemistry and physics.

Layered Models and Context Boundaries

Another important idea to grasp about layered models is that the layer boundaries are also context boundaries. This is important because operating principles can change across the boundaries from one context to another.

For example, in a computer system the hardware operates by various electrical principles that define the manner in which its components interact when an electrical current flows through them; across the context boundary in the second layer, the operating system functions based on mathematical and programming principles, as well as the requirements of a human interface; across the context boundaries in the third and fourth layers, a computer language development environment and an application program function according to logical process design, programing, and human interface principles; and in the fifth layer, the operation of a user’s application program may function according to some other operating principle such as double–entry bookkeeping.

Just as with the example used by Dr. Binswanger of the property of being able to “roll” emerging from the fact that two hemispheres that have been glued together, new properties emerge from each layer in a layered model as you move from the context of one layer to that of another. In the top layer of the example I used in the previous paragraph, the property of being an accurate accounting tool emerges from the system as a whole when all the layers inter–operate together.



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Computer Simulations and Digital Life–forms

Complex Causality as an Emergent Property

In a similar manner, programs that simulate life–forms can have multiple layers; each layer simulates a causality level, and each is a separate context with its own set of operating principles and emergent properties.

If we assume the same lower layers I described for the DLF Program in Section 2.4.1, then the fifth level in the system can simulate goal–directed behavior, the sixth level can simulate purposeful behavior, and the seventh level can simulate volitional behavior and concept formation.

Just as the complex causality of real life–forms is dependent on the mechanistic causality of biochemistry, the complex causality simulated in layers five to seven in the DLF Program are dependent on the mechanistic causality of layer four and the ones below it in a computer simulation system.

The idea new to artificial life research being introduced here is the substitution of the form of mechanistic causality that supports the layers of complex biological causality ; in other words, the substitution of a computer system for the mechanisms of biochemistry and a specially designed program to simulate goal–directed, purposeful, and volitional behavior as the interactions of a virtual entity with reality.

This may not seem like a new idea because people have been talking about simulating life with computer programs for years. To realize this idea is new, however, requires the recognition that biology is not mechanistic itself, which is a distinction not presently made by most



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computer scientists, and that an ordinary mechanistic computer program is therefore not sufficient to simulate life–forms.

The reason this important distinction is not made, as explained in the Introduction, is the epistemological approach most scientists use to form concepts and their implicit Idealism or Materialism. I hope that now the reader understands how using an objective method to form concepts differs from these other two approaches to knowledge, and can see how using an objective method leads to different conclusions about the nature of causality, life, and consciousness. These new and different conclusions, in turn, lead to very different strategies for simulating life and consciousness in a computer system.

Digital “Biology”

The key to understanding the “biology” of digital life– forms is realizing that they are not alive, but are calculations that are designed to mimic life. Furthermore, it is necessary to understand that goal–directed behavior is a more complex form of causality than mechanistic causality.

Once these concepts are reached, it is possible to design and a system which simulates the performance of goal– directed processes, and it is those new kinds of processes that then form the basis for simulating digital “biology” on a computer system.



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Summary

2.5 Summary

In this chapter, I have set the biological context that is necessary to understand what is required to simulate consciousness in a computer system.

I have pointed out that while life depends on mechanistic causality, that level of causality alone is not sufficient to explain how life operates, and that goal–directed, purposeful, and volitional behaviors are the result of successively more complex forms of causality. Finally, I have related these levels of complexity to layered models and the concept of emergent properties, and explained how these ideas can be used as a basis for a computer simulation of the behaviors of life–forms including consciousness.

I have also explained why in order to understand the reasoning about this subject, an objective method of forming and validating concepts must be used.

Now that both the philosophical and biological contexts have been set, I can go on to describe how to implement their practical consequences in computer code that interacts with reality and reduce the simulation of consciousness to practice.



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3

A Consciousness Simulator Design

3.1 Introduction

The first two chapters contain a lot of ideas that may be new to many readers, so this is probably a good point to take stock of where I am in the explanation of the topics contained in this book, and to provide a preview of where I am going with the explanation next.

With the identity of existence, causality, life, consciousness, and their basic relationships having been objectively defined and connected to a wider body of ideas in philosophy and science, to set them as a context, I can now translate conclusions drawn from those ideas into a design for a consciousness simulator. That is, by knowing the identity of the objects and processes that must be simulated to mimic awareness, and by knowing how they are related, a computer simulation system can be designed and implemented that reduces these abstract ideas to practice and performs the simulation of consciousness.

Therefore, in this chapter, I explain in a design overview the nature of a computer system that is a specific example of the ideas and implementation strategies I have been



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3.1 Introduction

describing, a system that employs a new kind of teleological computer program that simulates the goal– directed behavior of life–forms as they interact with objects in a simulated existence, including an overview of its conceptual level functions. Building on that foundation, I explain how to simulate perceptual consciousness as a form of goal–directed and automatic purposeful behavior that regulates the interaction of digital life–forms with a simulated reality.

The process of concept formation and how it leads to the emergence and simulation of volitional, self–conscious, and natural language behaviors similar to those exhibited by human beings is explained and described in Chapter 4. In that chapter I explain how those properties emerge from the conceptual form of simulated consciousness that itself emerges from the perceptual form of simulated consciousness described and explained in this chapter.

Chapter 5 is the description I used for my patent application, and it integrates all the main ideas contained in this book in one place. The chapter begins with a brief summary of topics that are covered in the first four chapters to set the context for someone skilled in the art of computer programming, a summary that is intended to jump–start their thinking on how to design their own computer–based consciousness simulator and become a future licensee of DLF Simulation Technology. In addition, Chapter 5 provides the details of how to simulate advanced conscious processes such as concept formation, self–consciousness, volition, and how a DLF can encode and decode its own simple natural language sentences.



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Now that it is clear where I am in the my explanation, let us continue with the rest of it.

3.2 A Design Overview of Digital Life–Forms (DLFs)

Digital life–forms are virtual entities that behave similarly to living organisms, including their conscious behaviors. These entities are unique in the way that they simulate life. In terms of the layered model I have been discussing, DLFs use a design with multiple layers, each of which is its own logical context and the cause of the layer above it. These logically connected, but distinct contextual layers simulate the multiple levels of causality that life–forms simulated on computer systems require to function.

At the most basic level, the computer hardware, operating system, programming language, and the simulation program layers (layers one through four) taken together simulate mechanistic causality for digital life–forms in a manner that is analogous to the way physics and chemistry are the mechanistic cause of real life–forms.

Unlike most computer program objects, however, the existence of the DLFs themselves is conditional: DLFs require simulated food and must act in certain ways to attain values or they cease to exist, just like a real life– form. In other words, they exhibit goal–directed behavior and they must cause their own future survival or they “die.” (Their instance is either erased from the computer’s memory, or their properties are set to zero.) The property of goal–directedness emerges from the design of the DLF Program, a design that is layered on top of standard computer programs; the teleological layer is layer five in the layered model.



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Note - The DLF Program in its current implementation uses an object oriented programming language. In the terminology of such languages, each object falls into a category, and the particular objects in any category are called “instances.” So there is a category of objects in the DLF Program called DLFs and each DLF is “born” when its instance is created and its attributes are given values and “dies” when that instance is either erased from the computer’s memory or its attribute values set to zero.

3.2.1 Simulating Life and Death in a Computer

Artificial life programs in the current state of the art simulate natural selection using genetic algorithms or whole simulated ecologies in which groups of simulated life–forms compete for simulated natural resources. In other types of experiments, the function of the nervous systems of life–forms is simulated to various degrees using neural networks or robot designs, and certain physical behaviors of life–forms are simulated using copies of plant, insect, or lower animal sensory/motor nerve pathways, or in come cases, even simple electronic circuits made from bits of old appliances. Other kinds of animal behavior has been simulated with models of the instinctual behaviors of higher animals.

These simulations can be explained using the layered model described in the previous chapter: The life–like behaviors of the simulated life–forms emerge from the simulation programs and do resemble some of the



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behaviors of real life–forms. Moreover, to the extent that these simulated life–forms perpetuate their own survival, some of them may simulate goal–directed behavior.

However, to the best of my knowledge, none of the these programs are explicitly designed to simulate the levels of complex causality that I described in the previous chapter. To whatever extent such programs simulate behavior that is goal–directed, I believe they are that way because the life–forms that served as a model for their design are goal–directed, not because their designers and programmers understood the type of non–mechanistic causality described in this book. There is no evidence that I have seen as of the time of this writing that the inventors of current state of the art life simulations knowingly designed complex causality into their simulation programs.

In searches I have conducted over the Internet and other places, I have found some programs that seemed to be goal–directed, but are so only implicitly. And none has claimed or even implicitly achieved simulated consciousness such as I have been describing in this book, as far as I have been able to ascertain.

In all of the examples I have been able to find, the designers seem to implicitly accept the materialistic idea prevalent in Western science that consciousness does not exist, and they assume that all actions of life–forms are therefore explainable by mere mechanistic or “billiard ball” causality; consequently, that is how they design their simulations of them. In Chapter 5, I review several examples of these systems in more detail.



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A Design Overview of Digital Life–Forms (DLFs)

Duplicating Levels of Complex Cause and Effect

In the DLF Program, the DLFs are designed explicitly as complete organisms with both mind (simulated consciousness) and body as found in real higher life– forms.

Furthermore, the simulation of a DLF’s consciousness depends on the survival of its body: If the DLF’s body “dies,” so does the DLF’s “mind.”

The DLF Program itself is the forth layer in the layered model I have been referring to, and the top layer of mechanistic cause and effect in the simulation system. The simulation of the DLFs at the fifth level in the layered model is where the spiral cause and effect of goal–directed behavior occurs, and is analogous to vegetative behaviors in real life–forms. Automatic perceptual behaviors are layer six in the layered model.

Life is not a miracle force; it is a complex, multi–level causal relationship between a life–form and the world in which the life–form’s continued existence is determined by its own self–generated actions. That relationship involves the alternative of life or death for the life–form, depending on how the life–form controls its own behavior, with survival as its primary goal. The actions a life–form selects from alternatives, either automatically or by choice, determine its fate.

A DLF’s primary goal is also survival; this goal is accomplished by taking various actions to obtain simulated food and to learn about its world. If the DLF takes the right actions that cause it to get simulated food, it “survives” and its instance is saved for continued use in the DLF Program; if the DLF takes the wrong actions, it



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“dies” by allowing its instance to be automatically deleted by the Prograph language, and it is replaced by a new DLF instance.

A DLF simulates Dr. Binswanger’s three tests for a life– form:

• Its actions are self–generated.

• Its actions have value–significance in that the DLF needs the goal of the action (simulated food) to survive.

• Its actions are caused by the goal because the goal (the simulated food the DLF acted successfully to acquire previously) provides the self–generated Energy Packets (EPs) that keep the DLF from being deleted and enable it to survive to act in the future.1

Like a real life–form, a DLF’s actions cause its own future survival.

What Separates a Living System from the World?

In real life–forms, the life–form and the world are separated by the life–form’s skin or other outer boundary. That boundary contains sensors (such as eyes, ears, touch, taste, smell receptors, and so on); these sensors transduce the energy impinging on the life–form from its environment into neural energy that is then transmitted to various locations within the life–form for processing.

Likewise, the life–form has effectors (such as arms, hands and fingers, legs, a tongue, and so on) that transfer energy from the life–form back into its environment to cause various changes in that environment. These make possible the actions the life–form takes that close the causal loop with its environment and make the life–form a closed



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system. The identity of the complete life–form and its relationship to reality form a dynamic, cyclic system, a special kind of complex causal relationship that is the essence of life.

The identity of the skin or membrane of a life–form causes a separation of a life–form from its environment and differentiates it from its environment. The skin or membrane is at the same time a barrier for which energy must change form in order to cross (by transduction), and it is a causal context boundary. The body of the life–form is the physical layer (and the first layer of a layered model) by which the life–form interacts with the physical world, but the internal operation of that first layer is different from the world. The body operates by goal– directed, electro–chemical causality vs. the mechanistic, electromagnetic and kinetic causality outside of it.

Note - Goal–directed electro–chemical causality is itself caused by mechanistic electro–chemical causality. The bodies of real life–forms are actually a combination of the two forms of causality operating in different logical layers.

This physical boundary is somewhat analogous to the physical layer of a computer system or a robot: Both these objects have transducers to convert various forms of electromagnetic and kinetic energy from their environment into electrical energy, which is the form appropriate for the causal inner workings of computers or robots at the physical level. But the causal context changes to a lesser extent; at the physical layer, the computer still operates by only mechanistic electromagnetic causality inside, just in the form of



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transistors and other circuit components rather than photons and pressure changes; the physical layer in a computer or robot is not a goal–directed form of causality combined with electro–chemical mechanistic causality like it is in a life–form.

The only way a computer system or a robot (as they are currently designed) can become goal–directed is if the appropriate program code is written at a higher level in its architecture to give it the correct identity and relationship to reality to simulate goal–directed causality and behavior.

3.2.4 Action Control in Biological Life–forms and DLFs

In biological life–forms, automatic action control is implemented by the pleasure/pain system; this system is very rudimentary in lower animals, but very sophisticated in higher animals and humans. The pleasure/pain system of life–forms is automatic in the teleological sense, not in the mechanistic sense.

If a life–form is too warm or too cold or overworks its muscles, it feels pain, and that has the effect of causing or stopping various actions which, in turn, cause the pain to stop. Some actions result in a life–form feeling pleasure; those actions tend to continue until a feeling of satisfaction occurs. If the life–form persists past the feeling of satisfaction, pain at some point stops the action. If a life–form is inactive for too long, a feeling of anxiety builds until the life–form acts. There are entire ranges of these feelings; some are physical, and some emotional such as fear, anger, joy and so on; these feelings automatically control the behavior of life–forms as a basic



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means to keep them within evolutionarily established survival ranges; the pleasure/pain system is programmed into their DNA.

All feelings are automatic evaluations of something in reality, with life as the standard of value. Physical pleasure and pain are sensed directly; emotions have physical effects, but are based on a relationship between perceptual or cognitive information and some survival requirement that is known by a life–form, such as when an animal or a person perceives a dangerous object like a tiger or fire and feels fear.

DLFs have simulated feelings to enable them to self– regulate their behavior like real life–forms. Some of these feelings simulate physical pleasure/pain and others are simulated desires or other emotions. This latter design feature causes purposeful behavior to emerge from the successful actions of a DLF: Its survival enables it to perceive objects that have brought it simulated pleasure in the past, which it remembers; these feelings are desires that cause its purposeful behavior, that cause it to act in order to have those objects and simulated feelings again in the future.

In other words, simulated feelings (which are “real” from the perspective of a DLF, at least in a causal sense) are what provide the motivation for action and its behavioral control; for a DLF, action control is therefore indirect and not explicitly part of the DLF Program code itself, but an emergent property of the program code interacting with data from the world (simulated or real) and a DLF’s values. A DLF’s behavior can be automatic or volitional depending on its level of simulated consciousness and the state of its simulated life: If a DLF has sufficient EPs



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(simulated fuel) and no threats to its simulated life so no necessitated behaviors are required to maintain its existence, the DLF is free to engage in any optional behavior alternatives that may be open to it.

In biological life–forms, being motivated by desires means that the control of actions depends on the life– forms’ consciousness (both the identity of the process that form their relationship to reality and the content in the form of knowledge and values it has in memory); in a DLF, action control is likewise controlled by its simulated consciousness, its desires, its values, and its relationship to its world.

Animals have instincts, which are pre–packaged survival knowledge that is triggered in appropriate circumstances. For example, a spider knows how to weave a web or a bird knows how to build a nest. Experiments have shown that these behaviors are not equivalent to human knowledge. A spider does not know how to repair a damaged web as a human might, but must start over from scratch and rebuild it. These behaviors are completely automatic and life–forms that have them do not seem to be aware of what they are doing; they just do it.2

It has been shown that humans, on the other hand, do not have similar automatic behaviors. Human babies do not know how to build shelters or find food or even walk or swim as many animals do; they must learn every survival behavior except very basic reflexes such as the sucking reflex. Humans must literally program their own soul.



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A Design Overview of Digital Life–Forms (DLFs)

3.2.5 Simulating the Higher Cognitive Functions

The way in which children learn survival behaviors is different from that of animals; humans use volitional behavior which manifests itself in the choice to focus their consciousness or not and the ability to form concepts. Human knowledge therefore takes an entirely different form at the highest levels than it does for animals: While humans share similar perceptual capacities with animals, volition enables humans to form concepts which enable them to have an enormous cognitive efficiency or unit economy of items processed that animals cannot match. Human concepts use a single symbol (a word) to stand for a potentially unlimited number of objects.3

DLFs have been designed to have the ability to form simulated concepts, which are open–ended data structures that allow a single symbol to designate all objects of a given type, past, present, or future. This capacity, once it has emerged, will enable DLFs to identify their world, including themselves, using only a few symbols like human beings do; human language enables concepts to be used to form complex conceptual relationships. Once the concepts have been formed and symbolized with words, a DLF can use language to simulate thinking and to communicate with humans and other DLFs; these capacities emerge from the conceptual level of simulated consciousness, and they are not written into the DLF Program code; they are actions of the DLF’s simulated consciousness and its highest level of causality.

Self–consciousness, in the form of a simulated “soul,” is a virtual entity that emerges from the conceptual level processes as they interact with reality and memory. First



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causes also emerge from the conceptual level conscious processes, but they and the entire conceptual level of awareness are caused by the levels below it; they are neither programmed nor necessitated.

Consciousness is neither a mystical (or otherwise supernatural) entity which has some special reality, nor is it a diaphanous one; it is an active, causal, goal–directed relational process performed by certain life–forms. Likewise, simulated consciousness is a goal–directed relationship that exists between subject and object (a perceiver as a virtual entity and the perceived). As with its biological counterpart, simulated consciousness has a specific, limited identity. That identity is born of the interactive, perceptual and conceptual process between DLFs and their world. The result of simulating that process at the perceptual level is that the DLFs gain the information they need to act in order to cause their future existence; as with animals, the simulated perceptual consciousness of DLFs is automatic and infallible. At the conceptual level, the efficacy of the process depends on the objectivity of the subject (the self-conscious DLF Mind simulation); the DLF Mind simulation is the cause of the formation of its concepts, but like the human consciousness that it mimics, it is designed to be fallible, so only the DLF Mind simulation can insure that its concepts are consistent with reality, and therefore objective.

Each concept must be formed by a separate simulated act of will by the DLF because concept formation is not an automatic process like perception is; (there are so many potential concepts that if it were automatic it would soon lead to cognitive overload, defeating its biological purpose of cognitive efficiency.) Each act of concept



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Simulating Perceptual Consciousness

formation by a DLF is not caused mechanistically or automatically from previous content or something outside the DLF, but by a self–generated, optional mental action.

Each concept that is formed changes the identity, and therefore the action capacity, of the DLF. As concepts accumulate and relationships between them are established, the full power of volition gradually emerges; the DLF eventually gains more simulated volitional control as well as the simulated capability to be self– conscious of and to alter its own identity; the DLF can, from that point forward, cause its own future identity and control its own action capacities by consciously changing its own identity. In other words, the DLF becomes self programming.

All of these capacities form three new levels of causality above and beyond the mechanistic level (layers five, six, and seven in the layered model), and they are all emergent properties by which the DLFs are able to causally affect their world, and themselves as part of that world.

These capacities, thus explained, are also new ideas to the state of the art in artificial life and artificial intelligence.


As an existent in reality, consciousness is a process that is a property of life–forms, a virtual entity that life–forms cause to exist for its survival value. As part of human knowledge, consciousness is a self–evident axiom; that is, it cannot be analyzed and broken into simpler components. It simply is.4



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Operationally, however, consciousness is an active, goal– directed process of differentiating and integrating knowledge about reality5, and like any other process, consciousness is a series of action events, or C.Events as I have called them in the terminology of the DLF Program.

The Five C.Events in the DLF Program

Goal–directed causality is cyclical, and can be thought of as spiraling into the future, with each cycle of cause and effect being a new point on a time line. This is also true for the causality of consciousness, which is an attribute of goal–directed objects.

Like a modern computer operating system, my design of simulated consciousness is “event driven” with the various causal events that simulate consciousness continuously repeated while the simulated consciousness is active, to effect a similar type of temporal spiral.

In the DLF Program, the C.Events following each other extremely rapidly like the frames of a movie or TV screen produce an apparently seamless view of reality for a DLF. The five C.Events in the DLF Program are:

1. Perception (Sensing and integrating content from simulated reality)

2. Pleasure & Pain (Evaluation of content as good or bad, with the DLF’s “life” as the standard)

3. Default Action Control (Automatic behavior control)

4. Memory (Content from previous C.Events)



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3.3 Simulating Perceptual Consciousness

5. Action (“Closing the system” by causing changes in simulated reality)

In biological life–forms, the process steps of consciousness may not be exactly the same, but it is a well known fact from studies on human reaction time done by the military and others as far back as the 1950’s that consciousness is limited to less than ten objects at a time and that automatized conscious reaction times occur in less than half a second. In DLFs, this means that the C.Event cycle speed will need to be at least that fast to be a realistic simulation, though that speed will ultimately depend on the computer hardware and the efficiency of the DLF Program or other implementations; it will also depend on the content that is being processed, as some types of content will require more processing time than others.

In the DLF Program, a C.Event is simulated with five program methods that call one another in sequence in an endless loop.

Figure 3-1 The five methods of a C.Event



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3.3.2 Simulating Sensation

To be designed to be causally equivalent to a life–form, a computer simulations system must have a connection to reality similar to a biological life–form. That means that it must have sensors and effectors, and it must interact with reality on the same scale as man and the higher animals.

Note - The examples in the following description were developed using the Prograph object oriented programming environment. It is important to understand that in this environment, the flow diagrams are the computer code. The figures below are screen shots of program windows called “methods” and sub–methods called “locals.” The format of “<<Some Name>>” is the Prograph language’s way of indicating object instances.

TheWorld

Designing a simulated life–form is a difficult task. One way of simplifying it is to use a simulated world to be its reality in early versions of the program. This course of action eliminates the need for real sensors, effectors, and the need to code the programs to drive them, which is a significant savings in effort and expense. Once the basic functionality has been developed and tested on a simulated world, real sensors and effectors can be substituted for the virtual ones.

Note - While using a simulated reality is useful for early development of the simulation system, the real world must eventually be perceived by



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DLFs if they are ever to achieve human–like behavior because humans exist in the real world and our knowledge is based on things like our sense of the scale of real objects. This does not mean that some simulated worlds, such as virtual reality or the Internet, could not be used in later DLF development or training to take advantage of the fact that DLFs are in fact “creatures” of the computer.

A virtual world for developing a DLF needs to be a place that contains dynamic objects that can be sensed and causally affected by the DLF’s.

In the case of the DLF program, its simulated world is a test space and a drawing space in which simple two dimensional shapes can be drawn and moved around by a human with a mouse, or by a DLF.

Energy Transfer and Sensing

In order for a DLF to sense the simulated world, reflected energy is simulated; this simulated energy carries the identity information of the objects from the test and drawing spaces to the simulated sensors in a DLF. Like a sensation in a real life–form, the simulated sensor output contains the identity information about the objects that was transferred to the DLF by the simulated energy and transduced by the simulated sensors.

Identity Transfer

Just as real energy such as reflected sunlight carries with it the identity of the last object it interacted with when it was reflected, the simulated energy in the DLF program



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carries the position, shape, and other identity information of the objects in a DLF’s simulated world to its simulated sensors. This information is not explicit, but implicit in the relationships between the simulated sensations.

Further processing is required to make the identity information explicit and useful to a DLF in the form of simulated percepts.

3.3.3 SimulatingPerception

As explained by Ayn Rand, percepts are integrated sensations; that is, sensations that have been processed by the brain of a life–form to extract the implicit identity information about objects in reality that the sensations contain, and to make it explicit.5

If a DLF is to perceive the world in a manner similar to a human or higher animal, the perceptual level processing of consciousness must also be simulated.

The World as Objects

In the space that simulates the DLF’s world, objects can be drawn with a mouse using standard Prograph language sketching methods. The resulting objects are two dimensional shapes such as lines or simple open and closed shapes such as letters or circles or triangles, and they can be either simple or composite; a triangle, for example, can be a single line or a combination of three lines. The shapes can be either stationary or move.

Figure 3-2 below shows the Existence Window that simulates reality in the DLF Program, including some example shapes I have drawn with a mouse.



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Figure 3-2 Simulated world in the DLF Program Existence Window

The simulated world also includes ascii text objects which are strings of characters that exist in a text space separate from the shapes, in this case, the string object “Hello DLF” is shown in the window. This design was used simply to take advantage of existing code in the Prograph development environment for capturing text, instead of reinventing it.

Energy and Identity Transfer

Using standard programming techniques and Prograph data structures, the data that make up the simulated object instances are transferred to a data type called “energy” that carries the raw data about the objects (ascii characters for text (called <<Symbols>>) and x,y coordinates for shapes (called <<Graphic Items>>)) from the DLF’s simulated world to its simulated sensors.

In order to do this, the simulated “light” must be changed to contain the identity of the simulated objects, and then radiated to the simulated sensors.



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Note - Parts of the text in the following screen shot figures may not be readable in some versions of this book.That is an unfortunate artifact of its conversion into graphic form.

Figure 3-3 Identity information from the Existence Window

So you can see that in figure 3-3, the raw data of the simulated objects in the Existence Window is input to this method inside the Symbols and Graphic Item instances (data structures). The Light Interaction local simulates the interaction of light with the simulated objects; in other



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words, the identity of the simulated light is changed to match the objects it has encountered, just as real light does in reality.

Now if we double–click on the Light Interaction local, we get the following:

Figure 3-4 The Light interaction local

In figure 3-4 we see that both the Text and Graphic Item simulated reflections are extracted from their instances, collected, and converted into simulated energy to be “radiated” by the calling method. The method simulates how real light acts as “carrier” for the identity of objects from which it is reflected. In other words, it simulates how light that is reflected from green leaves carries that



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color and shape identity information to a perceiver, or to a TV camera and then to a perceiver. The crucial point here is that identity information about the objects in the scene is conserved by the intervening processes.

Figure 3-5 Collecting object reflections

The list of graphic items that carry the simulated objects’ identities then pass to a converter local that converts them from simulated objects into simulated energy that carries their identity.

The conversion details are shown in figures 3-6, 3-7, and 3-8 below.



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Figure 3-6 Converting to simulated reflections

Note - The numbers to the left of the window names, such as 1:1 in Figure 3-1 means “1 of 1” cases, to indicate the number of cases for a method, of which there may be several. Each case deals with a specific type of processing for various types of instances. For example, in Figure 3-8, there are two cases, one for curved lines and one for straight lines.



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Figure 3-7 Calculating object attributes or properties



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Figure 3-8 Other attribute calculations

Once all the attributes or properties and their associated values have been calculated, these data are repackaged into a simulated energy data structure and sent on their way.

Less processing is required for text objects, because their properties and values are self contained in the ascii code and easily extracted by the simulated sensors.



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Sensation

The first stage of perception is sensation; in this stage the sensors transduce various types of energy from outside a life–form into neuro–chemical energy inside the life– form, conserving the identity information the energy carries to the perceptual processes inside.

In a DLF, this process is performed by simulated sensors that simulate the transduction of the energy to bring it into the DLF. Figures 3-9 and 3-10 below show the code for this process.

Note - Future versions of the DLF Program that use actual sensors instead of virtual ones will require more complex and extensive methods to drive and transduce real energy from real sensors; other methods will also be needed to extract the identity information of real objects from pictures, sounds, and so on at the perceptual level.

100 How to Simulate Consciousness Using a Computer System Blue Oak Mountain Technologies®, Inc. Patent Pending

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Figure 3-9 The percept method


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Figure 3-10 A sensing event

As you can see, the simulation for a sensing event amounts to simply calling the Get Value methods for the Energy and Scene objects. Doing so extracts the Text and Object list items so they can be passed on to the local that performs the perceptual processing.

Perception

In biological life–forms, perception integrates sensations into the objects we see in reality around us.


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To make a simulated percept out of simulated sensations, the text needs to be parsed into text object and the shape identity information for each text object and shape object extracted. This information is stored in the current instance of the DLF Mind as Symbol and Shape attributes, and it is these attributes that simulate percepts.

The process of integrating simulated sensations into simulated percepts, therefore, amounts to extracting the identity information from the incoming data structures, transferring it to instances of the Symbol and Shape data structures, and setting their values to simulate the DLF Mind’s “awareness” of the simulated percepts.

Figures 3-11 through 3-14 below show the essence of the code for simulating percepts of objects. Instances of all the data structure objects enter through the terminal connections at the top of the window.


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Figure 3-11 Percept formation event


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Figure 3-12 Perceive and remember shapes

Figure 3-13 Perceive objects


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Figure 3-14 Create and set shape percept

The end result of the simulated perceptual process is a list of perceived object instances in the Shape attribute of the current instance of the DLF Mind. These objects are the form in which DLFs “perceive” reality.

Consciousness is a relationship between a life–form and the objects in the world outside of it; the perceptual objects in the DLF Mind, the content, are the internal portion of that relationship, which correspond in various ways to the objects in reality outside to which they are related.

The perceptual objects’ identities consist of properties and values (or attributes or characteristics or features, depending on what terminology you choose); those properties and values are related to the sensations from


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which they were extracted, which are the internal form of energy produced by the sensors in the transduction process. These data are related by the mathematical techniques that underlie the perceptual property extraction process to the energy of the sensations; the sensations are related to the energy that carried the identity information to the life–form by the mechanism that enables the sensors to transduce energy from one form to another; the energy that carried the identity information to the life– form in the first place was itself changed when it interacted with the object external to the life–form; that external energy and the object it interacted with did so in a specific way because they are what they are (A is A), and finally, they are what they are because they are (Existence Exists).

This entire process is an unbroken chain connecting simulated consciousness to reality.

The DLF Program code shown in the figures above works, and you can see first hand that simulated perception is explained as a chain of various kinds of cause and effect operating by various means at several different levels as described in my earlier explanation of the layered model. The mechanistic cause and effect interactions that occur at the lowest layers (physical objects and energy in the world), would occur life–form or no; the difference the DLF makes is that the higher layer processes it possesses enable new properties to emerge, the complex forms of goal–directed cause and effect that form the relationship of simulated consciousness that links the processes in the DLF’s mind to reality, and thereby enable the DLF to “know” that reality.


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So given these processes, the simulated perception part of the C.Event is complete and the Pleasure/Pain method is called to evaluate the newly perceived objects.

3.3.4 Simulating Evaluation (Feelings)

In the previous chapter, I explained how the pleasure/pain mechanism in biological life–forms works as an automatic, goal–directed process to regulate their behavior; the operation of such processes are well known biological facts; and even though they are usually explained in mechanistic terminology, the overall processes remain the same.6, 7

Simulating One Form of Pleasure and Pain

One challenge in writing the DLF Program has been to find a way to simulate such reactions in a DLF. After all, DLFs are not alive; they are program methods and data that simulate life–forms. DLFs cannot really feel anything. Yet, an accurate, dynamic simulation of the appropriate causal sequences, can mimic feelings and make it seem as if DLFs can feel.

The simplest pleasure/pain reactions to simulate is “hunger.” Since a DLF simulates a biological organism, it must have an internal energy source to survive, and when that internal source runs low, the DLF must somehow be aware of that fact so it can replenish its energy.

Pleasure and Pain provides life–forms with an almost instantaneous means of evaluating the survival or value– significance of objects based on biological systems that


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have evolved over millions of years: In most cases, harmful objects cause pain and beneficial objects cause pleasure.

Since a DLF’s life is conditional, it must also evaluate its percepts just like a biological life–form does, and the percepts must therefore pass certain tests of value– significance before the DLF can go through another iteration of goal–directed cause and effect, of perception and action.

The general layout of these evaluation tests that simulate feelings, starting with physical functions, are shown in the figure below.


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Figure 3-15 DLF physical functions code, overall processes

The mechanism I use in the DLF Program to provide energy are Energy Packets (EPs); these are analogous to Adenosine Triphosphate (ATP), which is the “fuel” in biological life–forms; they are used up in some amount by every action taken by a DLF and are replenished by simulated “eating” that a DLF does by performing certain other actions. These processes will be explained in more


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detail later; for now, however, I want to focus on the simulated hunger feeling and explain how it works to cause certain DLF behavior.

Figure 3-16 The Pleasure/Pain method


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Since a DLF’s “life” depends on EPs, it must never run out. So the key to its survival is for a DLF to sense when the store of EPs is getting low. That idea led me to identify a range of values for EPs from less than 100 to more than 499; this range is the standard for the simulated “hunger” and “fullness” feelings that are set in the DLF Minds attributes for a given C.Event.

The simulated hunger or fullness feelings are simply a range of values that goes from maximum simulated hunger at -9 to maximum simulated fullness at 9. Both of these settings simulate pain so the DLF will neither eat continuously, nor will it not eat at all; other levels of EPs in between these two ranges simulate lesser degrees of pain or pleasure, with the most comfortable at a value of 2. These values are interpreted in the Action Reactor, which is a method that controls automatic behavior. This is an example of simulating what Ayn Rand calls “life as the standard of value” because the “life” of the DLF depends on having a supply of EPs.

The values that simulate hunger feelings are set in the Hunger/Fullness local by means of nine cases. The figures below show the code in cases 5, 6, and 9; the others are similar.

The DLF Mind instance comes in at the terminal at the top of the diagram and a Get is performed to get the current number of EPs from that attribute. The value is then tested against the standard for each case; if it passes the test a new value is assigned to the Hunger attribute; if it fails, next case.

Since the nine cases cover all the possibilities, some value will always be assigned to the Hunger attribute to simulate that feeling for the DLF in the current C.Event.


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Figure 3-17 Simulating hunger and fullness in a DLF


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Figure 3-18 Simulating hunger and fullness in a DLF

Once the simulated hunger value has been calculated, the DLF Mind instance is passed to the other simulated feeling locals and the simulated hunger or fullness value is passed to the Happiness local which uses it (along with all the other simulated feelings it receives) to calculate a happiness value for this C.Event. This calculation is the average of all the DLF’s simulated feelings.


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Figure 3-19 Setting the Happiness attribute

The simulated happiness feeling itself is calculated in the Calculate Feeling local as shown in the next figure.


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Figure 3-20 Calculating a simulated happiness feeling

In this way, each C.Event acquires a set of simulated feelings and an overall happiness feeling that enable a DLF to know the current status of its life by direct introspection of its state of simulated consciousness.


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Other Simulated Feelings

The other simulated feelings the DLF Program has were chosen because they seemed essential to enable a DLF to manage its simulated life and consciousness in a manner similar to the way higher animals and people do. There may be other feelings that will need to be added in the future to make the program function realistically, but only experimentation will answer that question.

The following figures show the code for the other simulated feelings; these locals all use a generalized method called DLF Mind/Feeling Calc to process the actual input vs. the standard of value for each simulated feeling to produce a new value for a given C.Event. The reasoning for each is explained in their comment fields.


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Figure 3-21 Loneliness/Company


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Figure 3-22 No Interest/Curiosity


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Figure 3-23 Boredom/Excitement


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Figure 3-24 Confusion/Clarity

Once all the simulated feeling attribute values in the DLF Mind have been set for a C.Event, the instance is sent to the action controller for further processing.

Automatic Action Selection

Before I go into the details of how actions are controlled in the DLF Program, it is important to remember that there are three types of actions that DLFs are capable of:

1. There are preprogrammed actions that the DLF performs that are either like reflexes or subconscious, automatized, learned behaviors, such as eating or basic draw routines. These actions occur by default based on the DLF Program code, or that code plus content from the DLF’s memory that sequences it based on


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successful passed actions that were stored in previous C.Events. These actions are preprogrammed so the DLF does not have to re–evolve them by recapitulating evolution.

2. There are automatically selected actions that are combinations of preprogrammed actions and data from a DLF’s memory. These actions are designed to simulate the automatic (in the biological sense) actions of life–forms, and can also include optional actions; that is, several alternative actions, that are not necessitated actions. These actions are teleological: Recall, in teleological systems, only survival actions are necessitated. (DLFs are not mechanistic, autonomous agents like the ones found in state of the art AL and AI programs.)

3. There are simulated volitional actions that are actions of the DLF Mind itself as a virtual entity (simulated mental actions); that is, internal actions of the simulated consciousness. These actions are an emergent property and cannot occur until the conceptual level of simulated consciousness is reached; only very limited optional actions are possible to a DLF at the perceptual level. Simulated volitional actions are caused by simulated consciousness of a DLF changing its own content using optional mental actions to change its own identity by manipulating symbols in its own memory. (How concepts are formed and simulated volitional action works will be explained in the next chapter.)

The Default Action Controller in the DLF Program performs the control of automatic and optional actions for the of a DLF’s perceptual consciousness simulation.


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Figure 3-25 Default Action Controller

In figure 3-25, the instance of the DLF Mind with its attributes set to the current C.Event enters through the top of the figure. The Cause an Action local causes an action by setting the Action Selected attribute to the instance of an automatic action for this C.Event, and then calls the Memory method.

Note - These methods simulate teleological causes, not mechanistic causes.


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Figure 3-26 The Cause an Action local

Inside the Cause an Action local as shown above in figure 3-26, there are two locals that cause automatic and optional actions: The Implicit Strategy Comparator and the Check & Set Action locals.

The Implicit Strategy Comparator is intended to simulate the controlled selection of automatic and optional behaviors that have evolved in humans as reflexes or have been learned as successful strategies in past C.Events. This local is also designed to allow a human working with


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the DLF Program to force the DLF to watch an action the human is going to perform in the Existence window, or to give the DLF a direct order; these latter conditions are handled by cases 2 and 3 of the local. Only the first case is directly involved in causing an action and that is shown in the next figure below.

Figure 3-27 Implicit Strategy Comparator, case 1


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In this first case of Figure 3-27, the DLF Mind instance enters, and its current feelings are extracted from its attributes. These are passed to the Implicit Strategy Processor, unless one of the flags is set and triggers case 2 or 3.

Figure 3-28 The Implicit Strategy Processor, case 1

The Short Term Memory (STM) is the memory from the last C.Event, which is made available for reference.


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Figure 3-28 above shows the first of the seven cases of the Implicit Strategy Processor local; the other six cases are shown below, but first let’s look at the Conscious Action Selection Simulator local, which has two cases of its own.

Figure 3-29 Conscious Action Selection Simulator local

In the DLF Program, actions are instances, and as may be the case in real life–forms, these instances may be defined over several C.Events. The first case in the figure above deals with the case where there is an action instance, but it is not defined sufficiently to execute. The Convert STM Content to Action local completes the action definition over one or more C.Events.


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Figure 3-30 Conscious Action Selection Simulator local, case 2

The second case of this local as shown in Figure 3-30 deals with the case where there is no action instance in STM from the last C.Event. It operates on the implicit principle that a consciousness desires to keep its feelings in the comfortable range, and that the best way to do that is to increase the value of the lowest simulated feeling. So this local searches memory for the action that was successful at increasing a given feeling in the past.

Now, let’s return to the other six cases of Figure 3-28. If any of the DLF’s simulated feelings are low enough (< -5) to indicate a simulated threat to its “life,” the Test Feelings local will succeed and call one of the following cases; if the Find Lowest Feeling local fails, that also results in the next cases, which are intended to simulate how a life–form can automatically select actions based on genetically implicit strategies, or select them optionally. Case 2:7 is shown in Figure 3-31 below.


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Note - The line of small “((((((((“marks is the way the Prograph programming language specifies the order of operations, when such specification is needed.

What each of these cases do is explained in the comments they contain.


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There are probably a few more cases that could be added, but I think these cover enough of the possibilities to enable a DLF to select reasonable repertoire of automatic and optional actions. (And to give good programmers lots of ideas for more.)

The whole point of these cases is to enable a DLF to control itself so it can survive and build a body of memories. That such memories get built is crucial to the conceptual level of the simulation of consciousness, and the causal properties that will emerge at that level.

The other code in the Cause an Action local, the Check & Set Action local, is there to prevent a DLF from endlessly repeating an action sequence (a problem known to sometimes occur with computer programs of all types and some life–forms). This local is shown in figure 3-37, and


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it simply checks the last 5 actions, and causes a random action if too much repetition is detected. Only experimentation will tell if this is necessary or a good way of preventing endless repetition of the same action, or if 5 repetitions is a good limit.

Figure 3-37 The Check & Set Action local

Once the Action Selected attribute of the current instance of the DLF Mind has been set, the instance is passed from the figure and goes to the Memory method.


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3.3.6 Memory

The Memory method of the DLF program is the simplest of the five methods that simulate the DLF Mind. At the top level, this method makes the memory in a local called “This C. Event to Persistent,” as shown in the next figure; this local sets the memory into a persistent and calls the Action method.

Figure 3-38 Memory method


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The memory itself is extracted from the C.Event in the local as shown in figure 3-39.

Figure 3-39 This C.Event to Persistent local

The Make New Memory local gets the contents of the attributes of the instance of the DLF Mind, puts them in a list, and attaches the list as the next item in the C.Event list; the C.Event list is the memory for the DLF Mind. Figure 3-40 shows the local extracting the memory.

Short Term Memory (STM) is an attribute of the DLF Mind that makes it easier for the DLF to use information from the previous C.Event, without having to search for it and recall it from the C.Event list.


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Figure 3-40 The Make New Memory local


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The Active DLF/Current Status method is a universal method that is called to update the window a human user of the DLF Program uses to monitor the status of a DLF as the program runs. It gets the window and updates its values for each C.Event as part of the Memory method and as shown in figure 3-41.

Figure 3-41 The Active DLF/Current Status

Once these functions are completed, the Action method is called so that the DLF Mind can implement the action it has selected for this C.Event.


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3.3.7 Action

This method is the next to simplest of all of the methods that make up a C.Event. This is the case because once an action instance has been defined by the Default Action Controller method, it needs only to be executed and its cost in EPs calculated to simulate energy expenditure.

The figure below shows the Action method.

Figure 3-42 The Action method

The Energy Usage local calculates the EPs and the Do it! local names and calls the appropriate action drivers to execute the action.


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Figure 3-43 The Do it! local

The Action Driver Class Hierarchy

The action drivers consist of several classes of methods that implement a DLF’s actions in the Existence window and close the causal loop with reality for each C.Event.

Note - These methods were only partially completed at the time of this writing, as indicated by the comment in the figure. I took time out from programming to write this book.


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Figure 3-44 The Action Driver Classes

The Action class itself has only one method called No- Act; this is just an empty method that is called to simulate the case where a DLF selects the option of not taking any action for a given C.Event. It is there mainly for documentation purposes.

The Say class contains the methods that enable a DLF to point, type symbols, and draw objects in the Existence window, in reality. These methods are shown in the next figure.

Note - A DLF could easily be enabled to talk as well by adding a text–to–speech method.


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Figure 3-45 The Say methods

The Type and Draw methods take the content from the action instance and use it to change the Existence window as is shown in the following two figures.


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Figure 3-46 The Type method


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Figure 3-47 The Draw method

Similarly, the other action classes contain methods that execute the appropriate action instances:

• The Ingest Class contains the Eat method.

• The Introspect Class contains the Recollect, Imagine, and SetAttributes methods. (The latter adjusts property values for Imagine, thereby changing an object’s measurements, and hence its identity, to simulate a DLF “imagining” something it has never seen before.) These actions are usually selected as parts of optional behaviors.

• The Conceptualize class contains the Conceive method, which enables a DLF to form concepts from its percepts and other earlier formed concepts by comparing the properties and values of objects and other concepts. (Keep in mind that since concept formation is not


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automatic, the Conceive method is selected as an optional mental action, and that it not the complete process of forming concepts. There are other actions required that involve additional optional behavior by the DLF that may occur over several C.Events in order for it to form a complete concept.)

• The Look class contains the Find and Watch methods, which call the No-Act method in the current design because version 1.7 of the DLF Program does not require the pointing or focusing of sensors. These methods are placeholders for future versions of the program that will use real sensors.

3.4 Simulated Perceptual Consciousness in Action

Now that I have described the C.Event cycle and explained the main methods that make up the DLF Program simulation code, it should be clearer to you how the program simulates the complex causality of goal– directed action and consciousness at the perceptual level.

It should also be clearer to you how the program is a reduction to practice of the abstract ideas that I explained in the first two chapters to set the context for this description.

3.4.1 Consciousness: The “Movie”

The simulated reality in the existence window is perceived by the current DLF, evaluated, actions are automatically selected based on the perceived content. The simulated feelings that automatically result from the


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Simulated Perceptual Consciousness in Action

evaluation of that content, the content itself, and the DLF Mind’s reaction to it is recorded in memory, and finally, the selected action is executed.

As C.Events repeat continuously, their quick repetition will integrate them to form a seamless simulation of perceptual consciousness that is similar the way the frames in a movie are integrated by our minds, and similar to what is inferred about how other types of conscious events are integrated in our minds and the minds of the higher animals.

However, just as it is impossible to know what is in the mind of another consciousness from the inside, so it is impossible to know the subjective perspective of a DLF. However, in Prograph Trace mode, it is possible to step through each method in the DLF program, and observe exactly how the DLF’s simulated conscious works from a detailed, outside perspective.

It is also important to note that the perceptual form of consciousness is passive, automatic, and time dependent; all the DLF’s actions result from the interaction of percepts of specific objects in the present (linked to a few past percepts by memory association) and its feelings; the actions are designed to keep a DLF “alive.” This form of perceptual consciousness is similar to that of higher animals such as apes, and is a level of consciousness humans share with higher animals.

Note - Remember that simulated feelings of pleasure and pain are based on survival strategies and other traits that real life–forms have developed over millions of years of genetic evolution. The DLF Program code8 is intended to


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essentially reverse engineer genetic traits that are coded in the DNA of the cells of real life– forms by copying the causal architecture of some of them, so their evolution does not have to be recapitulated by the DLFs themselves.

3.4.2 The Transition to Simulating Volitional Consciousness

Over time, a DLF’s percepts will cause simulated desires for a DLF, which in turn will cause the DLF to automatically select certain actions to cause its own future survival. As this process continues, more and more memories of its experiences will build up, memories of the DLF’s successes and failures in doing so. Assuming the DLF survives, it will learn which actions proved useful for survival in past situations that can be applied to the current one by means of simulated subconscious memory association; this will enable a DLF to improve its ability to survive using its simulated consciousness abilities.

DLFs that consistently fail to select actions that succeed in generating EPs will not survive, and their failed action sequences will be wiped out (or possibly marked as failures for future DLFs). In this way, only pro–life behaviors will be saved for use over the long–term.

After a substantial amount of experience with its simulated world has accumulated in a DLF’s memory and it has sufficient content, it will be time for a human tutor to help the DLF form concepts so it can learn to utilize the highest level causation, that of simulated volitional behavior. The memories of perceptual experiences over


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Summary

many, many C.Events are the content that will make possible the next higher level of simulated consciousness: The conceptual level.

Once a DLF reaches the conceptual level, which is the seventh and top layer of my layered model of causal complexity, it will be able to initiate its own first causes; but in order to understand how this is possible, we must first understand how DLFs form concepts and how concepts change a DLF’s identity and action capacity. That will be explained to another level of detail in the next chapter.

3.5 Summary

In this chapter, I have described and explained the essentials of how to simulate perceptual consciousness using a computer simulation system to animate a Digital Life–Form. I have described the design overview for the DLF Program version 1.7 for doing this and explained how it operates.

In a manner analogous to how biological life–forms presuppose and depend on the mechanistic causality of physics and chemistry, the simulated processes of the goal–directed, perceptual consciousness of DLFs presuppose and depend on the mechanistic causal layers of code and electronics that support their existence in a computer simulation system.

In other words, the content of simulated consciousness is made possible by its teleological causation and its supporting mechanistic computer causation, just as the


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content of biological consciousness is made possible by its supporting teleological causation, and ultimately, the mechanistic causation of physics and chemistry.

Both kinds of causation are natural phenomenon that are part of the behaviors of certain kinds of entities. The DLF Program simply uses them in a different form than they occur naturally.

And, as with biological consciousness, the perceptual content of the DLF Mind simulation will make possible the causal foundation for the simulation of the conscious functions that occur at the conceptual level.


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3.5 Summary


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4 Explaining Self–Consciousness

4.1 Introduction

Before discussing how self–consciousness works in biological life–forms and how it can be simulated, let us summarize the key ideas that have been described and explained in this book so far.

The ideas presented in this chapter are the most difficult to grasp of all those presented so far because like the idea of more than one form of causality, they are counter intuitive to what most of us learned from our education in currently accepted scientific and cultural assumptions; it is crucial, therefore, that the context be clearly set now, to help you see how these new ideas can be connected to reality. Unless you can trace through the chain of concepts I have been presenting as a foundation for your understanding, you will not be able to validate the ideas for yourself, and they will be neither objective ideas in your own mind nor become new knowledge for you.

In Chapter 1, I explained the metaphysical and epistemological basis of the ideas that are prerequisite to having the right context for being able to understand the causal nature of life in general and both the causal and


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Introduction

epistemological nature of consciousness in biological life–forms. I explained how understanding that context is itself a prerequisite to designing a computer simulation system to simulate consciousness, and that attempting to use mysticism as an epistemological basis or pretending that consciousness does not exist at all will preclude being able to design a successful simulator.

In Chapter 2, I explained how the existence of life is conditional and depends on goal–directed causation in addition to being based on mechanistic causality, and how being able to understand this crucial, but counter intuitive point depends on the objective philosophical context that was set in Chapter 1, plus the analysis of the causality of life as described and validated by Dr. Binswanger in his book The Biological Basis of Teleological Concepts.

In Chapter 3, I gave a design overview of the partially completed prototype DLF Program that I have written and explained how it simulates goal–directed behavior, sense perception, pleasure/pain, automatic action selection, memory, and action in a simulated reality. I explained how perceptual consciousness emerges from goal– directed causation as a property of some life–forms that have evolved the capacity because of the survival value that capacity provides to those life–forms, namely to be able to perceive the objects in reality around them in order to find food and avoid dangers. I also introduced the idea of a conceptual level of consciousness emerging from the perceptual level.

In the context of simulating consciousness using a computer system as an animation platform, I explained how the DLF Program design provides a similar simulated ability for a DLF to sense reality, perceive


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Explaining Self–Consciousness

objects in reality, identify those objects, and store the resulting information in its memory in the form of simulated percepts for use in the future; further, I explained how such simulated percepts are the content of the DLF’s simulated consciousness; they are the form in which DLFs perceive reality, just as real percepts are the form in which biological life–forms perceive reality.

In this chapter, I describe and explain in detail how perceptual content in the consciousness of human beings is further processed into data structures called concepts and how that unique ability enables human beings to become self–aware and self–programming.

I describe how a properly designed simulator can mimic these capacities to some degree, and how a special type of concept called an “implicit axiomatic concept1” can enable simulated self–consciousness, simulated volition, and simulated natural language to emerge as properties of the DLF Mind (a virtual, teleological entity), just as the biological forms of those conscious processes do in the minds of children.

Finally, I show how the use of simulated concepts, volition, and natural language can emerge in a DLF Mind as it interacts with the world, including its interaction with human teachers; these capacities will emerge in a spiral2 fashion that enables ever-wider contexts to come into a DLF’s processes of simulated awareness.


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The Emergence of Simulated Conceptual Consciousness

4.2 The Emergence of Simulated Conceptual Consciousness

Given the perceptual level of consciousness, concepts and the use of the conceptual capacity is what makes possible the human mind’s properties of self–consciousness, volition, and natural language. These capacities are all part of the human rational faculty and are inter–related.

Before discussing how DLFs form simulated concepts to enable simulations of human–like rational capacities to emerge as new properties in the virtual entity I call the DLF Mind, it is necessary to explain where concepts fit into the layered model that describes the architecture of DLF Simulation Technology, more about what concepts are, why concepts are needed at all, how they emerge from the perceptual level of consciousness (both real and simulated), and finally, the cognitive power that concepts give human consciousness, which the DLF Mind can mimic. This power is a direct result of the properties of concepts and the new power those properties bring to conscious processes; it is cognitive power that is not possible with percepts alone.

As with the idea of complex causality, a clear understanding of how concepts are formed by Ayn Rand’s method is not widely known in the scientific community; there are many books on concepts available, but most of them accept the premise that concept formation is ultimately either based on intuition (divine or human) or subjectivism (nominalism). Neither of these views can result in concepts that are objective . Nor are these views useful in designing a consciousness simulator because they depend on outside intervention by humans precisely because they are non-objective; traditional approaches to


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concept formation thereby undermine the whole idea of a simulated consciousness that is more independent of human control than state of the art technology allows.

The new ideas Ayn Rand identified about concepts and their formation provide the basis for a clear, scientific understanding how self–consciousness, volition, and natural language are possible, for explaining how these capacities work, and for explaining how they can develop in the consciousness of a child. Then, building on that foundation, I will explain how that development process can be mimicked and extended to the simulated consciousness of DLFs by giving them conceptual capacities similar to those that humans have by using new data structures that simulate the properties of concepts.

4.2.1 Topping Off the Layered Model

In Chapter 2, I explained how a system to simulate consciousness can be thought of in terms of a layered model like the ones often used to describe computer systems and computer network systems. I also explained how such layered models are useful to understand how properties can emerge from different contexts (layers of subsystems) that, while causally dependent, are explained using logically independent terms (such as the different terms used for explaining the electronic properties of the components of a computer’s hardware such as capacitance or resistance that make digital processes possible, as opposed to the software terms used to explain the software properties that emerge from the binary arithmetic of its operating system or the higher level languages that run its application programs). I explained how in biological life–forms, vegetative and higher forms of goal–directed behavior emerges from the mechanistic


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processes of physics and bio–chemistry of the lower layers of the model contained in the left side of Table 4-1, a model that shows subsystem layering for living systems.

Note - On the biological side, little is known about exactly where to draw the lines for mechanistic and goal–directed behavior layers because the biology of living cells is not yet completely understood. In this book, my main focus is on the computer side of the model, the layers of which can be clearly identified.

Remember that one of the things that makes layered models useful as an aid to understanding complex systems is the fact that they are modular and that different lower layers can be substituted in some cases without changing the functionality of the higher layers.

Biological Life–forms Digital Life–forms

Layer 7 Conceptual Consciousness (Reason) Simulated Conceptual Consciousness

Layer 6 Perceptual Consciousness Simulated Perceptual Consciousness

Layer 5 Goal–directed Behavior Simulated Goal–directed Behavior

Layer 4 Mechanistic Cellular Processes DLF Program

Layer 3 RNA, Protein, ATP Synthesis Prograph Programming Environment

Layer 2 DNA Processes Computer Operating System

Layer 1 Electro–chemical, Physical Processes Computer Hardware

Table 4-1 A Layered Model of Complex Causality

In the case of DLF Simulation Technology, my strategy is to substitute the mechanistic causality of electro–chemical functioning and the mechanistic, physical aspects of the body of a life–form (the physical layers on which biology


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depends), with a computer simulation system, while maintaining the teleological causality of the upper layers in the form of logical operations. The mechanistic functions that simulate the mechanistic aspects of biology are represented in the layers one through four on the DLF side of the model as shown in Table 4-1, and these serve as an animation “platform” for the logical operations that simulate the teleology of higher life–form functions.

Note - Someday it may be possible through the use of nanotechnology to use nano–scale machines for layers one through four on the right side of the model instead of computer technology. The result would probably be a better animation platform and more life–like simulations.

The simulation of higher life–form functions is the simulation of the functionality of goal-directed behavior, perceptual consciousness, and conceptual consciousness as closely as is possible given available computer technology. This is done in layers five through seven; some of these capacities will be partially programmed and some will emerge as part of a virtual entity during the operation of the system as its memory becomes organized by its perceptual and conceptual content, and then is recursively processed, thereby changing the memory’s identity for subsequent events of simulated consciousness.

I have described how in the case of the DLF program, the computer hardware is the first layer of the model, the computer operating system the second layer, the Prograph programming environment the third layer, and the DLF Program the fourth layer. I have also explained that the fifth, sixth, and seventh layers are for life–like simulation


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of goal–directed behavior, purposeful, perceptually conscious behavior, and conceptually conscious, volitional behavior, respectively, on the part of DLFs.

Layers one through six were explained in Chapter 3. It is now time to explain layer seven, from which simulated volitional behavior, including concept formation, self– consciousness, and natural language emerge as new properties of the consciousness simulation system.

Note - It is important to be clear that I am not equating the processes on the left and right sides of Table 4-1, but merely drawing an analogy. The idea is to show the approximate relationship of the contextual layers from a functional stand–point in the two types of systems as an aid to understanding them. However, the DLF simulation must duplicate the causality of each layer if it is to function.

In biological life–forms, volitional behavior is unique to human beings. Free will depends on there being a “self” to will choices; by definition, full volitional behavior cannot be automatic or mechanistically necessitated3.

“Volition” (free will) is a difficult concept for most people to understand because it is not clearly defined in our culture, either scientifically or otherwise: Science has tried to replace volition with either the deterministic necessity of mechanistic causality or statistically driven random chance; non-scientific people think of volition as indeterministic mysticism (miracles) or uncaused, random chance.4 But none of these views is correct: Choices made by a conceptual consciousness are neither necessitated


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nor uncaused; such choices are caused by the prior cycles of mental processes and life processes of the life–form with conceptual consciousness that makes them; that is, choices are caused by the processes in the lower layers of the model shown in Table 4-1 that make the life–form possible, by the nature of concepts, and by the functioning of the life–forms conscious processes.

Given the identity of life–forms, there are two types of freedom available to them that are not available to inanimate objects:

• Optional Actions:

In life–forms, only survival actions are necessitated. This is different from the situation with non–living objects, where all actions are necessitated. Optional actions can be executed by life–forms either externally or internally, physically or mentally, provided all their survival needs are met for some period of time.

• Optional Mental Actions:

Symbol systems are built in the memory of some life–forms using optional mental actions to effect certain changes to their memory.

The fact that symbol systems are logical, as opposed to being physical , means they obey different laws of operation than physical systems, and once created in memory, they provide different action capacities to the life–forms that possess them, as opposed to those that do not. The difference between logical and physical systems is that the former consist of symbols in and under the control of a mind, and the latter consist of real, physical


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objects in reality. These two kinds of entities have very different action capacities because of their very different kinds of identities.

The nature of optional actions in general, and optional mental actions in particular, have important implications in the explanation of the causation of volitional action.

However, in order to understand specifically how these causes operate in conscious life–forms, it is necessary to distinguish the fully developed volitional capacity of an adult from the developing one of a child because, as is the case with most life processes, the capacity does not attain its full range of control all at once. Adult human beings are able to plan and act almost independently of near– term input from the mechanistic, necessitated, causality of the real world; adults can largely ignore near–term stimuli and act for the long–term. The behavior of infants, on the other hand, is almost completely near–term, reflexive, and automatic 5; and while automatic goal–directed behavior is not mechanistic, it does have a very narrow latitude of alternatives, making long–term behavior impossible to an infant. How does the former capacity develop from the latter?

The answer to this question is that it develops in small steps as an infant and then a child changes its own identity (and hence action capacity) as its mind matures and forms concepts, and then combines the concepts into thoughts (sentences) that identify reality and itself. While it is true that concepts are volitional and a fully functional volition requires concepts in order to reach the range of control of an adult human, both properties emerge


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simultaneously; they are inter–dependent; one is not the complete cause of the fully developed form of other. Remember, teleological causation is a spiral.

Free will choices are caused, but not in the mechanistic sense of causation by some single prior event at the same level in the logical structure of the system (events are not causes); volition is caused by the identity of life–forms; specifically it is caused by the conditional nature of life, by the nature of goal–directed behavior, and by the content of the perceptual level of consciousness. Volition is caused by all of these causes in combination and by the range of control the identity of concepts adds to consciousness; volition emerges gradually with increasing power as concepts multiply.

However, the fact that volitional actions are caused, does not mean they are necessitated in the traditional sense. That erroneous implication results from the mistaken, “billiard ball” view of causality that posits events as causes.

Inanimate objects in the world outside a consciousness operate based on mechanistic causality only, and every cause and effect sequence is necessitated by the identities of the acting entities. Every sequence is necessitated because a non–living object’s identity is what it is (A is A), and it is its own action capacity; to act differently, the object would have to be something that it is not! An non– living object’s actions, all of its actions, are necessitated by what it is, period. That is mechanistic causality: “...causality is identity in action...,” as Ayn Rand observed.


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This fact is also true for life–forms. But the identity of life–forms is different from that of non–living objects and, therefore, so is their action capacity; remember, life–forms are conditional, and that fact means they are also teleological, and that fact leads to their capacity for optional actions, or actions which are caused but not necessitated in the mechanistic sense.

However, before explaining how optional actions work in more detail, it is important to learn a little more about concepts, because concepts are not only caused by optional mental actions, but they are also the aspect of the identity of consciousness that makes possible the full range of power of volition in adult humans.

The percepts of life–forms automatically and accurately convey identity information about reality into a conscious system.

Note - The fact that we perceive living objects as well as inanimate objects is irrelevant to the point I am making here.

Concepts, as formed by Ayn Rand’s method, tie together multiple perceptual events, multiple perceptions of particular instances of objects, their actions, their properties, and their relationships in a timeless manner that is independent of any particular object, event, or situation (though all those specifics are what the concepts mean); concepts can do this because they are formed by the comparison of the measurements of objects and other concepts, and they are defined by ranges of those measurements, thus linking the particulars together as the units of groups of two or more similar members, all of


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which are symbolized by a natural language word, and eventually defined by a natural language sentence (a definition).

While concepts, and the words that symbolize them, get their meaning from the chain of definitions and their other content that connects them to reality, they are a form of symbolic information; they are not just their content, but also data structures that are mental entities in their own right, and therefore part of the identity of consciousness. Concepts, when focused on one at a time, serve to break up, to separate events and the objects they contain from their surrounding context so they can be studied separately by consciousness.

A conceptual consciousness can be conscious of certain aspects of the identities of these objects separately, without the constraints of any single object, event, or percept, and most importantly, without the constraints of mechanistic causality. Thus, while perceptual consciousness is awareness of specific objects (a particular rock rolling down a particular hill at a specific rate of speed), conceptual consciousness is the awareness of categories or groups of objects, their attributes, and their relationships (rocks, hills, gravity, the action of rolling, and speed) as symbolic, abstract ideas. These abstract ideas (concepts) are mental structures (analogous to (but different from) data structures in computers); they are structures that can be manipulated independently of the objects and other entities that they mean in reality. Natural language integrates concepts into symbolic representations of specific aspects of reality, “scenarios” in reality, and general facts about reality as a whole.


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In other words, while the connection to reality of physical objects is physical (they are in reality), the connection to reality for words and sentences is meaning, and meaning is not physical, but rather a chain of logic that can be changed or broken by optional mental actions.

In this context, concepts are epistemological structures, not metaphysical ones, and therefore they are not subject to the same laws of reality as objects or percepts of objects. Symbols have a different identity from physical objects, and they therefore have different action capacities, just like a computer simulated airplane and a real one do. Symbols have a different metaphysical status than percepts or real objects. Symbols are subject to logical and teleological laws, not physical laws.

If this idea is not clear to you, consider why corporations spend millions to simulate new products on computers before they actually build them. Though those simulations are different in nature from the simulation of concepts and consciousness, a similar principle is operating.

The point is, that while concepts and natural language connect human knowledge to reality through meaning, the symbolic nature of concepts and natural language separates human knowledge from the physical objects of reality and from the laws of mechanistic causality in a similar way to how computer simulations separate simulated airplanes and car designs from the consequences of the laws of physics; if a simulated airplane “crashes” because of a design flaw, no one dies, and it is much cheaper to correct the logical mistake in a computer program than the damage that occurs if a real airplane crashes. Nothing gets destroyed; all that happens is that a program crashes or produces wrong answers.


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Physical laws and “digital” laws are not the same because the objects involved have different identities, and therefore do not have the same consequences in reality; one does not invalidate the other, they are just different. The identity of one is real objects and the identity of the other is symbols that represent real objects or other symbols. The identity of the entities involved enable that separation to be made; they are metaphysically different, and they have different action capacities as a result.

Simulations of airplanes are great for testing designs; if a simulated plane crashes, the only costs are the redesign of the plane, reprogramming the simulation to run another test, and the computer time to do so. However, simulated airplanes don’t fly passengers and cargo very well; real planes are needed to do that...different identities, different action capacities.

By analogy, concepts depend on a human brain and percepts just as a computer simulation of an airplane depends on computer hardware and the appropriate simulation data, but a conceptual system is a teleological, symbolic system, not a mechanistic, physical one. That difference is what makes human imagination possible: People can imagine all sorts of physically impossible things because optional mental actions are not necessitated, and symbols are not constrained by physical laws.

Concepts formed by Ayn Rand’s method plus natural human language result in a reality based symbol system inside human consciousness, and that symbol system is the basis of both an increasingly large range of control of volition and its own future development.


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In order for volition to emerge in a human consciousness (or a DLF’s simulated consciousness), there must be the lower level processes of a biological life–form or digital life–form to cause it. In other words, the life–form must be alive and conscious at the perceptual level, and it is that identity that ultimately causes volitional behavior.

Let us review: Every event in reality has a cause, but events are not causes themselves, entities are. The process of causes and effects is the interaction of the identities of the objects or other entities involved. Remember, the identity of an entity determines its action capacity. In addition, there are two kinds of causality: Mechanistic causality and teleological causality, with the second kind presupposing the first.

In the case of life–forms, whether biological or digital simulations of them, their identities and corresponding causes exist in layers of increasing complexity. At root is the simple mechanistic causality of unconditionally existing objects, the form of causality which makes life processes possible.

Life operates conditionally by the more complex form of causality of goal–directed behavior (teleology), which, if successful at attaining its goals, causes life to survive as a process and causes life’s own continued existence. Once survival strategies become reliable, more and more resources that life requires can be accumulated. Once sufficient resources are available, life–forms can engage in optional actions because the resources that are in excess of those needed for basic survival buy them the time and the freedom to do so.


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Recall, only survival actions are necessitated in a teleological system, as opposed to a mechanistic system, in which all actions are necessitated.

Essentially the same principle is operating here as the one that allows companies to grow in the business world: Like life–forms, the existence of a business is conditional. A start–up company has few resources and everything it does is necessitated by its need to earn profits to survive. Once successful and flush with cash, a company can engage in optional actions, such as looking for competitors to buy out or new lines of business to develop. It can do so because by its own previous success, it has bought itself the time and resources to engage in actions which are not necessitated by its immediate need to survive as a financial entity; the actions are not necessitated because the company’s immediate survival needs are already met.

Optional actions are actions of a life–form that are not necessitated by its survival. They are selected automatically (caused in the teleological sense) from a few possible alternatives that may be available to the life– form’s self–regulation system. Most life–forms with perceptual consciousness have a very narrow range of control because nearly all their optional actions are physical; their optional mental actions are extremely limited to things such as recalling a few memories or perhaps forming a mental picture of something. And, they have no power other than to select one or another of the available action alternatives at any given time. They cannot change their optional actions or look for different alternatives. They can only select one to execute.


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In other words, life–forms with perceptual consciousness have a very limited ability of self–modification, a very narrow range of control. However, using optional mental actions, specifically the choice to focus the mind, human perceptual consciousness can operate on itself much more extensively by comparing and modifying its own memories to cause the formation of concepts and a learn natural language.

The concepts and language that result from using optional mental actions change the identity of human consciousness and thereby enable that consciousness to cause an ever wider range of free will choices as concepts multiply over a period of time. This is how volition “bootstraps” itself to its full range of control, as observed in adult humans.

Volition is possible because the identity, the causal nature of goal–directed behavior makes both consciousness and optional actions possible for some life–forms. Optional actions affect reality physically when they are selected by a life–form. If directed externally, optional actions cause physical changes to something outside the acting life– form. But internally directed optional actions cause changes to a life–form’s own memory and are mental actions that change its identity, which means the action capacity of that life–form’s consciousness.

Note - Some optional actions can be self destructive. These are the actions that death eliminates from the identities of life–forms over the long– term.


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Conceptual processing can give volition a wider range of control over a period of time. In humans, the choice to focus the mind is the optional mental action that leads to concept formation and conceptual processing; optional mental actions produce knowledge in the form of words and natural language sentences, and natural language is information about reality in symbolic form. This means it is information that is connected to reality through meaning instead of by physical causation. As mentioned above, symbols are different from physical objects; they have a different identity from physical reality, and that difference means they have a different action capacity.

The identity of life makes possible the existence of optional actions, one of which is the mental action of focus. Mental focus is a means by which a human can cause changes to its own memory and leads to the formation of concepts and a whole range of other actions; these other actions are the mental actions of building and manipulating a symbol system in memory. The existence of a symbol system in memory enables the ability of human consciousness to initiate a first cause.

A first cause is an optional physical action that is defined by a symbol system in a mind; that is, it is an optional physical action initiated by a human consciousness that executes an action definition that was symbolically defined in previous conscious events using optional mental actions. It is considered a first cause because its source is internal to a life–form, not some outside entity.

Once the conceptual level of consciousness is reached, optional mental actions can be used to compare some of a human mind’s percepts or change some aspect of a symbol system stored in memory such as a word, a


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sentence, an equation, a fantasy or some other form. Symbol systems can also be used formulate words or sentences in various ways to define new physical actions, rather than being limited by the specific, fixed action alternatives available to a simpler perceptual consciousness. Since symbols are connected to reality by meaning instead of a physical connection like an optional physical action is, an action that is formulated as a word or a sentence is not physically connected to reality, but logically connected instead (until it is executed), and that makes it analogous to a computer simulation. Such mental simulations can be manipulated in similar ways as computer simulations are and for similar purposes.

Therefore, a human being who, by means of an optional mental action, chooses to compose the sentence: “I will roll a rock down the hill.” in one conscious event and then does so in another conscious event has initiated both an optional mental action and a first cause. The person has composed a sentence to define an action symbolically (the optional mental action), and caused a rock to roll down the hill, the first cause (which is an optional physical action). Neither the act of composing the sentence nor the act of rolling the rock are necessitated by mechanistic causality.

The action of composing the sentence is an optional mental action (that is possible for a life–form if survival needs are met), and therefore, it is not necessitated mechanistically (though caused teleologically). The sentence itself is a first cause (when executed as an optional physical action) since it begins a new chain of cause and effect in reality; the sentence is entirely caused in the mind of the human assembling its symbols by the optional mental action; the sentence is a self–generated,


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self–regulated action, and its only connection to reality is by the logic of its meaning, until it is executed.

Moreover, if the action is executed, and if its logic is flawed, the outcome in reality will differ from the meaning of the sentence. This fact proves the connection is only one of meaning. Contradictions can exist only in the meaning of sentences, not in reality.

The actions of composing the sentence and rolling the rock are not necessitated mechanistically because a human being is not mechanistic; they are not necessitated teleologically because they are both optional actions. In addition, the sentence is a first cause because its only connection to reality is through logic, through its meaning, yet once it is executed, it becomes the first cause in a new chain of cause and effect.

The word “first” in “first cause” does not mean the action is uncaused. It simply means it is not caused by an entity outside a life–form. It is the beginning of a new causal chain at its own level in a hierarchy of actions, but it is still caused by consciousness and the levels in the hierarchy of other causes below it: First causes are caused by consciousness, which is caused by the goal–directed behavior of life–forms, which is caused by the mechanistic causation of physics and chemistry. There are no uncaused causes.

Strictly speaking, volition itself is the act to focus one’s mind. But the term can also refer to the range of control or range of alternatives, of choices available.

The range of control of volition emerges gradually because the source of that range is concepts (as a symbol system), and concepts multiply slowly as a child acquires


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more and more knowledge; concepts can neither be formed automatically nor all at once, so they increase in number gradually.

When a child chooses to use an optional mental action to form a concept he changes his mind’s identity, just as when he chooses to move his arm, he changes his body’s identity. Once a new concept exists, it confers a small amount of new causative power to the child, because having a new concept, he has new information that he did not have before; his identity is changed and hence his action capacity is increased.

So not only is volition caused, but like life itself, volition is caused by previous instances of its own use: Each concept a child forms increases action capacity (causative power) and buys its consciousness another degree of freedom from the constraints of the perceptual level of consciousness. This is another example of complex, cyclic causality that spirals into the future: The use of volition with a small range of control that a child has to begin forming concepts (using optional mental actions), results in the child having more concepts; the new concepts provide the child knowledge of new aspects of reality and more future range of control to use his volition; he can initiate more first causes, some of which lead to more new concepts, which result in a still wider range of control to use his volition, and so on, until the child becomes an adult and is in full volitional control of his mind. (Obviously, not all children choose to form valid concepts, so not all adults attain full volitional control of their minds.)


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The ultimate result is a form of cognitive self–regulation in which consciousness has the identity and corresponding action capacity to be capable of initiating an enormous range of first causes.6

Now that I have outlined the basis for how volition leads to first causes in life–forms which have a conceptual consciousness, I can explain in more detail exactly how it emerges. But first, I need to explain more about the nature of concepts because it is the properties of concepts that make the full power of volition’s emergence possible.

4.2.2 The Survival Value of Concepts

Individual percepts are all unique, and as such are only of limited usefulness to a biological life–form or a DLF in its struggle to survive; a given percept can cause a DLF to see an object in reality, feel simulated pleasure or pain, and to act in a certain way, but the information the percept contains is very specific to the event and time dependent.

In the context of survival value, percepts stored in memory are useful, but only if they can be remembered; and at some point, the search times to find a particular percept in memory and the mental calculations necessary to determine its usefulness to a current survival problem would become too great to perform in a timely manner. There are simply too many instances, too many units of information, and the limited capacity of any cognitive system would at some point be overloaded.

It is for this very reason that simulated sensations, the X,Y points in the DLF program’s Existence window, for example, are integrated into objects with properties and


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values. A list of hundreds or thousands of X,Y coordinates is integrated into a single object instance that consists of a short list of properties and values, the values being measurements of the properties made by using an appropriate measurement standard. Using this approach means there are fewer units to process and is an example of processing unit economy at the perceptual level. Unit economy translates into efficiency of awareness, which translates into efficiency of action, and that has survival value to life–forms.

A DLF’s ability to identify its simulated environment and act to maintain its supply of Energy Packets (EPs) would be much more difficult in its simulated world and impossible in the real one if all it could see were those long lists of X,Y points. The fact that the points are integrated into objects that are on the human/animal scale of consciousness that a DLF is designed to simulate, greatly reduces the number of units that need to be processed in order to take survival action; instead of thousands of X,Y points, all the DLF has to deal with is a short list of properties and values for each object it perceives. For a biological life–form such as a small animal, the equivalent situation would be that instead of seeing a patch of pin–points of light of various colors (sensations), the animal is perceptually conscious of objects instead, such as a piece of food or a tiger.

Note - While biological life–forms do not use X,Y coordinates, their visual systems do consist of arrays of millions of individual neural sensors in their retinas, which amounts to a similar situation as sensing X,Y points. The output of


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these sensors are integrated automatically into objects by perceptual consciousness so they can be identified more easily.

To survive, both biological life–forms and DLFs need a way to reduce the units of information in their consciousness to a cognitively manageable level. Percepts provide one level of unit economy; concepts provide many more levels of unit economy. That is the main cognitive function of concepts and the source of their survival value7.

Note - Let me make it clear that by “concepts” I mean only those that result from the concept formation process defined by Ayn Rand in: Introduction to Objectivist Epistemology (See Appendix A). Words defined arbitrarily and at random by intuition and subjectivism are not concepts.

Concepts make it possible for consciousness to identify and take survival actions in response not only to vast numbers of concretes with fewer conscious units to process, but also to be aware of things like relationships which have no material existence at all. (I introduced this idea in the previous chapters, but now perhaps you can see it in its full context.)

This situation also explains why concepts must be formed volitionally, not automatically. Concepts can contain virtually anything, and they can be multiplied endlessly, so their formation must be selective, or they would soon defeat their own purpose of processing unit economy and overwhelm limited cognitive capacities of any


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consciousness capable of forming them. From the point of view of biology, the most important selection criteria is to form concepts which have survival value: That means concepts about reality and method because those kinds of concepts lead to action efficiency, which leads to better survival odds.

Note - Concepts about fantasy worlds can also have survival value, but that is not relevant to the topic at hand.

Concepts are an open–ended data structure that are symbolized by words and used by the human mind as one of its means of awareness; concepts can contain and stand for a potentially unlimited number of units of information of any kind, and result in knowledge that is stored in a way that is not time dependent; concepts can contain information about percepts, relationships, or other concepts, all to many levels of abstraction.

Concepts are symbolized by the words of natural human languages, and words are man–made perceptual data objects that consciousness processes as symbols that represent the vast amount of information concepts contain. Because of the open–ended quality of concepts, one single word can stand for all objects of a given type that have existed in the past, exist now, or will exist in the future. The word “man,” for example, through the concept it symbolizes, stands for every man that ever was, is, or ever will be; the word “universe” refers to every physical existent and certain relationships between them that are contained in the universe along with objects, such as the places objects exist and time; the axiomatic concept “existence” refers to the state of being of absolutely


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everything of any kind that has, is, or ever will exist throughout all eternity, and that is a lot of information; it is more information than any mind could ever hold as separate pieces.

Note that concepts are timeless, unlike percepts which are time dependent. A percept contains information about the identity of an object the instant it is perceived, whereas a concept refers to any object or relationship or other concept of a given type at any time. This is an important way in which concepts are different from both the goal– directed causality of percepts and the laws of mechanistic causality that govern inanimate objects.

In addition to unit economy and timelessness, another important aspect of concepts is that they are formed volitionally by choices made by consciousness. This means in effect, that a conceptual consciousness is an active and fallible process, as differentiated from perceptual consciousness, which is a passive, automatic, and infallible process. A conceptual consciousness actively defines concepts, which means it modifies part of its own memory structure; this is another way of saying that, over time, it sets its own identity and, therefore, its own action capacity, within the limits set by reality. You could think of perceptual consciousness as reactive, and conceptual consciousness as proactive.

Concepts must be connected to reality, however, or they become subjective fantasies, and this is why intuition and subjectivism do not work as a means to form concepts. Making a connection to reality requires very little effort, and is as simple as looking at the world with one’s eyes and comparing a few memories to form first level concepts such as the concept of “rock” or “tree.” But to


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form and validate abstract concepts such as “vegetation” or “biological,” it requires much more effort because it is necessary to compare many, many memories of percepts and other concepts, and to prove their content by reducing them back down the conceptual hierarchy to the percepts from which they were originated.8 Not to do so would be precisely equivalent to writing computer code and never “proving” it by running it on a computer (which reduces the code to practice and validates it), but just saying it works anyway without testing it. (Anyone who has ever written even a simple computer program knows how silly that idea is...)

Concepts, that have been validated by testing their connection to reality, increase the power of consciousness by many orders of magnitude by providing unit economy, certainty of fact, and enabling a human being to set its own action potential in complex ways.

Simulating concept formation is, therefore, an essential feature of the simulation of consciousness and volition at the human level.

4.2.3 How DLFs Form Concepts

In this section, I will explain in general terms how DLFs form concepts, while the specifics of the process will be covered in more detail the next chapter. (How humans form concepts is explained in detail in the references listed in Appendix A, specifically: Introduction to Objectivist Epistemology.)

Concepts are formed, whether by humans or DLFs, by differentiating a group of two or more objects, other existents, such as relationships, or other concepts on the


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basis of one or more shared properties. The differentiated entities are then viewed by the concept former in a special way, by regarding them as content, as units or members of a group of two or more similar members, as Ayn Rand has explained. This is done by comparing their instances in memory and observing that the values associated with the entities’ shared properties are specific values of a range of values that those properties typically have in reality, and then using that shared range of values as an objective basis to integrate them as units of the group. The concept formation process is completed by symbolizing the concept with a word, by allowing the word to refer to the entire range of values to integrate all the units that share a particular range of measurement values for certain properties into a single concrete. 9 The single concrete (the word) “stands for” or symbolizes the units (including all the rest of their properties and values), thereby conferring unit economy to the consciousness because now it has to process only a single symbol instead of a whole bunch of separate percepts.

For example, to implement this process in the DLF Program, a DLF forms a concept over several C.Event cycles. The DLF might perceive in the Existence window several shapes such as a circle, a rectangle, and two or three triangles of different types and sizes. The Conceive method, which can be selected as an optional mental action, enables a DLF to differentiate the triangles from the other shapes on the basis of comparisons of their shared properties of three straight lines with end point intersections by comparing the currently perceived shapes as well as other examples in the DLF’s memory.


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Next, the DLF (using the Conceive method) recognizes that the object property values of the three straight lines and their intersections are part of a range of possible values for this configuration, omits the particular property value measurements of the triangles being compared, but retains the properties themselves and the acceptable measurement range within the entire context of the DLF’s knowledge at that point; the new units are thereby included in the range of measurements that are typical for triangles.

Note - The fact that the comparison of properties is made means that concept is made “within the entire context of the DLF’s knowledge at that point” means that concepts are contextual. As a DLF’s knowledge increases, concepts will need to be updated and redefined to accommodate new information.10

Finally, the DLF asks or is told by a human tutor monitoring this process what the word is to designate the units for the concept, which in this case is obviously “triangle.”

From that point on, the word “triangle” in the DLF’s simulated consciousness is a single percept that stands for any triangle the DLF perceived in the past, perceives now, or at any time in the future. This state of affairs is possible because the properties and measurement range retained for these units during the concept formation process provide an objective, ostensive definition that amounts to a kind of “detector” for all triangle units.


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All new percepts passed by this detector will either register as triangles or not depending on whether their properties and values are in the appropriate measurement range or not. So for example, if the concept “triangle” had been formed based on observations of isosceles and equilateral triangles only, a scalene triangle the DLF perceived after the concept was formed would also be recognized as a triangle because it would fall into the appropriate measurement range of “three straight lines connected at their end points;” the fact that the scalene triangle had never been perceived before is irrelevant. The same is true in principle for concepts of other types of objects, properties, relationships, and so on, and I will work through a few more specific examples of these in Chapter 5.

The concept formation process is fairly simple for concepts that are not very abstract, and if concept formation were an automatic process, a DLF could endlessly form concepts of all kinds of things, and of other earlier formed concepts. This is in effect what happens as children form first level concepts of objects and their actions because it is so easy for children to form them; their units are so prevalent, that these concepts are formed with very little effort, almost automatically because they require very little mental focus; but because at least some conscious effort is required to focus their minds on the content of the concepts, children must choose to make that effort and select the mental actions to form these concepts.

So concepts must be formed by simulated “choice” even by a DLF if a consciousness simulator is to behave like a biological life–form; that is, the DLF must use simulated volition or a simulated act of “will” to select an optional


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mental action and expend its Energy Packets (simulated effort) to select and form only those concepts that are cognitively necessary or useful to a DLF’s own survival. Remember, the point of forming concepts at all (like nearly everything else in biology) is their survival value; defining concepts changes a life–form’s identity and hence its causal potential: The best use of cognitive resources is that most of the concepts formed must be those that maximize the survival value, that give causal potential to actions that cause survival.

Such a design will make the DLF mind more human-like than animal-like. However, since the whole point of the DLF program is to simulate a human type of consciousness, to succeed at this goal, a DLF will have to form most of the 50-100,000 odd concepts most humans use as part of their knowledge of natural language; doing this will only be possible with the help of a human tutor because without one, the DLF would have to literally reinvent all of the cognitive discoveries in human history that gave rise to those concepts and language in the first place.

4.2.4 How Conceptual Level Consciousness Emerges

I described briefly above how the conceptual level of consciousness is caused by and emerges from the perceptual level of consciousness. Now that I have explained, in addition, how concepts are formed, we can consider more specifically how this emergence occurs.

It is well known in the study of psychology that human consciousness is able to distinguish objects and their properties from a background of other objects; there have been numerous experiments conducted concerning the


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capacity of human and higher animal subjects to differentiate and perceive figure–ground images; these experiments have offered many clues as to how consciousness processes real world scenes to differentiate some foreground objects from a background of other objects. Figure–ground processing ultimately results in the content of consciousness, in the form of percepts, being stored in memory for human beings.

It is also well known in psychology that human minds have two parts: They have a conscious part and a subconscious part. In humans, the subconscious is a part of the mind which evolved a capacity to compare objects and identify the similarities and differences between them. It does this comparison work automatically: If you doubt this, look at any two objects and your subconscious will instantly tell you if they are the same, different, or similar.11

Note - Volition controls the choice to focus on some specific bits of content or subject matter to get this process to compare it, but the actual comparison processing is subconscious and automatic, and the choice to direct perception to specific content on a continuous basis requires a fully conceptual consciousness.

So if the data are available, they get compared and sent up to the conscious mind from the subconscious; if a human being is awake, perception will provide that data constantly. The survival value of such a comparison capacity is obvious: For example, once a person is stung by a bee, seeing yellow jackets or wasps, which look


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quite similar from a distance, would recall painful memories and lead to avoidance of a potentially dangerous situation.

Note - Animals seem to be able to associate painful memories, make some comparisons to avoid similar situations for survival purposes, but apparently lack the capacity to purposefully direct a comparison process like humans can.

After a while, the results of large numbers of comparisons build up in a child’s memory along with various associations to pleasure/pain feelings and other percepts; the child’s memory becomes organized according to the similarities and differences of the objects that have been observed. But collections of similar percepts, associated but passively held in memory is not yet a conceptual capacity; these data are simply the implicit basis for one.

In other words, the first two steps of the emergence of the conceptual level of consciousness are:

• First, the observation of objects results in their identities being stored in the memory of a conscious system in the form of percepts, thus changing the identity of memory and the identity of the life–form possessing the memory containing the information.

• Second, automatic comparison processing of those memories to note similarities in commensurable properties of the objects derives more information from the original percepts, information that is linked to them by association, but information that is still passively held until it can be consciously processed further at a later time.


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Before the conceptual level can emerge, two additional steps are required:

• Third, objects that are similar must be noted as units (that is, members of a group of two or more similar members12),

• Fourth, the units must be symbolized by a word.13

(Eventually, they will also get a verbal definition.)

The last two steps are not passive and automatic, but active behaviors on the part of the conscious system which require mental effort: The consciousness must choose to remember the similar objects as a group of units rather than individual objects, and it must think of (or acquire from someone else) another percept to use as a symbol to represent the group. These are both volitional behaviors.

Note - In a fully developed human consciousness, there could be other steps prior to the first step that may also be volitional: The choice of what to look at, where to direct one’s perception, and various methods for doing so. But in a small child, what is perceived is very likely to be automatic because the child may not yet have learned to be proficient at directing his perception.

For primitive men, the last two steps must have been a difficult challenge because no words or other symbols existed until some genius invented them, but for modern children the last two steps are quite easy: The parents of such children are constantly naming objects for them!


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The child, in a culture that uses natural language, merely has to associate the words provided by parents with the named referents, which are then remembered visually in most cases; doing so amplifies the automatic comparative processing done by the subconscious; thinking of objects as members of a group is easy if someone repeatedly makes the association linking the objects. Being constantly confronted with associations linking groups of similar objects with various words, the child soon has a collection of simple first level concepts of objects and is able to name the objects without his parents help. Some effort and an act of choice are required on the child’s part, but these things are easy for its eager mind.

Note - In spite of many claims to the contrary, animals cannot perform this process. What children do in a few months, animals fail to do even after thousands of attempts and much coaching by wishful scientists.14

The child must play an active role in this process; he must choose to regard similar objects as units and he must choose to use the words that are provided by his parents, but this is made so easy by the constant rewards of attention and affection that it is really semi–automatic. The active, volitional behavior on the part of the child is minimized by the efforts of the parents to teach him, who lead him through the process.15

Concepts are used to differentiate and bring into conscious focus different aspects of objects’ identities. Once a sufficient number of concepts of objects have been formed, the naming extends to actions and other properties of objects; implicit similarities of objects are


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made explicit. Concepts of properties serve to differentiate the properties of objects from the objects themselves; the same is true of concepts of actions of objects and of the relationships between objects.16 This capability is something that is not possible at the perceptual level of consciousness because percepts are not data structures that can be taken apart by the human mind.

So what started out as a process of focusing attention on objects to get emotional rewards from a parent, sooner or later leads a child to choose to focus on actions of objects, properties of objects, and the relationships between objects, by applying the same mental process to them as he applied to simply naming objects. As the behaviors in the process just described become automatized, the identity of the memory of the child or other conscious system is organized even more by the conceptual groupings and linkages thus formed; the identity of the consciousness is changed, and along with it, its action capacity.

The child is now conscious of an ever increasing set of symbols which stand for an unlimited number of objects of certain types, as well as some of their properties and actions, and he is aware in a timeless way. When the child looks at reality, his perspective has changed: Instead of an endless sequence of specific, time dependent percepts, he now sees, in addition, named objects such as mommy, daddy, red ball, blue ball, rolling ball, block, barking dog, table, chair, window, cloud, tree, rock, and so on.


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The child can even identify these objects, some of their actions, and some of their properties verbally, though he cannot yet use language. He can use words to name them. All of these new capabilities increase the child’s volitional control over his own mind.17

At this point we can say that the conceptual level of consciousness has emerged. And that emergence changes the action potential of the conscious system that is the child’s mind. The ability to use concepts to differentiate the actions, other properties, and relationships between objects leads to an explosion of content and a new, more powerful organization in a child’s mind that an animal cannot match. The same will occur in a simulated consciousness in the DLF Mind, once it organizes its memory contents using concepts.

Boot Strapping More Complex Choices

The attributes or properties of entities determine their action capacities.18

Concepts are a special kind of content of consciousness: They not only expand the power of consciousness through unit economy, but they are part of the identity of a life– form that can form them, one of its attributes; once a concept is part of the identity of a life–form, it changes the action capacity, the causal capacity of that life–form, expanding its volitional control.

Every time a new concept is formed, the identity of the life–form, human or simulated, changes; memory becomes more organized and that new organization permits more identifications of the reality the consciousness perceives. Since, according to the law of


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causality, identity determines what actions are possible to an object, life–forms that can use mental focus to form concepts control their own action capacities, or causal potential.19

Remember, concepts are not the same as percepts or objects in the outside world; they are timeless and, as symbols, partially independent from that world, and they can therefore be used to easily formulate and initiate first causes.20

This means in effect, that a consciousness with the capacity to form concepts is capable of programming its own identity, and thereby causing its own action capacity. This is not a conscious goal for children or for DLFs at first, but becomes one as soon as having concepts produces pleasurable results, just as running becomes a conscious goal for children once the fun of it has been experienced. The choice to form concepts leads to more mental control, which produces pleasure, which causes the desire to use volition to form more concepts and to use words for more than to just name and describe things.

Eventually, this process leads a consciousness to understand why certain events always follow some, but not other events.

Concepts of Causality

As I have pointed out, concepts have two properties that enable them to make consciousness more powerful: They allow a consciousness to focus on various perspectives of percepts, and they are timeless.


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In the former case, this makes it possible for humans to be aware of different aspects of a single percept, whereas animals or children who have not yet formed concepts can be aware only of complete percepts.

In both animals and humans, percepts are data structures that are an integrated whole, and they contain time dependent information; they are the content of our consciousness of reality, but in an integrated, specific, time dependent form. Concepts, on the other hand, allow the human consciousness to focus on various aspects of a group of similar percepts, to cognitively isolate, focus on, and identify specific aspects of percepts, such as their shape, their actions, or their relationships.

For example, a child and a chimp may observe a blue ball and a red ball rolling across the floor. For the chimp, this is an integrated experience, and while he may remember it and recognize it as something previously perceived if he observes subsequent, similar events, each of those events is just a new, somewhat different integrated experience for him. The child, on the other hand, can use concepts he has formed to focus on different aspects of each perceptual scene to separate and identify them mentally, and because concepts are timeless, he can differentiate various repetitions of the balls rolling into separate, but similar cognitive events, whereas the chimp cannot.

One of the aspects or perspectives the conceptual capacity gives the child is the ability to focus on and mentally separate an action from the object performing it. This enables the child to identify actions separately from the objects performing them; the same is true of the other properties of objects such as shape, color, size, location, and so on. From his parents, the child gets the words to


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symbolize the actions and the grammatical constructions to string a few words together. The parents might say: “Red ball rolls. Blue ball stopped.” or “Block falls–bang. Food falls–splat.” Concepts allow the child to cognitively separate the actions, properties, and consequences or subsequent states of the object’s properties from his percepts of them. The words, which are remembered visually, convert these abstract properties into the equivalent of concrete perceptions so they can be stored perceptually in memory. The human mind is primarily a perceptual processor; concepts and language are logical structures and processes that “run” on perceptual processes; they are a code of symbols.21

Once the child has sufficient concepts of objects and their properties, he can use the same technique to isolate and focus on relationships between objects. For example, by observing the location of his ball under a table, a chair, and his playpen, the child can differentiate the similar locations from those percepts, and with the help of his parents suppling the words (such as “ball under chair”), the child can form the concept “under.” By focusing on and forming concepts of other relationships, the child can become conscious of the spacial relationships of the objects in his world.22 Relationships of other kinds soon follow this same pattern, including temporal relationships.

At some point it becomes apparent to the child that different objects have different actions, and that identifying the properties of an object enables him to predict the action or subsequent state of the object. It is at this point that the child grasps the implicit concept of causality.


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Note - The concept is said to be implicit because its data are in his mind, but he has neither processed it yet, nor has the word to name it.

Consider that it is the properties of the conceptual data structure that have made all this possible; they have done so by integrating multiple percepts into a different data form, thus allowing percepts to be taken apart and analyzed for cognitive purposes; they have made it possible for memory to be organized, by choice, such that a great deal more specific information can be derived from a perceptual scene than is possible using percepts alone.

While it will be years before the child grasps causality explicitly, the data needed to do so is implicit in the numerous instances of it filed under the concepts he has formed of numerous objects observed behaving consistently in certain ways, of states and relationships of objects following consistent patterns of behavior such as occur if an egg and a soap bubble are dropped from three or four feet to the floor. 23

Once his consciousness is organized in this way using concepts, and given the words supplied by his parents, the child can even describe causal sequences verbally using the strings of words he has heard. This is still not language; the child can only string a few words together to make more descriptive names for his conceptual identifications, but he is another step closer.

The point to grasp is that it is the cognitive power concepts that brought him closer. A DLF that has sufficient EPs to enable it to select optional mental


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actions can form and use simulated concepts to organize its memory in a similar manner and will, therefore, have similar capability.

4.2.7 Concepts of Consciousness

Consciousness is a process performed by certain kinds of life–forms. In the examples I have been describing in the preceding sections, it is a process occurring and developing in the mind of a child. The child’s parents and perhaps other children or a pet are objects in the reality the child perceives, and consciousness is one of the actions that those other life–forms perform.

Once a child has formed concepts of actions, properties, and relationships, he can form concepts of consciousness and use them to identify future instances of it. Having the concepts of actions, properties, relationships, and the implicit concept of causality is a prerequisite to being able to do so: Consciousness is an action itself and it causes changes in other objects. The types of other concepts just mentioned must, therefore, first be formed, before they can be used to identify the actions, properties, and relationships of consciousness.

Concepts of consciousness are formed in the same manner as are other concepts, and that subject is covered in detail in one of the references24; the important issue to grasp here is that once they have been formed by a child or a DLF, a real or simulated consciousness can then identify its own consciousness; it can conceptually identify itself.


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Simulating Volition and Self–Consciousness

Like the concept of causality, the concept of consciousness is implicit, but a powerful accumulation of data that completes the ground work for the emergence of a volitional self–consciousness.

4.3 Simulating Volition and Self–Consciousness

According to Ayn Rand, volition is not a separate faculty of consciousness, but an attribute of the faculty of reason, the faculty that processes concepts and makes thinking possible. But if to reason is the process of consciousness using concepts, how is this possible? How does the concept formation process get started in the first place, if it depends on itself for volition?

The answer for humans is that early concepts of perceptual concretes are so simple and easy to form that they are formed almost automatically; they are formed simply because the capability to do so exists and because life–forms can select optional actions if they have sufficient energy. Simple, first level concepts such as concepts of common, everyday objects form a foundation of conceptual consciousness that makes the more systematic use of volition possible, which in turn, makes it possible to form more complex, abstract concepts. This spiral may seem similar to that exhibited by goal–directed behavior. That is because concept formation is a form of goal–directed behavior. Children choose to form more concepts because of the pleasure derived from previous instances of that process; they make forming concepts a goal to feel that pleasure again.

The full capacity for volition and reason, therefore, emerge from the simple choice to use the capacity to form first level concepts, to select the capacity as an optional


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action; that use of volition causes pleasure which in turn causes the desire to repeat the feeling in future conscious cycles by forming still more concepts. This spiral into the future, bootstraps the process.

Life–forms will use all the capacities they have; this fact is an automatic function of their pleasure/pain systems, which itself is set by genetic evolution: To be able to use every capacity a life–form has offers a survival advantage. The proof of that statement is the fact that a given capacity actually exists in a life–form, be that capacity the ability to form concepts or the ability to run; only attributes that offer survival advantages persist in biological life–forms because all others disappear when the unsuccessful life–forms fail to survive. Biological life–forms therefore are driven by their pleasure/pain systems to use every capacity that they have so that when they need them, they can use them for survival. This is self evident to anyone who has ever observed young animals or children.

In the case of humans, this automatic need to act, to continually exercise some capacity, includes the capacity to form first level concepts of objects and actions; that is, to focus, to compare and perceptually measure objects, to regard them as units on the basis of perceived common properties, and to symbolize the resulting concept with some convenient percept such as the universal stick figure for a person or a word provided by an adult.25 Humans have the capacity to form concepts, so they use it because it is easy and it feels good, just like they walk, run, yell, wave their arms, and so on because it feels good. Humans do not have to form concepts, but first level concepts are so easy that they will choose to form at least a few of them.


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Without training, humans at this stage of development will not form concepts systematically, but they will form them purposefully, expanding their range of volitional control in the process. The existence of modern natural languages makes this process even easier because the concepts and the code for their use (grammar) already exist. All the child has to do is imitate adults to learn the systematic use of concepts, even if the child does not fully understand what the concepts mean at that point. Later, when the meaning of the concepts becomes clear, the method for using them systematically (natural language), has already been automatized.

The design of the DLF Program is such that DLFs have the same type of (simulated) purposeful desire and limited volitional use of their conceptual capacity as humans do initially; in other words, DLFs can select the conceive method to compare percepts as an optional behavior. The result is that DLFs can form first level concepts of objects they perceive in the Existence window, expand their knowledge and volitional control, and begin the long spiral to full volitional control.

4.3.1 Axiomatic Concepts Make Self–Awareness Possible

Included in the concepts formed at the first level are axiomatic concepts; these concepts are unique in that they can be held both as implicit, first level concepts and as the highest abstractions possible, the ultimate genera, the “top of the hierarchy” for all contexts; this latter fact only becomes known as part of a highly abstract, objective philosophy. However, since most people don’t study philosophy, they hold axiomatic concepts as implicit first level abstractions.26


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To be implicit means that the information, the data, that is contained in a fully formed concept is present, but not completely processed.27 An example of a similar situation can be seen with the concepts of “operating system” and “application program” in the mind of someone who is new to computers and has had no one to name these components of the computer system for him. Such a person could certainly see the differences between the operating system and the application programs, he would see and remember wordlessly that one program is used for control of the computer and must always be present in the background, while an application program is used to do some specific task, such as writing letters; he would even build up a number of specific, perceptual experiences using these programs. But such a person would not have the concepts of “operating system” or “application program,” just the data from which those concepts could be formed. The concepts would be implicit in such a person’s mind; the knowledge is there, but it is unprocessed. The same is true of axiomatic concepts in the minds of most people.

Axiomatic concepts are implicit in the very capacity to form concepts in the first place because the data they integrate are always available to form them explicitly, even if those steps have not been taken and words are never used to symbolize them. The reason the data are always available is that the scope of axiomatic concepts is so broad.

Conceptual consciousness is a relationship, a relational process performed between the mind/brain and other body systems of human beings and reality; to exist, to be an identity of some kind is a property of every object in reality; the fact of awareness is a property of every


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conscious event of every human being. These are basic axioms.28 The automatic subconscious integration process integrates these data just as it does for any other first level concept. If you have formed any first level concepts (and that is where everyone must start), then the objects they symbolize exist, are identified, and you have processed them (at least perceptually) in order to be aware of them.

The axiomatic concepts integrate data across the entire body of a consciousness’ knowledge base because the units they group are all existents. They, therefore, form a boundary between the known and the unknown.29

Another reason axiomatic concepts are important is that they make self–awareness possible. If an animal could use language, it could only say things like: “Here now food.” “Feel now hunger;” percepts are specific to sensory input and time dependent. Axiomatic concepts enable a human consciousness to use language and to say: “There is food.” “I am hungry, and I am conscious of it.” This is something a perceptual consciousness cannot do; it is a capacity that only emerges at the conceptual level because of the way concepts store data.30

As with any process, consciousness is a series of steps or events; in the DLF Program I have called these steps C.Events, but the human mind, being limited in capacity can likewise be aware of and process only so much information at any instant. If you introspect, you will see that your memory consists of a series of perceptual events. Some of these events are of objects outside in reality, some are of the consciousness directed at its own body and mind, in other words, directed at itself. It takes the power of concepts, specifically the timelessness of concepts to link these series of separate, specific,


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perceptual conscious events into a continuum that is the “self.” Self–consciousness emerges at the conceptual level because concepts, and specifically axiomatic concepts, are data structures that have the necessary properties to make it possible.

The self is a virtual entity.

Most people are not even aware they use and depend on axiomatic concepts because the concepts are implicit and held visually as mentioned above, rather than with explicit verbal definitions; they have never consciously completed the integration of the data and added the words “existence,” “identity,” and “consciousness” to symbolize their axiomatic concepts. Unless people have been exposed to philosophy, they simply accept the self-evident facts without questioning that reality exists, that objects have identity, and that they are conscious of it, just as the person in my example above would accept the existence and function of “operating system” or “application program” as self–evident on his computer. That is all people need to do to function: One does not need to know the terms “operating system” or “application program” to use a computer at a basic level; nor does one need to know the terms “existence,” “identity,” and “consciousness” to live. These concepts only become important in the study of philosophy or the operation of consciousness.

It is exposure to philosophy that leads people to question these self–evident facts. What answers or conclusions they draw then depends on what philosophy they have been exposed to and accept. Objectivism explains how all human knowledge is built on a foundation of self–evident


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facts that are perceived by every one of us in reality. Irrational philosophies lead to the denial of the very self– evidences on which their existence depends.

Few people realize that it is the operation of the axiomatic concepts in their minds that enables them to grasp reality as an integrated continuum, instead of as a series of disconnected, short, perceptual scenarios, as is the case for the automatic, non-conceptual consciousness of animals.

No one knows how long it took the first humans to systematize the concept formation process so they could teach children to do the same. No one knows how long it took before someone invented language and the ability to form second and higher level concepts, but that they did is proven by the fact that I am writing this book using those inventions.

The point is that the conceptual capacity, though not automatic, is pleasurable, self–perpetuating, and it continually extends the range of volitional control. Humans find they can cause changes in themselves, in the identity of their consciousness and thereby in their action capacity. Because of the axiomatic concepts, humans are able to be aware that they are aware of themselves and the changes they cause to their own consciousness and action potential. They choose to form concepts on a continuing basis because it feels good, it has survival value, and they know it.

The design of the DLF Program enables DLFs to simulate these same capacities by virtue of their ability to form concepts, including axiomatic concepts based on optional,


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goal–directed actions. The properties I have described emerge from the conceptual level of consciousness, regardless if it is real or simulated.

4.3.2 Axiomatic Concepts and Full Volitional Control

I explained earlier that full volitional control of consciousness implied a “self” existed to exercise that control, and it should now be clear that axiomatic concepts enable that “self” to emerge, and that the “self” is a conceptual structure, a virtual entity.

Remember, the concepts that make volition and self– awareness possible are implicit in the minds of developing children. These implicit concepts also must be formed in the following order: Existence, entity, identity, action, causality, and consciousness.31

Each of these axiomatic concepts depends on the previous ones in the series. A child or a DLF must be conscious of something before they can differentiate an entity (object) and perceive its identity. They must have concepts of objects before they can form concepts of the actions (and other properties) of objects and of causality, because the concept of causality is abstracted from lower level concepts of the identities of objects interacting, and therefore presupposes concepts attributes and concepts of objects have already been formed.

A child or a DLF is itself a kind of object with a specific identity that acts causally in a certain way. The concepts of self and self–consciousness can therefore not be formed until causality is grasped conceptually; “self” and “self–consciousness” are concepts that differentiate one’s own body and mind from that of others and the rest of


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reality, and thus enable a conscious system to add itself to its own world. All of these capacities depend on and are made possible by the nature of concepts.

Once the self is a part of a child’s world, full volitional control can emerge. The self can be conscious of its own causal role in the actions it takes in reality. But to be able to take maximum advantage of volitional control, the self needs one more thing: Natural Language.

4.3.3 Axiomatic Concepts Make Natural Language Possible

With the invention of a systematic code to name objects, actions, other properties of objects, and relationships, comes the ability to think using logic, or as Ayn Rand calls it: “the art of non–contradictory identification. 32” This process amounts to the use of concepts in a systematic way to identify reality by means of symbols and grammar.

A symbolic representation of reality, if it is accurate, is in effect a mental simulation of reality; it is reality in a different form in the memory of a conscious system, in a way that is analogous to the simulation of a new airplane design on a computer is reality in a different form (represented symbolically) in the computer’s program. The question is: How are concepts tied together to accomplish this, to mean something about reality?

Natural language consists of sentences, each of which is a complete thought or proposition about reality. To go back to the example used above, if a child who has learned language observes a red ball rolling in front of him and says: “The red ball is rolling across the floor.”, the child is


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relying on something to tie those words to the scene; that something is his implicit axiomatic concepts, which he uses to make his assertion.

In order to know what he is saying, the child is relying on the implicit conceptual knowledge, knowledge that tells him that he, the ball, the floor (and the rest of existence) exists. The child is relying on the fact that he, the ball and the floor are objects, that he, the ball, and the floor have identities, and that those identities are interacting causally in certain relationships, including the relationship of his own consciousness to the scene.

The axiomatic concepts preserve the epistemological continuity of the scene, they confine the child’s knowledge to reality, and provide “epistemological guidance,” or a way to check propositions against known facts.33

The child can construct such a sentence because his concepts have enabled him to separate the components necessary from the integrated percept he has observed. Each concept in the sentence encodes some aspect of the scene; each concept has been formed from numerous observations and comparisons of similar percepts. It is those observations and subsequent processing that enable the concepts used in the sentence to tie the words to reality and give them meaning: “The” indicates a specific object, “red” indicates a property, “is” indicates the object exists and is acting now, “rolling” indicates the nature of the action as an attribute of the object, and “across the floor” indicates a relationship to the object of the action.


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Note - The child does not have to add: “and I am conscious of it” because that relationship to the ball is implied by the fact that he uttered the sentence in the first place.

Every sentence of every natural language uses some permutation of the Subject–Verb–Object (SVO) encoding. The reason is that every natural language is connected to reality through the axiomatic concepts. Every sentence is about something doing (or not doing) some action to some object: Existence, identity, causality, and consciousness. That is the core of natural language.

Note - I was once told by a student of linguistics that there are six permutations of the SVO encoding, though only four are known to be used in actual human languages.

Once a child learns the SVO encoding sequence and has a reasonable collection of concepts, he can make up sentences at will. It is at this point that full volitional control is reached.

The symbols and grammar of natural language can be manipulated independently from the world they represent and potentially put together in any configuration because the nature of concepts gives them that independence. Language can be about truth if it corresponds to reality, or fantasy if it does not. The properties and values of objects words symbolize, the components of reality, can be expressed in language and changed at will while in the form of information, in symbolic form, and then reality


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can be changed (or not) depending on the desires and action capacities of the consciousness using natural language.

For a conceptual consciousness, a large amount of physical causation is optional. For animals, with only automatic perceptual consciousness, there are no such freedoms because percepts are integrated, time dependent data structures. There is no way for the animal to separate all parts of reality and then reassemble them as humans do when they formulate sentences and acquire all the changes to human action capacity the sentences imply. Only very narrow optional actions are possible to animals based on perceptual learning and assuming the energy is available, whereas man can build rockets to carry him to the moon, if he chooses to.

This is how a conceptual, volitional consciousness initiates a first cause: It first modifies the symbolic version of reality in its consciousness as an optional mental action to manipulate natural language to satisfy a desire to change something; the desire causes the modification in its memory as one of several options the consciousness has available, and the modification changes the causal potential of the life–form because it changes its identity. In a subsequent conscious cycle, another desire then can cause the identical modification in reality in the form of an optional physical action (if that choice is made by the conscious system), and a first cause has been initiated.

For example, the child in the example above who is watching the red and blue balls roll across the floor makes a choice to focus his mind, initiates an optional mental action, and thinks to himself: “I will stop the red ball.” If


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he does not act on that thought, he has merely caused a change in his memory. If, on the other hand, he does act on the thought and stops the ball in reality, by initiating an optional physical action he has initiated a first cause in reality: Nothing in reality necessitated his action, because to satisfy a desire or not is internally controlled, optional mental behavior for the child. There is no “billiard ball” like event that necessitates his action. His action is caused, but it is caused by the child’s own, internal control system, if and only if, he chooses to act.

In other words, the change in reality is caused by the identity of consciousness and the nature of teleological causality; it is not determined by some perception or mechanistic outside event. Consciousness is not mechanistic, it is not necessitated to change its identity, to form concepts in order to cause changes in reality; that is an option that is under the direct, internal, mental control of a conceptual consciousness.

DLFs simulate living entities, exist conditionally, and therefore have both necessitated (if they are to survive) and optional actions. DLFs can also form concepts and therefore be aware of multiple facets of reality, an aspect of their identity that amplifies the ability to use optional mental actions.

DLFs will be taught natural language by a human tutor, so they will not have to reinvent it for themselves. Language is not primarily a means of communication; language is a tool of concepts and thinking. By teaching DLFs language, they will inherit the power of concepts and thinking, including self–awareness and the ability to initiate first causes.


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Once having achieved the ability to use natural language, DLFs will be capable of increasing levels of rational behavior, just as human children are.

4.3.4 The Spiral Theory of Learning in DLFs

The full power of concepts and the ability to think will not occur in DLFs all at once, as I have explained. Like life, learning is a repetitive process that spirals into the future.

The cognitive development of DLFs will follow much the same pattern as that of human children. First, they will use simulated concepts to name objects, then actions, then other properties of objects, and finally relationships between objects, as the sophistication of their concepts and use of language increases. At each pass around the learning spiral, each of which will require many C.Events, their memory will contain more facts and this will expand their context, which in turn will allow DLFs to form new concepts, expand the definitions of those already formed, and increase their volitional control.

DLFs will also have to develop the simulated counterpart to human psycho-epistemology, which is the interaction between the conscious and subconscious functions of the mind.34 Each conceptual human consciousness develops its own habitual method for interacting with its subconscious, and this phenomenon should develop as part of the operation of a properly designed consciousness simulator as well.

The conscious part of a conceptual consciousness is the function involved in active processing; the subconscious is all the other functions it has, such as memory and the


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Simulating Volition and Self–Consciousness

comparison function used to form concepts. 35 This view is consistent with conceptual consciousness being an emergent property of the conceptual level: It means that various conscious actions are consistently chosen by such a consciousness (or not) as it regulates itself, and these actions rely on subconscious cognitive functions to operate.

According to Dr. Binswanger, the interaction between the conscious and subconscious in a fully functional, normal mind involves the conscious part of the mind asking questions, and the subconscious part providing the answers. The main questions that get asked about a given piece of conscious content are:

• Is it true?

• Is it good?

• Is it important to my life?

• Is it relevant to my current train of thought?

These questions are asked over and over through many conscious cycles for conscious content for which thinking is involved.36

In other words, when a DLF is “thinking,” its method of functioning will be to focus on some content and ask the above questions of its simulated subconscious; a given question will be asked in one C.Event and answered in the next.

This process is a learned, optional behavior on the part of the virtual entity, the DLF Mind; it is not preprogrammed, and its effectiveness partially depends on the content and connections made in past C.Events. More


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content and better connections between concepts mean better answers to the questions; this in turn means more efficient “thinking” by a DLF.

4.4 Two Interesting Scientific Discoveries

Science and engineering are moving forward at an ever increasing rate. The following descriptions of two scientific discoveries not only support that assertion, but also the thesis of this book.

4.4.1 “Nano–Biology”

To some, the idea of substituting computer or other man– made mechanistic technology for the natural mechanisms of physics and biochemistry to serve as an “animation platform” for life–like processes may seem like an impossible fantasy. Yet times are changing quickly, as an article in the March 2001 issue of Popular Science Magazine demonstrates:

“Carlo Montemagno and his colleagues at Cornell University in Ithica, New York, have created the first microscopic motor–no bigger than a virus particle–that runs on a biochemical called adenosine triphosphate, or ATP, which the body burns to generate energy. ... Through a series of chemical reactions, the team grafted nickel propellers–each only about one thirty-thousandth of an inch long–onto the central shaft of 400 of these motors. When immersed in an ATP solution, five of the motors spun their propellers at an average speed of eight revolutions per minute for 2 1/2 hours, which was


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4.4 Two Interesting Scientific Discoveries

captured on video tape. ... Montemagno’s team is now trying to build devices that can self-assemble inside human cells.”37

Just a few years ago the idea of nanotechnology was science fiction. Now it is becoming a reality. Perhaps the idea of substituting man–made mechanistic platforms for natural ones in life processes is not so far fetched after all...

4.4.2 Perceiving the Identity of Objects

Another article on human sense perception in the same magazine supports the theory advanced in this book that perception (consciousness) is the identification of the properties of objects:

“At the University of Rochester’s Brain and Cognitive Science Lab, Professor Mary Hayhoe asks a subject wearing a head-mounted eye tracker to make a peanut butter and jelly sandwich. ‘We all do it pretty much the same way,’ she reports, ‘and we all look at pretty much the same locations on objects. That was really surprising. I would not have expected that for natural behavior.’ ... With each glance at that jar of peanut butter, you gather different information, according to Hayhoe. You look at a certain location on the jar to grasp it, another to see that it is peanut butter and not jelly, another to gauge the position of the jar relative to your body, and so on. ... ‘That’s different from our impression that when we look around, we feel that we see everything at once.’”38

These findings support the idea that perceptual consciousness is a specific, limited, goal–directed process of awareness, the purpose of which is identification .


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4.5 Summary

In this chapter, I have explained how self–consciousness emerges at the conceptual level of consciousness due to the unique nature of concepts as data structures.

I have also explained how conceptual consciousness emerges from perceptual consciousness through concept formation, and how volition, self–awareness, and natural language emerge from implicit axiomatic concepts.

I have explained how these same capacities emerge from the design of the DLF Program and the rest of the simulation system that gives DLFs the ability to form and use concepts. Finally, I have explained how once started, and with the help of human tutors, this process will spiral onward into the future to enable DLFs to use simple natural language and attain full simulated volitional control in a manner similar to the way humans do.

In the final chapter, I will review all the ideas covered in this book so far to create a concise snapshot of both the theoretical and technical basis for the DLF simulation system; the chapter is written with the needs of a programmer skilled in the art of object oriented programming in mind. I will explain how the DLF Program code needs to be written, including some example flow charts to show a programmer how to enable DLFs to simulate life, simulate the perception of reality, act in reality, simulate concept formation, and how the concepts formed result in the emergence of simulated volition, self–conscious DLFs, and simple natural language understanding. Examples of how a DLF with those properties can encode and decode simple English sentences will also be described and explained.


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4.5 Summary

Chapter 5 is a complete description of my invention and is the basis for my patent application, so it integrates and summarizes all of the essential ideas presented in this book.


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5 How to Simulate Consciousness

5.1 Introduction

In the previous chapters, I described the theoretical basis for the invention discussed in this book, the prototype consciousness simulator I have been designing and building in a specific object–oriented programming environment to help work out and test many of the ideas used in the invention, and how it is possible to use such an invention to simulate or mimic some animal and human conscious behaviors.

One purpose of this chapter is to help all readers to integrate these ideas by describing how to reduce the invention to practice; in other words, by describing how to build a computer simulation system capable of simulating rational self–consciousness, including simulated volition, or “free will” as it is also called.

Another purpose of this chapter is to make the description of the simulation system intelligible to someone skilled in the art of computer programming, and do so in terms general enough to be applicable to any object–oriented programming environment.


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5.1 Introduction

That being said, let us review what current state of the art computer systems are and see how they differ from what I am proposing in my description.

First, it must be stated clearly and unequivocally that a computer system can never be alive or conscious in the exact same sense as a biological life–form can. However, a carefully designed computer simulation system can imitate or mimic the processes of life and consciousness to some degree (just as a mannequin imitates or mimics the human form), and such imitations will improve as computer technology becomes more powerful.

However, simulated consciousness is still simulated ; a computer simulation system is not conscious the way animals and human beings are conscious; it merely imitates some of their actions to make a better human interface.

All computer systems are electronic machines built by human beings, machines that manipulate binary bits for human goals according to human logical rules specified in the computers’ automatic programs. They are neither alive, nor conscious, and they certainly do not have “free will.”

Strictly speaking, all computer systems do is manipulate binary bits in the form of electrical capacitances; they do not add, do not do word processing, do not process information, do not sort records in databases, and so on. Only people do all these things, and it is only in reference to a human mind, a conscious human mind that the terms such as information and knowledge can apply; it is the human mind that sets the context for these terms.1


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How to Simulate Consciousness

In addition, all computer systems must first be programmed in order to function at all; that is, every action the computer’s processor is to execute must be described in a suitable programming language in exacting detail by a human programmer. This is why the programs can run by themselves in the first place, why they can be called automatic, as opposed to requiring human intervention at every step, that is, as opposed to being manually operated. Any breaks in the program definition are an action that is undefined and will cause a computer to “crash,” with absolute certainty, if the undefined section of computer code (also known as a “bug”) is encountered by the processor. Even the so–called “self– programming” capabilities of genetic algorithms and neural network computer systems (Holland) rely on specific programs to pre–define their basic functionality; only after these programs are running properly can they interact with their environment to automatically modify themselves from a program that is too general to perform any task except change its own parameters, to one that does some specific task that is defined by the data the program inputs.

The basic problems of all attempts to design computer systems capable of Artificial Intelligence (AI) (McDermott) or Artificial Life (AL) (Pattie Maes at MIT and Los Alamos laboratories) is that computers are not alive, not conscious, and they require a programmer to specify in advance not only what actions the computer system will perform, but also where, when, and how it will effect the actions. By contrast, the intelligent behavior of biological life–forms is largely self–defining and is so without the requirement of human intervention. The mechanistic, pre–definition of action, therefore, is at the same time the reason computer systems can run


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5.1 Introduction

automatically, and their downfall as life and consciousness simulators; it is their downfall because it is impossible (a contradiction) to attempt to pre–define the actions of a self–defining system, yet that is exactly what some in the field of AI have attempted to do.b

Note - This statement does not mean that no part of a self–defining system can be defined by human programmers, an idea which implies the system must completely recapitulate evolution. The statement simply means that the self– defining aspects of such a system must be designed in a way that the system has the control and energy to define itself at some level, and that could mean using a set of basic actions that have been programmed by human beings.

In fact, it is precisely the attributes of consciousness and volition, or “free will,” that enable human beings to perform manual tasks at all, such as to invent and build computers or to write their code in the first place. This is possible precisely because the human beings that AI and AL computer systems are supposed to emulate are not automatic: Computer programmers are not another kind of computer program; they are people with free will who are capable of performing manual tasks! The behaviors of the human programmers who write the automatic programs that computer systems run do not have all their behaviors pre–defined. The computer programs they write may be automatic, but they are written manually.

How does one break this paradox? How does one design a computer system that is not automatic?


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Genetic algorithms attempt to get around this paradox by emulating or recapitulating evolution in the hope that eventually intelligence will emerge; neural networks attempt to do something similar by emulating the function of neurons in higher life–forms. It is possible that these methods working together and with help from programmers to adjust their operation from time to time could, eventually, produce a simulated life–form that could eventually simulate consciousness and volition; but if the history of life and human evolution is any indication of how long such a process takes, it will undoubtedly take a long, long time before we see any results. For example, in the Blue Genes project at IBM®, one protein folding took a year of processing time to simulate with a petaflop computer that was 1000 times better than the average super computers in the year 2000.

The invention described in detail in this chapter makes it possible to build a simulated life–form with the attributes of simulated consciousness and volition in a much shorter time, probably in two to three years of focused effort by a team of two or three programmers using off–the–shelf computer hardware and an object–oriented programming environment. Of the entire development time, approximately a third of it would be required to write the basic simulation system code; the balance of the development time would be used to train and refine the system by helping it to learn and understand the world it perceives and improving its program code and operation. The amount of data to be learned by a consciousness simulation system of this design is large, but not unmanageable; the objects and relationships found in a


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5.1 Introduction

typical pre–school play room would be sufficient to get the system started and for it to learn basic percepts, concepts, and other aspects of reality.

One reason for the relatively short development time is the fact that this invention copies many design ideas from real life–forms instead of attempting to re–evolve them to recapitulate evolution in some manner as genetic algorithms do. In other words, just as the AL researchers did not re–evolve the gait of their insect robots, but reverse engineered their operation by copying real life– forms, so this invention seeks to reverse engineer the simulation of goal–directed behavior and the processes of consciousness rather than re–evolve them.

Another reason is that this invention solves the pre– definition of action problem by programming a simulated life–form with attributes similar to those that enable biological life–forms to define their own teleological actions. The invention breaks the paradox mentioned above by using the more complex form of causality found in biological life–forms, a form of causality that works by moving both the energy source for the life–form and its control into the simulated life–form itself. With the locus of control and its own energy source, the potential exists for action that is independent of either direct or indirect human control. This form of goal–directed action is different from current state of the art robotic or computer agent action as it operates in extant systems, and the differences will be explained shortly in detail.

Consider the following thought experiment crafted by Dr. Harry Binswanger to differentiate the actions of living from non–living objects: An ice cube and an earthworm are put on slanted boards and over each hangs a heat


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lamp. Both the ice cube and the worm will move away from the heat, but the cause of the ice cube’s action is external and mechanical, whereas the cause of the worm’s action is internal and teleological. If the experiment is repeated with the boards flat instead of slanted, the ice cube will not move and simply melt because it has neither life nor the capacity to preserve it; the worm, however, moves on its own internally controlled power because the heat is inimical to its life, and its goal is to survive.2

I will explain how teleology or goal–directed behavior can be simulated in more detail below; for now, consider that this approach enables a new kind of behavior that is not automatic in the usual computer or mechanical sense in which this term is applied to state of the art computer systems (that is, pre–programmed, in the sense of an automaton), but automatic from the perspective of life– forms and a life simulation system, a system that behaves like an earthworm rather than an ice cube.

However, simulating teleological behavior does not preclude the predefinition of some of a simulated life– form’s actions. Like state of the art genetic algorithms and neural networks, this invention is partially pre– defined and partially emergent, but the design is also different from current state of the art ideas. By pre– defining a system with an internal energy source and internal control like a life–form , I have essentially copied the logic of the causality of life that has taken billions of years to evolve in order to cause the behavior of an earthworm. I have applied a strategy similar to that which some in the field of AL have used to very good results in


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Introduction

the development of robots that simulate the capacity of insects to negotiate rough terrain without pre–defined actions, such as some of the cock roach robots.

The difference is that I have applied that strategy to both the cellular level of life and to mental actions of complex life–forms to simulate their consciousness. How I have done this will become apparent as I describe the invention in detail.

The new behavior, along with some new data types, set the stage for the eventual emergence of simulated self– consciousness and simulated volitional behavior by that simulated consciousness. In other words, the invention solves the previously unsolved problem of needing to specify all of an AI or AL system’s actions in advance by permitting all actions and then using an automatic means of eliminating the unwanted actions, where automatic here means in the biological sense, not in the sense of a computer automaton.

The essential ideas of the invention are presented, described, and explained based on the following key points:

• The problem of action pre–definition is solved by using goal–directed behavior: Goal–directed behavior is how all life–forms stay alive; it is required by the conditional nature of life processes and is an automatic means of limiting behavior without specifying what behaviors are permitted or when they are permitted; energy and action control reside inside the life–form, and actions are controlled by simulating pleasure and pain, with the digital life–form’s own “life” as the standard of action. This method of control is based on a complex form of causality: In order to act, a simulated


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life–form must first survive; unwanted actions, therefore, do not get repeated in the long–term because the life–forms that would have repeated them no longer exist. This form of causality is complex because it involves the issue of the survival (continued existence) of the acting object, as opposed to the simpler form of mechanistic causality in which there is no issue of the acting object’s survival (because non–living objects exist unconditionally).

Note - At the risk of being redundant, I want to re– emphasize two important points: First, the fact that goal–directed behavior is the means this invention uses to solve the action pre– definition problem does not mean human programmers cannot pre–define basic actions such as Look, Find, or Eat to build a starter simulation system; goal–directed action refers to actions (or sequences of basic actions) selected by a life–form for survival purposes; it does not mean the recapitulation of the evolution of actions from no actions. Second, in biological systems, automatic goal–directed behavior is still goal–directed, not automatic in the sense of computer or robot automation, which operates on mechanistic causality.

• Intelligent action presupposes consciousness: Awareness of the world outside a digital life–form is made possible by simulating consciousness. Simulated consciousness begins with the simulated perception of objects, and these perceptions provide a processing unit economy (or content–oriented form of data compression) that has a survival advantage to digital


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Introduction

life–forms; the need to process fewer units saves processing cycles (energy and time), properties of importance to a life–form whether real or simulated. The perceptual form of simulated consciousness is automatic (in the biological sense), partially pre– defined, and is programmed in the conventional way; the processes that produce the content for simulated perceptual consciousness are not modifiable by a simulated life–form, only the sequencing of its actions in reality are. This statement goes back to the earlier explanation that this invention is partially predefined and partially self–defining: How the simulation system described herein senses the world, resolves objects in the world, and forms its percepts of them by extracting their attributes is pre–programmed; what objects the system “looks at or focuses on,” the content of its perceptions and how it reacts to those percepts (which actions in its basic action set it selects and in what order), it defines for itself. This simulation cannot reprogram its perceptual processes, but it can reprogram how it makes use of those processes to aid its own survival by the action sequences it selects.

• Volition (free will) implies the ability of self– regulation: Another level of control is possible for a digital life–form by making some of the processes of simulated consciousness self–modifiable, unlike the perceptual processes just described. This enables a digital life–form to change the way in which the content of its simulated consciousness is stored in memory and to have simulated volitional control over its memories by organizing them with a new data type called a concept. Concepts (when formed by a process defined by Ayn Rand, see note below) are symbolized by natural language words, and the specific way in which concepts


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are calculated define their meaning and the digital life– form’s simulated abstract view of reality; concepts also set the stage for natural language to emerge in a DLF in the mid to latter stages of development. The emergence of natural language will occur approximately half way through the 2-3 year development process for the simulation system, since it depends on the system having formed (calculated) several hundreds to perhaps a thousand conceptual chains; this is necessary because it is the conceptual chains that the system uses to calculate the meaning of the natural language words that symbolize its concepts. The details of this process are explained later in this chapter.

• The need to process fewer data units equals a survival advantage: An additional survival advantage for a digital life–form comes from simulated conceptual consciousness due to the time independence and even greater processing unit economy attributes of concepts, and the fact that concepts can represent in conscious awareness normally invisible phenomena such as relationships between perceived objects; this fact makes possible a stable, unitary world view for a digital life– form, a world view that is in the form of abstract symbolic information in addition to percepts of specific objects (though every abstract is connected to specific percepts via chains of “manual” calculations).

• Concepts make simulated self–consciousness possible: The consequences of the self–modifying nature of simulated volitional control over memory at the conceptual level in a simulated consciousness of a simulated life–form enables the emergence of a concept of “self” for that digital life–form. Once formed, the concept of “self” enables a simulated rational self–


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Introduction

consciousness to emerge, as well as the use of natural language in the form of simple sentences. A digital life– form can then use natural language primarily as a means of gaining further knowledge and, secondarily, for communication with human beings and other digital life–forms; this greatly improves the system’s interface. As with the description of simulated volition above, these attributes will emerge in the mid to latter parts of the 2-3 year system development process, and for the same reason: Simulated natural language and self– consciousness depend on chains of conceptual calculations to operate, so they cannot emerge until the system has formed those concepts and can follow the calculation chains of the concepts to use the knowledge of reality the concepts contain (including knowledge of “self”).

Note - The term concept as used in this description means only concepts as defined by the method described in the book Introduction to Objectivist Epistemology by Ayn Rand (see references). None of the common uses of the term “concept” that are found in philosophy, science, or the field of AI, such as how the term is used by Lenat, Shank, Collins & Stevens, and so on apply to this description; a more detailed explanation of what this means follows later.

The details of the ideas introduced so far and how to reduce them to practice is what the description in the balance of this chapter contains. Once these basic ideas contained in this description of the invention are learned and integrated by an expert programmer, that programmer


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will be able to design and build a digital life–form which can simulate rational self–consciousness. The simulation system can be created by the following steps:

1. Set up an object–oriented programming environment with the necessary classes and objects running on off– the–shelf computer hardware such as a PC with a medium amount of memory and hard disk storage.

2. Write the program methods for a rich, simulated world of objects and actions (these are “reality” for the digital life–form), including an interface to enable interaction by a human teacher for drawing shapes and typing text. (A plan to transition the digital life–form to interaction with the real world using off–the–shelf sensors and software should also be created at this point in the design, to be implemented when the digital life–form reaches some designated level of knowledge; the exact level will need to be determined experimentally, but will probably occur when the system is not learning any longer, or not learning fast enough.)

3. Write the program methods for simulating goal– directed behavior and the life processes for the digital life–form as specified in later sections of this chapter (including simulated “death” to eliminate behaviors that are anti–life, and a means to preserve survival behaviors). Some of these methods have already been written and tested in the proto–type DLF Program described in Chapter 3.

4. Write the program methods for perception, evaluation, action selection, memory, and taking actions to cause change in the simulated world (and eventually the real world) by the digital life–form as specified in later


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5.1 Introduction

sections. (Some of these methods have already been written and tested in the proto–type DLF Program described in Chapter 3.)

5. Write the program methods to enable a digital life– form to perform the actions of object comparison and memory modification necessary for concept formation (as per the method defined by Ayn Rand); that is, for the digital life–form to be able to calculate chains of conceptual relationships and symbolize them with natural language words as specified in later sections of this chapter. (A proto–type program for forming simple simulated concepts of closed shapes has been written, and it was successfully tested on actual hand drawn shape data by the author during some experiments performed in the early 1980’s.)

6. Animate the digital life–form, allow it to explore its world to build some memories, help it form concepts by providing examples and the words to serve as conceptual symbols, transition it to perceiving and acting in the real world, and then repeat the interaction over and over in various contexts to “train and educate” it, thereby helping the emergence of simulated self– consciousness and natural language understanding.

If done correctly, when these steps have been completed, the capacities of simulated rational, self-consciousness and volition will have emerged in the digital life–form in the mid to latter part of the 2-3 year development process I have been describing. Moreover, it cannot be emphasized enough that in order to be successful, the simulation system must perceive objects in their natural relationships in the world to be able to simulate human


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consciousness in order to form concepts of those objects and relationships that are similar to the corresponding human concepts.

While this process may be started with a simulated world such as a game world, if the digital life–form is to be conscious of and function in the human world, then it must ultimately perceive in, act in, and form concepts of the same world we humans perceive, act in, and form concepts of, and it must do so directly (for itself).

Before I can go into the specifics of how to implement the steps listed above, the ideas that were described and explained in detail in the first four chapters of this book must be reviewed to set the proper context. This invention’s reduction to practice depends on those ideas, many of which are new to the state of the art and therefore must be reviewed for even the most experienced computer programmers.

5.1.1 A Few Prerequisite Ideas

As explained above, computer systems are man–made machines that are neither alive nor conscious and can never be so in the exact same sense as biological life– forms.

Moreover, there is a huge difference in the ways in which computer systems behave as they are designed to operate in the current state of the art and the way life–forms operate. In computer systems, actions are programmed, which means they are caused and regulated by humans and are therefore very predictable, whereas life–forms are independent entities and much less predictable.


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5.1 Introduction

Note - To whatever degree computer systems are unpredictable at all, it is only because of pre– programmed randomness (such as from the use of random numbers), or that some systems are so complex that human consciousness cannot process all the possibilities in order to predict what all of causal consequences of the programs will be as they are run. (However, unpredictability due to this latter limitation does not imply that state of the art computer systems operate by something other than simple, mechanistic causality.)

Computer systems are action platforms that are capable of an endless variety of potential actions; they are like a blank sheet of paper, which is a “platform” on which an endless variety of stories can be written. But computer systems are like “causal paper,” which instead of merely describing reality like ordinary paper, can animate aspects of reality in both actual and virtual forms.

Computer programs are what limit the endless potential actions (causes) computer systems are capable of, limit their actions to some set of specific actions: The actions that constitute a specific computer program. In current state of art systems, actions are limited by the programmer by pre–defining them in exacting detail when a program is written.

The actions of life–forms, on the other hand, are self– caused and self–regulating like the earthworm in Dr. Binswanger’s thought experiment. Only certain specific actions get repeated, but this does not imply that life– forms are “biological computers,” as many people have


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assumed. While it is true that some actions are pre– defined, biological pre–definition is not the same as computer programming. The alternative is that the actions of life–forms are limited by something other than simple, mechanistic or “billiard ball” causality (though not something supernatural). In order to understand what that “something” might be, it is first necessary to look at the identity of life–forms in more detail, especially the conscious ones.

Ayn Rand identified a metaphysical fact that is implicit in every action of every object: What a thing is, its identity, determines what it can do, its action capacity in reality.3 This fact is as true for computers as it is for rocks or conscious life–forms: In order to understand how to simulate consciousness, it is first necessary to understand what consciousness is.

Metaphysically, consciousness is a fundamental state of being; it is axiomatic, and it is an attribute of some life– forms, a relationship they have with reality. From a metaphysical point of view, conscious life–forms are in a state of awareness of the world they live in, as opposed to life–forms without consciousness that are not in that state (the latter operate my means of simple sensations, not perceptual consciousness, or awareness of objects).

Note - Axiomatic, as the term is used by Ayn Rand, means implicit in your awareness of some fact, and therefore inescapable. For example, the statements: “Existence Exists,” “A is A,” and “Consciousness is Conscious” are axiomatic because they are implied and affirmed by any attempt to deny them.4


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5.1 Introduction

Operationally, consciousness is an active process supported by other subconscious processes in living organisms. These subconscious processes are the brain functions of conscious life–forms, and they are all causal processes, as is consciousness itself. Consciousness is the subset of subconscious processes that are being activated at a given time; being activated makes them conscious processes. Both conscious and subconscious processes are brain processes that operate because of the activity of the neurons of which the brain is made.5

There is evidence that the encoding of new memories may be a physical process, that at least part of the cycle of consciousness is caused or at least supported by new neurons growing in a life–form’s brain.6

Consciousness is a limited process with a specific identity (consisting of several subprocesses), just like any other attribute of a life–form is something specific; for example, consciousness has properties and values as do the attributes of size, number of limbs, and the process of digestion; this is how consciousness can be a causal process: It is made of the same “stuff” as the rest of the life–form, so it can interact with the rest of the life–form. The identity of conscious processes interact with the identities of objects and other processes in a living body just like any other life processes do, so there is no “mind– body” dichotomy. However, the result of conscious processes is awareness, instead of, say, the nutrition of digested food. Unlike digestion, consciousness is an pro– active process, but more on that later.

Perceptual consciousness in biological life–forms is a process of sensing reality, integrating sensations into percepts by isolating objects and extracting their


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identifying attributes, evaluation, action selection, memory, and action in reality, a process which repeats in an endless cycle (as long as a conscious life–form is alive and awake).7 In most life–forms that possess it, consciousness is largely an automatic process (in the goal–directed or teleological sense of biology).

The exception is Man, who has the capacity of free will; he is capable of manual conscious behavior.

Rational consciousness is a more complex form of consciousness that is non–automatic (in the biological sense) and is made possible by a special “data structure” called a concept. Concepts are open–ended categories based on similarities observed in reality and symbolized by words; concepts make fully developed human volition and natural language possible. Concepts are also the basis for a virtual entity called the “self,” but concepts as defined and used herein are not the same as those commonly used in the field of AI.

Simulated consciousness and its use of concepts as is described for this invention is not “model building” as that term is used in state of the art AI research, which uses a rationalistic method of designing AI systems.

In this invention, percepts are neither little pictures of objects nor mathematical models in the traditional sense, but rather they are identifications , sets of attributes of objects–in–the–world resulting from the calculations to make the properties and values of their attributes explicit, properties and values that are implicit in relationships in sensor outputs. Concepts are not words linked to arbitrary definitions in a database, but rather they are open–ended data structures that embody relationships calculated by a


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Introduction

specific process (to be described later) based on the differences and similarities of objects perceived by the system earlier; the result is that in this invention, natural language words mean the entire conceptual calculation chain, including the percepts of objects at its base, not just the concepts’ definitions. This is a very different arrangement from that found in extant systems in the current state of the art.

Note - This point cannot be over emphasized: The use of arbitrary definitions for creating “concepts” and “building models” is pervasive and the accepted method in our scientific culture. People do it unthinkingly; in fact, most people do not understand what it means to define a concept objectively, unless objectivity is explained in excruciating detail. The reason for this is that most people have never been exposed to Ayn Rand’s formulation of the idea of objectivity, a formulation which is new to epistemology, and has only been in existence since the 1960’s.8

Concepts formed objectively in the manner described later in this chapter make possible complex volitional interaction of the conscious with the subconscious and with reality in verbal form, interaction that involves asking questions and evaluating the answers. For example, stop reading for a moment and ask yourself the following:

• What is 2+2?

• What did I have for breakfast today?


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• Where am I?

• What is the cube root of 87?

• Why am I reading about consciousness?

In each case, your subconscious provided you an answer (or not) which you can judge as true or false, good or bad, relevant to what you are thinking about, important to your life, and so on, as Dr. Binswanger has pointed out. As an on–going process, this kind of verbal interaction between the conscious and subconscious processes in your mind is called thinking. 9 The simple natural language sentences of the questions are incomplete thoughts, but with the answers, they become complete thoughts.

The “Q and A” above and the process that produced it are neither practical magic nor the work of supernatural spirits; the phenomenon is caused entirely by goal– directed processes in your brain, each with a specific, limited identity. These processes can be simulated using a properly designed computer simulation system to enable them in virtual form.

To build a computer system that simulates life, consciousness, and thinking will require the design of a new kind of computer simulation system that does not exist today, a new system design that is not found in the current state of the art.

Note - When these new systems have been perfected, they will probably no longer be called “computers” or “machines” because they will


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5.1 Introduction

be recognized as a new kind of man–made system, one that is capable of manual behavior, and one that is yet to be named.

If computer systems are thought of as active, symbolic representations of reality, virtual reality, or reality simulators instead of accounting systems, databases, or word processors, it should become more understandable how computer systems can be designed that simulate the attributes and functions of life–forms to some degree of causal accuracy. Exactly what that degree is can only be determined experimentally after some are built and have been improved upon as new devices normally are. (Not many people imagined in the 1950’s that the sophistication found in today’s virtual reality systems would be possible only some 45 years later. (Jaron Lanier, VR))

Computer systems can be designed to simulate consciousness for the same reason they can simulate a jet airplane design in flight, the vegetative processes of life– forms, genetic evolution, or an entire eco–system: A good computer–based simulation system is an accurate and dynamic, symbolic reproduction of part of reality. In other words, the virtual entities and their actions in the computer correspond to the real entities and their actions in reality. Such a simulation is an objective formulation by human consciousness of the dynamic relationships between the objects involved in a given aspect of the real world, including their causal relationships, all of which are then translated into the form in which they operate in the computer simulation system.


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Computer simulation systems substitute variables, calculations, and logic for real world objects, their relationships, and causality. I call this process causality substitution.

As I pointed out in the Introduction to Chapter 1, it is well known in the state of the art that computer hardware and software can be functionally substituted for each other, that they can be equivalent processes in different forms (physical vs. logical). The idea of causality substitution is only slightly broader in scope; causality substitution includes not only computer hardware and software, but also the complex causality of teleological systems. The key is to duplicate the causal activity of goal–directed behavior using logic and virtual entities, just as state of the art software logic duplicates the mechanistic causality of computer hardware (or airplanes) to make possible the substitution of one form of a given process function for the other, of the virtual and logical for the physical and causal.

In other words, a simulation system is the objects and the relationships of some part of reality represented in a symbolic and a logical form as measurements and calculations, a form that is animated by a computer system as a mechanistic platform; this base or supporting part of the simulation system that embodies the invention described herein is governed by mechanistic causality and pre–defined by conventional object–oriented programming methods.

But the life and consciousness simulation system I am describing is also more than the hardware and software platform that animates it; it is the hardware and software plus the teleological interaction of the simulation system


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5.1 Introduction

as a whole with reality; it is more than the sum of its parts. The simulation system substitutes for and replaces the real biological processes (the causes and effects) of a life–form with measurements and calculations (in a logical and teleological form) that produce identical (or nearly identical) effects in reality. This is the part of the system that must be teleological and self–defining if it is to successfully imitate life and consciousness.

The key here is to identify the essential elements and program substitutions that need to be made; this is the pre–defined part of the simulation system. This aspect of the design involves identifying the necessary and sufficient set of elements to develop the substitutions for, and then writing the software code for those elements, making sure they can interact with reality teleologically. The self–defining stage of the development of the simulation system is the management and tutoring of those basic elements as they simulate the active processes of life and consciousness, and as they recursively operate on the simulation system itself.

The bottom line here is that to make simulated life–forms, a properly designed computer simulation system is substituted for the physics and chemistry that animates, and is the “platform” for, real life–forms, just as in other simulations, computers are substituted for and replace the physical systems that animate real, non–living objects, such as jet airplanes in flight. But in order to simulate life–forms, the system must also take into account and simulate the teleology, the goal–directed behavior of biological life–forms. This requires more complex software logic than is used in state of the art simulators.


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The important point to grasp is that the simulation of a life–form, and especially its attribute of consciousness, is not just a computer program alone. Rather, it is the correct design of a complete system of computer programs and hardware in continuous, teleological interaction with reality itself. The simulation program is only one part of a complex system of mechanistic and teleological causes and effects, just as biological life– forms are. This complex system interacts with reality to digitally reproduce as many of the relationships (both mechanistic and teleological) that exist between biological life–forms and reality as is technically possible.

Note - Obviously, the quality of life–form simulators will get better with improvements in technology according to Moore’s Law and other factors, as well as from the experimentation with various simulation system design strategies.

The programs in a life–form simulator must be designed to interact with reality much like real life–forms do. This latter point is important because much of what life–forms are capable of when they are mature comes not from some intrinsic property they have from birth, but from their causal interaction with reality as they live and function in the world, and the changes that occur in them as a result of that interaction. Information processed from such interactions is added to their memories as they learn their environment; the result is changes to the life–forms’ identities. And changes to what they are amount to changes in their action capacities for future interaction with their world (changes to what they can do).


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5.1 Introduction

The world that life–like simulation systems must interact with is reality; it is the Existence of which all objects, including life–forms, are an integral part.

Existence is the natural, the metaphysical, the primary; it just is in one sense, but in another sense when perceived in the form of objects, it provides the content, the data for consciousness to process, either automatically and infallibly as percepts or by choice and fallibly as concepts. 10

The content of consciousness, the content that is its data, is either perceptual or conceptual information. The conceptual information is the epistemological, a man– made, volitional form of information at the conceptual level of awareness; it is what it is partly because of its nature as part of reality (the identity of objects), but also partly because of the choices that were made by the people who formed the concepts, made up the symbols (words) that represent concepts mentally, reasoned out the logic, and invented the grammatical structures that enable the content to be represented in their minds in abstract form as sentences. This fact makes conceptual knowledge fallible: Some of the choices required to form concepts could be mistaken, could be non–objective; they could have been made in error. Conceptual knowledge must therefore be checked against reality to make sure it is correct, that its content and meaning accurately reflect reality. It is the infallible nature of perceptual knowledge that makes it possible to do so.11

Note - As to the validity of sense perception, the idea, for example, that a stick which appears “bent” when half submerged in water shows that


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percepts are inaccurate and fallible is wrong. This idea drops the context of the identities of light, water, and consciousness, focusing only on the appearance of the stick. But the identities of the light, the water, and the stick interact in a way that causes light passing through water to refract. When observed by human consciousness in that form (including the entire context), the stick is bent because of the refractive nature of water interacting with light. Our percept of it is, therefore, an accurate awareness of that fact of reality. This is a fact which must be taken into account when spearing fish in a stream, for example, and therefore has direct survival value, as well as philosophical and scientific importance.12

Objective conceptual knowledge, however, is certain and infallible and used in combination with percepts, is a powerful survival tool. Such knowledge is objective because it corresponds to reality and is therefore useful to select survival actions from alternatives.

Human beings can even reanimate information from memory to a degree by imagining their abstract ideas in action. That is how new hunting techniques, agricultural techniques, animal breeding, and new machines were probably invented by early cultures for example. (Don Norman, Things That Make Us Smart).

The process of the invention described in this book carries the chain of logic of animating information from memory in the imagination one step farther: It converts information about life and conscious processes from static, abstract description (in a natural language or a


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5.1 Introduction

mental simulation in a human imagination) into a form that can be executed on a computer system, making the information dynamic again, but in a different form that is more independent of a human mind. In addition, the invention includes the idea of adding teleological causation to the simulation system so its simulated life– forms can act for their own goals, instead of acting only for human goals.

In the current state of the art, computer simulations of new jet airplane designs, for example, automate descriptions of the planes that have been created by the human imagination, putting the designs into virtual operation to test not only the interaction of parts such as the landing gear, but the entire airplane in simulated flight, and even whether a person’s hand will fit into a small space to replace a part. This invention does the same for life–forms: It does so by simulating in specially designed simulation software some of the attributes of biological life–forms such as goal–directed behavior (teleology) and the processes of consciousness.

Experiments that simulate life–forms (known collectively as the art of Artificial Life (AL)) have shown that it is possible for computer systems to simulate various aspects of life processes, though all the work I have been able to find in the current state of the art uses non-teleological (mechanistic) causality to do so, and is therefore quite limited. And of course, the field of Artificial Intelligence (AI) has shown it is possible to automate some complex behaviors of humans and other life–forms, though also in very limited ways in the work done to date, and always to satisfy some human goal.


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Note - These experiments are well publicized and common knowledge in the AL and AI communities. For example, there is the Big Blue chess program and the Blue Eyes project done by the IBM Corporation, the “animats” of Pattie Maes of MIT, and the robots of Rodney Brooks, also of MIT. There are many other examples.

The work in AI especially, is not so much the simulation of consciousness as it is the automation of certain complex human behaviors, especially the attempted automation of reasoning. Even in the AI field itself, this approach has produced mixed results that have not been very life–like, though successful in very narrow domains.b

The IBM Corporation’s Big Blue chess program, for example, does not simulate a life–form, but mathematically calculates the consequences of various moves in the game of chess. In this way, it is not much different from any other large computer application program such as those that model complex financial systems or attempt to predict weather cycles. Big Blue does not simulate a thinking life–form or the process of choice performed by a human chess player, it is merely a very powerful forecasting program with the ability to automatically move chess pieces in reaction to moves by human players or other chess programs using a series of pre–defined actions activated by mechanistic causality.

Other examples of non–teleological AL and AI work in the current state of the art are the Blue Eyes project at the IBM Almaden Research Center in San Jose, CA, and a


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5.1 Introduction

robot called “Kismet” that has been developed by Cynthia Breazeal in Rodney Brook’s lab at MIT.b The Kismet robot is especially interesting because it has a “face” that resembles a human face and is capable of simulating human facial expressions such as “calm,” “disgust,” anger, “happiness,” and so on. Kismet makes these “expressions” in response to what it “sees” or “hears” with its camera “eyes” and microphone “ears.” While this is useful work in the field of robotics, and could in fact be used by the simulated consciousness of my invention to show simulated feelings in the real world, it is important to note that Kismet is neither conscious nor goal–directed. Kismet is a collection of hardware and software that automatically and mechanistically mimics certain human facial expressions in response to various scenes and sounds captured by its sensors using pre–defined actions. It does not simulate a life–form (except in appearance like an automatic puppet), it is not goal directed because it has no values of its own, nor the means to evaluate. Kismet is not conscious; it is mechanically sensing its environment and comparing those sensations to a database, much like a robotic welding machine or a waldo, but one that uses light instead of pressure for activation of its actions.

Rodney Brooks own robot, COG, is a similar example of the same mechanistic processes, and was the inspiration for Kismet.c COG is a mechanistic replica of some of the functions performed by the autonomic nervous system in a human. It is impressive, useful work, but it is not teleological. COG is performing its actions to achieve the values of Rodney Brooks, not its own.

This is not an artificial distinction: Rodney Brooks is a human being and as such must act to gain and keep values to maintain his own life, his own survival. One of his


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values is making COG act like a robot; this is not a “made up” value, but a real one possessed by Rodney Brooks: He gets paid for doing the work; with the money he can buy the food and other things he needs to survive; if he does not do so (or find some other means), he will soon be dead and cease to exist. COG, however, has no values because COG is a machine not a life–form, so it can take no action to achieve values it does not have. COG is an example of mechanistic, “billiard ball” causality operating in a very sophisticated machine using pre– defined actions activated strictly by mechanistic causality that has been pre–defined by a human being for human purposes.

What has not been shown in the work in AI or AL is the simulation of a complete life–form based on the theory of the teleology demonstrated by real life–forms, life–forms with consciousness as an attribute. That kind of simulation must be built around the goal–directed behavior of the simulated life–form, not that of its human programmers. This means the simulation must be internally powered and motivated by the value significance of its actions to its own survival. That type of design is not found in the current state of the art.

The description in the following sections will show someone skilled in the art of computer programming how to build a computer system that simulates the complex causal aspects of biological life–forms from the teleological point of view; it will also show how to simulate perceptual consciousness, conceptual consciousness, rational self–consciousness, volitional behavior, and natural language understanding at the level


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5.1 Introduction

of simple sentences; these are all attributes that are possessed by some biological life–forms, not by machines.

The most important word in the previous paragraph is the word “simulate.” Life processes in general and consciousness in particular are attributes of living entities, not attributes of machines. Strictly speaking, a machine such as a computer system can never be conscious in the exact same manner as biological life–forms because machines are not alive.13

The description in this chapter will show that life and conscious processes operate with a different form of causality than do machines, and that all we can hope to accomplish is to simulate life and conscious processes, to imitate them using computer technology to store teleological process relationships to reality that are similar to those that life–forms possess, and to then animate those process relationships to some degree of accuracy.

The other important issue to hold firmly in mind, as was mentioned at the beginning of this introduction, is that consciousness is a limited, quantify–able, causal process with a specific identity, and that identity determines what consciousness is capable of doing in reality as an attribute of a life–form. Consciousness is part of everyday reality and neither supernatural nor a transparent, empty non– entity. As with the objects that are its data, consciousness itself is identity; it is a collection of properties and values in the form of an active, teleological process with specific content that causes specific effects in reality.


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The design and functionality of the consciousness simulator described on these pages depends on this fact as its modus operandi. But before that description can begin, I must better differentiate the work described herein from the current state of the art, and it must be determined where the description of my invention should start, what processes should be described, and in what order.

5.1.2 Differences from the Current AI/AL State of the Art

It should be apparent to the reader by now that the invention described herein is designed in a way that is very different from the current state of the art AI and AL systems.

Most of the differences stem from differences in the invention’s theoretical basis. In fact, the invention is so radically different theoretically that I will deal with the major differences mostly in this section, and only mention them in a few other places through the chapter where they are particularly important to the reader’s understanding and to differentiate the details of the description. It should also be noted that to see the differences clearly, a significant investment must be made in reading and thinking about the passages cited in the primary references. In fact, it is strongly recommended the references be read in detail because the ideas they contain are not widely known.

State of the Art Concepts vs. Objective Concepts

I will describe the nature of concepts and how they are calculated in this invention in detail in the latter part of this chapter to make clear the specific kind of concept required for the invention to work; however, it is crucial


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Introduction

to the understanding of even the earlier parts of the description for the reader to differentiate the term concept as I use it in the following description, and the more common usage of the term in the field of AI because the two uses are incompatible.

In the context of this invention, I mean a very specific type of concept and only that type: I mean concepts as formed by the method proposed by Ayn Rand in her book Introduction to Objectivist Epistemology. Concepts formed by the Objectivist method are the only kind of concepts that this invention will simulate because they are the only kind that will enable the invention to work. In addition, Rand’s method provides for a specific and objective way of abstracting concepts into a hierarchy, and then validating that hierarchy; it is a methodology that is found in no other epistemology and is essential to the simulation of thought.

It is Rand’s method that makes concept formation and validation a calculate–able process that is based on and connected to reality, as opposed to being arbitrary constructs as are found in the current state of the art.

In the current state of the art in AI and other fields there are many ideas that have been advanced as to the nature of concepts and various “cognitively plausible processes that lead to new concepts,” such as induction, deduction, abduction, analytical reasoning, and so on. None of these ideas apply to this invention as they are described and in those contexts.

Whatever those processes lead to, they do not lead to concepts as they are defined by Objectivism and simulated in this invention. That is not to say that DLFs


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will never perform any of the processes listed in the previous paragraph, just that they will not use them as a method to form concepts.

If one studies the history of epistemology, one will find that no matter what their identity, all concepts ever formed fall into one of three categories as a result of how they are formed:

1. Intrinsic: Intrinsic concepts are formed and defined based on some attribute(s) that is said to be intrinsic to the objects the concept subsumes. The essence of the means of identifying these attribute(s) is never perceptual, but rather always by means of intuition or mystic powers of some kind. The Forms of Plato, many religious concepts, concepts of evil spirits, ghosts, and the concepts of philosophical Idealism are examples. No process or method is necessary to form this type of concept because intuition or mystic powers are emotional or supernatural by definition and do not require methodical processing; any method of cognitive consciousness one might use would therefore be superfluous.

2. Subjective: Subjective concepts are formed and defined by purely arbitrary means or by reasoning based on arbitrary means and assumptions; their formation and definitions are simply assumed, made up, or “word–smithed” until they are practical for some particular purpose. The concepts of modern philosophical Nominalism, the “spin doctors” of modern politics, some concepts of Scientific Materialism (such as Einstein’s famous comment about theories being “free creations of the human mind”),


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5.1 Introduction

and the many of the constructs in AI programs are examples. Since these concepts are arbitrary by definition, no concept formation method is possible.

3. Objective: Objective concepts are formed and defined by means of a method that is both based on sense perception and the specific and limited nature of human consciousness. Concepts are abstractions based on observed, measurable differences and similarities between objects in reality, or abstractions based only on earlier formed concepts which are ultimately connected to objects in reality. Reality in this context means a reality independent of, and primary to, human consciousness. No concept may be a “floating abstraction” because any concept that cannot be traced to its roots in sense perception by following the chain of earlier formed concepts down the conceptual hierarchy is arbitrary (as in non–objective), and therefore such a concept is invalid because it is disconnected from reality and has no specific context and place in the hierarchy of concepts; it has no meaning. In a system of objective concepts, no abstraction can be referred to as an actual or “real object” independent of sense perception, ever, and be a valid concept. Examples of how objective concepts are formed using this objective method are explained in detail in the references.14 They are also summarized in the section of this description that deals with how to simulate concept formation using simulated sense perception, simulated volition, and various forms of calculation.

Of the three ways of forming concepts, the objective concept formation method is used by this invention because it is the only one of the three types that is a


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method. To simulate concept formation on a computer system, neither mysticism nor generating arbitrary concepts at random will work very well.

Only an objective method that enables the calculation of concepts based on the similarities and differences in real objects measured in simulated sense perceptions can connect a simulation system to the reality outside of it in order to simulate an awareness of that reality, do so objectively, and give meaning to the system’s natural language words. That is why I chose the Ayn Rand’s method to use for this invention.

However, that choice also makes the ideas described herein theoretically incompatible with most of the rest of AI and AL, due to various contradictions between my theoretical premises vs. theirs.

Theoretical Differences

In the current state of the art, AI and AL systems are designed around the rule bases of expert systems, genetic algorithms, neural networks, contextual ontology spaces, and other similar computing techniques commonly used in the fields. For example:

Patrick Henry Winston is the director of the Artificial Intelligence Laboratory at MIT. His book Artificial Intelligence (Third Edition) is described in a Web page on the Internet as follows:

“Part I is about representing knowledge and about reasoning methods that make use of knowledge. The material covered includes the semantic-net family of representations, describe and match, generate and test, means-ends analysis, problem reduction, basic search,


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5.1 Introduction

optimal search, adversarial search, rule chaining, the rete algorithm, frame inheritance, topological sorting, constraint propagation, logic, truth maintenance, planning, and cognitive modeling.”e

All of these computing techniques (and more that are listed for the other parts of the book) were designed to solve various computer programming problems. None of them, however, has anything to do with life processes, survival values, or the alternative between life and death for biological life–forms, or objective concepts which are central to the invention I have been describing.

Doug Lenat’s Cyc system is another good example of state of the art AI technology as it developed through the 1980s and early 1990s:

“... During the 1984-1989 time period, as the Cyc® common sense knowledge base [Lenat&Guha 90] grew ever larger, it became increasingly difficult to shoehorn every fact and rule into the same flat “world.” Finally, in 1989, as Cyc exceeded 100,000 “rules” in size, we found it necessary to introduce an explicit context mechanism. That is, we divided the KB (Knowledge Base) up into a lattice of hundreds of contexts, placing each Cyc assertion in whichever context(s) it belonged.”c

This system, as with the previous example, is based on an enormous set of rules devised by human beings and their interpretation of how various aspects of human intelligent behavior works. However, it is not based on human consciousness as an attribute of a life–form, nor does it take into account the biological basis of consciousness, or the formation of concepts based on sense perception of reality.


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The Dynamic Memory of Roger Schank

Another well known researcher in the field of AI is Dr. Roger Schank, and one of his books that is representative of his views on AI is entitled Dynamic Memory.

The views on AI that Dr. Schank expresses in his book on human memory as he thinks it relates to AI research are consistent with those of other researchers in the state of the art. For example:

“We hypothesize that the basic entity of human understanding is what we have termed the personal script. Personal scripts are our private expectations about how things proceed in our own lives on a day to day or minute to minute basis.”d

Dr. Schank explains how he arrived at his hypothesis of personal scripts and other memory structures by studying various references in human psychology and why, in his opinion, these entities are a useful model for writing AI computer programs.

He goes on to explain how scripts and various other memory structure entities he and his research team have invented, such as memory organization packets (MOPs) and thematic organization points (TOPs) can be useful in both storing and processing information at various levels of generalization in both humans and computers. He also explains examples of various program structures he and his team have created to demonstrate his hypothesis.e

As with the other AI researchers who’s work I have cited, Dr. Schank is a Materialist. It is clear from his book that he believes consciousness is a form of information processing that can be replaced by a computer program. It


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Introduction

is also clear that the design of such a program can simply be invented by programmers using their interpretation of human language, memory, and thought processes, and that this interpretation does not have to be connected to the survival of a life–form.

Dr. Schank is a very traditional AI researcher.

The “Animats” of Patti Maes

Of all the people working in the fields of AI and AL, Patti Maes is a good representative of those who have been closer to using biological processes as a basis for developing computer technology that simulates life–forms she called “animats” in some of her papers on the subject and “autonomous agents” in others. The following is quoted from one paper she wrote that surveys some good examples from the AL field:

“The relatively new field of Artificial Life attempts to study and understand biological life by synthesizing artificial life forms. To paraphrase Chris Langton, the founder of the field, the goal of Artificial Life is to ‘model life as it could be so as to understand life as we know it.’ Artificial Life is a very broad discipline which spans such diverse topics as artificial evolution, artificial ecosystems, artificial morphogenesis, molecular evolution and many more. ... The goal of building an autonomous agent is as old as the field of Artificial Intelligence itself. The Artificial Life community has initiated a radically different approach towards this goal which focuses on fast, reactive behavior, rather than knowledge and reasoning, as well as adaptation and learning. Its approach is largely inspired by Biology, and more


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specifically the field of Ethology, which attempts to understand the mechanisms which animals use to demonstrate adaptive and successful behavior.”d

I have added italic emphasis to this quote in four places to indicate some key phrases:

• To “model life as it could be” is not to simulate life as it is, as it actually exists. The emphasis is on human understanding and other goals, not teleology, a fact supported by the very title of the paper which is Artificial Life meets Entertainment: Lifelike Autonomous Agents.

• To focus on “fast, reactive behavior” and “mechanisms which animals use” of an “autonomous agent” imply that mechanistic causality underlies the actions of animals, not the more complex teleological form of causality of biological life–forms. The term “autonomous” as used here, means “automaton” in the mechanistic sense.

Neither in the quote nor in the balance of the paper is there any consideration of consciousness as an attribute of these artificial life–forms, no description of values possessed by them, no explanation of goal–directed behavior based on the alternative of life and death from the perspective of the life–form, no description of objective concept formation, and no explanation of consciousness as a means of identifying reality for survival. There is only description of mechanistic causality operating as “fast, reactive behavior” in the form of computer software written to satisfy human desires.


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5.1 Introduction

The Unintelligent Robots of Mark Tilden

Another, somewhat different approach AL has been taken by Mark Tilden of Los Alamos Laboratory in New Mexico. Mark has designed a number of robots that behave remarkably like insects and other small life– forms.

The unique thing about Mark’s work, is that no computer software is involved, merely some simple, but interesting electrical circuits he designed. One in particular works as follows: “Within the core of the circuit, Tilden explains, transistors oscillate, thereby producing rhythmic pulses of electricity. A robot’s different walking behaviors thereby result from different rhythms as oscillators fall into sync.”f

Tilden’s robots are strictly simple electro–mechanical devices, and he explains their life–like behaviors as resulting from the mathematically chaotic behavior of their oscillating circuits. However, while these may be useful devices, that simulate how some life–forms walk, they are mechanistic, not teleological, and serve only to satisfy human goals.

Conclusions About the Current State of the Art

The approaches summarized above are representative of, and leading ideas in the fields of AI and AL in the current state of the art. Yet they are all designed around various computer system constructs or other mechanistic systems that have very little to do with the teleological processes that are the basis of biological life–forms or conceptual chains calculated from the similarities and differences of real objects based on the kind of sense perception that


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biological life–forms use; these are not examples of teleology and concept formation as a form of conscious identification of reality for the purpose of survival.

The reason for this is quite simply that the theories on which the current AI and AL state of the art systems are based, have very different roots in the genealogy of ideas from the ideas which form the theoretical basis of the invention described in this book. The fact is that most scientists are unfamiliar with the Objectivist ideas and their consequences in the fields of AI and AL, so they base their ideas on other theories.

The net effect, however, is that there is very little basis for comparison of the ideas in this invention and the current state of the art other than to say that they are very different and incompatible.

Design and Operational Differences in the Invention

The differences I have been describing are not only theoretical, but carry over into the actual operation of AI and AL systems.

Most state of the art AI systems have been designed by modifying techniques originally created and used for other computing purposes, such as databases, expert systems, or control systems. State of the art AL systems that have been designed to more closely imitate life– forms have, however, also avoided the simulation of teleology as it operates in biological life–forms.


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Introduction

For the design of the simulation system embodied in this invention, I have focused on theoretical consistency with ideas of the metaphysics, teleology, and epistemology of Objectivism, and that fact has had the following practical consequences on my simulation system design:

• The specification of causality as identity–action, that is, of the action capacity of objects stemming from the identity of objects, not the over simplified view that events are causes, the action–reaction explanation used by most scientists in AI and AL today.

• The specification of two forms of causality: • Simple, mechanistic causality

• Complex teleological causality

• The specification of the fact that computer simulations are not alive and that whatever their actions are, that those actions imitate teleological causation, but are not actually alive or teleological in the same sense that biological life–forms are.

• The specification of teleology as the essential differentiating factor of life processes from the simpler mechanistic processes that make them possible by serving as an animation platform, that life–forms are conditional and therefore have values (the ice cube vs. the earthworm), that they therefore must survive by gaining their values in order to act in the future, and the use of Dr. Binswanger’s three test criteria for goal– directed action: • That the action must be self–generated (meaning it is internally

powered and controlled).

• That the action must have value–significance to the agent performing the action.

• That the action must be caused by the value–significance of the action to the agent.


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• The specification of the fact that if simulations of life– forms are to be realistic, they must include the simulation of complex, teleological causality as is found in biological life–forms.

• The specification of the fact that, as a consequence of the above view of teleology, automatic biological action and action pre–definition have different meanings in this description than in the current state of the art: • Automatic action for this invention means the simulation of

goal–directed behaviors and instinctual behaviors exhibited by biological life–forms such as nest building or web weaving by spiders, or the automatic processes of sense perception in higher animals, and that this is not the same as computer automation of actions.

• Pre–definition of action for this invention means the programming of the means to effect actions such as finding and moving objects, finding and eating food, using sensors, and so on, but not the order or timing as to when or if these actions will be selected and activated by a simulated life–form; action selection and activation is teleological and based on value– significance to the life–form and its interaction with reality. This is different from state of the art AI and AL systems where every aspect of actions must be pre–defined using mechanistic causality, including their order and timing, and where the acting agents are mechanistic automatons.

• The specification that simulators are not conscious and whatever “awareness” of reality they simulate, that it is an imitation of biological consciousness, just as a mannequin is an imitation of human form.

• The specification of the simulation of consciousness as a reality based, teleological process that is a specific, finite, natural, identity as an attribute of a simulated life–form, an active process that uses the measurements of sensors as its only data and the identity of the sensed objects as its only form of content.


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Introduction

• The specification of simulated concepts as a calculated datatype in the simulation system based only on simulated percepts or other, earlier formed concepts, and the entire concept formation process is based on the method of objective concept formation as identified by Ayn Rand.

• The specification that it is the nature of concepts as produced by an objective method that uses optional, goal–directed actions that leads to the simulation of rational consciousness; and further, it is precisely because such actions are optional to the simulated life– form that leads to the emergence of the attributes of fully developed simulation of volition, self–awareness, natural language understanding and reasoning ability, and that these attributes derive from the nature of reality–based concepts, especially their hierarchy and context.

• The specification that natural language understanding in particular is the process of thinking, and as such, is primarily a survival strategy and means of more efficiently processing and storing information about reality; that natural language is only secondarily a communication system. And further, that the meaning of sentences is the connections embodied in the calculation chains of concepts formed by the Objectivist method. Moreover, the meaning of sentences is the entire chain of calculated concepts traced all the way back to percepts of the objects used to form the concepts in the first place, not just the definitions of words stored in a dictionary.

The reader will find it helpful to keep these differences clearly in mind as an aid to their understanding as they read the remainder of this chapter.


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5.2 Biological vs. Digital Life–Forms

In order to make the distinction between state of the art mechanistic automatons and the simulation of the teleological attributes of life–forms clearer, I have created the following explanation.

The processes and behaviors of biological (real) life– forms and the Digital Life–Forms (DLFs) that are simulated with a computer system designed to the specifications of this invention can be divided into layers according to their function in the overall simulation system. In other words, they can be broken into layers of subsystems, so they are easier to understand, as long as the layers reflect the natural dividing points in the information (somewhat like carving a turkey at its joints rather than cutting it randomly).

The higher subsystem layers are causally dependent on the lower ones. The practical effect of this is that the upper layers cannot function without the lower layers. In the world of life, a cell cannot function without DNA, energy, and chemistry, and an animal cannot live without cells; in the world of machines an operating system cannot function without computer hardware, and an application program cannot be run directly on the hardware without the operating system layer (assuming a modern computer). In the world of teleology, life process functions such as consciousness cannot function without cellular processes and food digestion, which in turn depend on the mechanisms of physics and chemistry.


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However, within subsystem layers, each layer has its own uniquely independent types of causes and effects; it is a system unto itself and can only interact with the other subsystems indirectly across some kind of interface or intermediary.

For example: In a computer system, one cannot mix application program functions (which are logical and digital) with electrical resistances and capacitances of circuit oscillations (which are physical and analog), without a processor and operating system to interface the interaction between these two different types of subsystems. Similarly, in biology, one cannot mix the functions of processes like blood circulation (which is mechanical) with cell reproduction (which is molecular and chemical).

In each of these cases, objects with certain specific identities at a certain scale interact with certain causes and effects, and those causes and effects, taken as a subsystem, collectively cause effects in other layers, also taken as a subsystem, though they cannot interact directly with each other; instead they act through one or more intermediaries or interfaces. This is true of both mechanistic systems such as computers and the entire life process. The processes of life, though based on mechanistic causality, are teleological processes that interact with the mechanistic world through specialized chemical reactions at the molecular level and sensors and muscles at the human scale.

Note - What this idea means in effect is that while some systems of different forms can interact, they may not do so directly. For example, the systems may contain dis–similar objects, the


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attributes of which are incommensurable, and therefore they cannot interact directly. The reason is that causation is enabled by the identities of the objects that interact, and incommensurable identities cannot interact with each other; a capacitance, for example, which is electrical and analog cannot directly call a digital program method, though that capacitance may maintain some value in memory for the digital program. A computer processor must serve as intermediary between the two incommensurable contexts of analog and digital to enable their interaction.

I introduced this idea in Chapters 3 and 4. Below, Table 5-1 reviews the layers of subsystems into which life– forms can be divided, as well as some essential functional similarities and differences between real and simulated life–forms.

The fact that various processes are on the same layer in the table does not mean they are exactly equivalent, but rather, for the purpose and context of this invention, they are functionally equivalent. They are similar enough in function so that they can be causally substituted for one another.

For example, in the lower three layers, while the computer system attributes shown are not the same as their biological counter–parts, in the context of animating a digital life–form, these subsystems perform similar enough functions, such as providing power and control, that within the context of this invention they can be considered functionally equivalent.


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Likewise with the upper layers; simulated perception, concept formation, and reason are not the same as in humans, just special, calculated imitations of these functions, but imitations that are similar enough to be useful to the digital life–form to aid in its simulated survival just as the real forms of these functions are useful survival tools for human beings.

Biological life–forms Simulated, Digital life–forms



Layer 5 Goal–directed Cellular Processes Simulated Goal–directed Behavior

Layer 4 Mechanistic Cellular Processes Digital life–form Simulation Program

Layer 3 RNA, Protein, ATP Synthesis Object-Oriented Prog. Environment



Table 5-1 A Layered Model of Complex Causality

To carry the analogy a step farther, just as a computer application program cannot function without the operating system and hardware in the layers below it that cause its operation, so the attribute of consciousness cannot function without the lower teleological layer 5 on which it depends causally either, or without the mechanistic layers of 1-4 that support layer 5.

Consciousness is an attribute of certain types of life– forms and is caused by their teleological processes; it is not a “stand–alone” process. Likewise, natural language and reason, which are the most complex behaviors in humans, are not stand–alone processes either, but are


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caused by and depend on conceptual consciousness, perceptual consciousness, and ultimately life processes operating in cells, to function.

To simulate complex human behaviors such as natural language and reason, all of the subsystems on which they depend must therefore be simulated as well.

5.2.1 Computer Systems vs. Teleological Systems

Computer systems and life–forms are two distinct categories of systems. The essential difference between them is that one is alive and the other is not; the form of causality that operates in state of the art computer systems is simpler than the form that operates in life– forms. Computer systems, as currently designed, are merely machines that automatically execute pre–defined actions worked out by human consciousness for human goals. Life–forms, on the other hand, are teleological; they have their own goals; they also initiate and sustain their own actions to maintain their very existence.

Note - The concept “causality” as used here means that the identity of an object determines its action capacity in relation to other objects, such as with the case of the difference between dropping a bowling ball and an air–filled balloon from a tall building: The two very different identities of these objects cause two very different effects. This view enables more detailed interactions to be described, as opposed to the over simplified action–reaction view of causality.15


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The form of causality that operates in life–forms is more complex than simple mechanistic causality because a life– form’s existence at any given time is dependent on all previous instances of its existence and the success of the survival actions it took in those instances , whereas the existence of a state of the art computer system or any other non–living object is not.

In other words, the existence of a computer system is unconditional; it may stop working, but it remains part of reality and can be restarted. Whereas, the existence of a life–form is conditional; furthermore, the conditionality is part of the life–form’s identity as a life–form. The condition is that the life–form must cause its own future existence (survival) precisely because it is a goal–directed or internally driven teleological entity, as opposed to a rock, which is not goal–directed; any actions of a rock are simply the result of outside forces. This is a metaphysical difference. Failure to attain its goals causes the life–form to cease to exist, a condition in which it is no longer part of reality and one that is irreversible. 16

For example, since state of the art computer systems use simple, mechanistic causality; if there is a causal failure in a program that makes it stop functioning, nothing happens to the program (in most cases) or the computer hardware, other than part of it stops working; it can be restarted later by human intervention. But if there is a causal failure in a life–form (sickness) and the immune system is unable to cope with it, the life–form not only stops working, it also ceases to exist. It dies; its physical form putrefies and disintegrates because life–forms are driven and supported from the inside, not the outside like all other kinds of objects. This is a profound and


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fundamental difference between state of the art computer systems and biological life–forms, between mechanical/logical systems and teleological systems.

Note - Computer systems are conditional in the sense that they are man–made, and man having volition could have chosen not to make them at all or to design them differently or choose not to repair them; but that is a different issue and not relevant to the point being made here. The same is true for computer code that ceases to exist if it is only stored in Random Access Memory and there is a power failure; the code is not alive and does not have the capacity to save itself (being controlled from the outside by humans), whereas life-forms do have the capacity to save themselves (within their functional and internal energy supply limits).

Any number of alleged counter examples can be created that supposedly disprove the conditional nature of life– forms is fundamental; the reason usually given is because other objects can be said to be conditional too, such as clocks with bombs as part of their structure or submarines designed to operate in acid and disintegrate if they stop maintaining their hulls, and so on. However, these mechanical examples miss the essential point about the conditionality of life–forms.

First, the conditional nature of life–forms is not a “design standard,” it is a metaphysical fact that anyone can observe for themselves. The design and function of life– forms is not determined by some arbitrary, revocable human choice, but rather by the basic nature of the life


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itself, and the requirements of the reality in which life exists and must ultimately survive. Life is an objective fact of reality.

Take the robot submarine example: The main problem with this example is that the submarine is a man–made machine, a human–goal–directed object, not a self–goal– directed object.

The reason the submarine is not a goal–directed object, is that it operates conditionally only because human beings made it that way, and they only made it that way so it could act to further human values; those values are external and not the submarine’s own values; it has no values of its own. The conditionality is contrived , not natural.

The reason life–forms are conditional is because they evolved that way, naturally. The reason life–forms are goal–directed is because they are conditional: They are naturally and intrinsically unstable and will cease to exist without continuous, internally caused action. Biological life–forms’ internal energy source, means of action control, and values came into natural existence before the life–forms did as extensions of mechanistic causality; these complex causal processes are what make life–forms possible in the first place. They are not the same as conditional processes invented by human beings for the sake of argument.

The second way machine counter–examples miss the point is that the biological function of the survival vs. death alternative for life–forms is not just some non– essential attribute they happen to possess, but one that goes to the very core of their existence: Conditionality is a means of natural behavior selection that works by


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wiping out the life–forms with any behavior that is not survival efficient. Life actively uses conditionality to naturally alter identity (remember, identity determines action capacity); the result is only life–forms that encode efficient survival behaviors exist over the long–term. Conditionality causes evolution.

To illustrate the nature of conditionality, Dr. Binswanger quotes biologist Albert Lehninger as follows: “A living cell is inherently an unstable and improbable organization; it maintains the beautifully complex and specific orderliness of its fragile structure only by the constant use of energy. When the supply of energy is cut off, the complex structure of the cell tends to degrade to a random and disorganized state.”17

The nature of life–forms is that they are conditional objects, and their continued existence depends on the condition that they themselves continue to act to attain the goal of survival, to cause their own future survival, by internally powered and controlled causation. Hence Ayn Rand's definition of life as “self-initiated, self-sustaining action” as explained by Dr. Binswanger.18

The bottom line on this issue is spelled out by Dr. Binswanger as follows:

“Life and goal–directedness are intimately related. For consider the three requirements of goal–directedness: self–generation, value–significance, and goal–causation. Each implies the others, and all are a consequence of the essential nature of life: conditionality. Self–generation means there is an internal store of energy. This store must be replenished–hence the need to obtain energy–hence the phenomenon of value–significance. What underlies goal–


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causation? The fact that only valuable actions get repeated. Why do only valuable actions get repeated? Because the value here is survival value, and to repeat the action, the agent must survive.”19

Robotic vs. Goal–Directed Causality

The previous section notwithstanding, what if people design an artificial life–form using biological components instead of a computer simulation? Would such an entity be teleological even though it has been designed by human beings?

The answer is “yes.” It is true that being designed by humans would mean that to some degree, the artificial life–form would share human values because the humans who designed it would want it to do certain things or they would not have built it in the first place. But if it is a real life–form, that is, if it is alive, it must be conditional because that is the essential attribute of all life–forms. The artificial life–form would therefore be goal–directed, which means be internally driven by its own values, energy source, internal locus of control, and the value– significance to itself of its own values. The fact that it also shared some values with the humans that built it is irrelevant:

The essential facts are that it has values, it acts to gain and keep them on its own power, and its survival depends on it doing so.

Note - Nanotechnology may someday make this possible, as pointed out in the previous chapter.


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To build a simulation of mammalian life and the most complex attribute of biological life–forms, conceptual, rational self–consciousness, the causal context of the rest of the life–form cannot be dropped, especially its conditionality and goal–directedness. As an attribute of a life–form, consciousness cannot exist without the complex, teleological causes that make it possible, any more than it can exist without the mechanistic ones that underlie those; the causes on which conceptual self– consciousness depends are all the layers of causes, all of the subsystems below the top layer as shown in table 5-1.

Based on the foregoing explanation, it is now possible to see where the description of a consciousness simulator must start. It is the nature of teleological causality that determines the starting point, because this is what serves as the interface between the mechanistic causality that rules in the subsystem layers below it, and the conscious behaviors that operate in the layers above it.

Just as various mechanistic cellular processes such as RNA, protein, ATP synthesis, DNA processes, electro– chemical processes, physical molecular processes are the mechanistic basis of the goal–directedness of biological life, there must be some mechanistic processes to be the basis of the goal–directed processes of simulated life.

The point of this discussion is not to show that machines can exist conditionally (which they cannot), but to show that in order for life processes, consciousness, and volition to be simulated, the conditionality that occurs naturally and controls behavior in biological life–forms must be emulated by a simulation system if it is ever to


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Biological vs. Digital Life–Forms

simulate intelligence. The design strategies and logical processes that work for mechanical automatons will not work for simulating life–forms, only teleology will.

This invention is not a “conditional machine.” It is a new kind of virtual entity that emulates the conditionality attribute found naturally in the identity of all life–forms, and it does so by simulating the complex teleological causality of life.

Computer vs. Teleological Action Definition

Causes are not causes without effects, and effects in the context of life are the actions of some teleological agent acting on some object(s) in reality in order to survive.

There is a huge difference in the way state of the art computer agents (automatons) and goal–directed agents as defined in this invention operate:

• Computer agents and other state of the art automatons are all externally driven. They do not have values, they do not have their own energy source or internal control, they do not have to survive by their own actions; state of the art computer agents are completely defined and controlled by the parameters of their programs to implement human values. Any situation that puts them outside their pre–defined parameters will necessarily cause them to fail to operate.

• Life–forms and simulations of them as used in this invention are internally driven. Teleological agents are driven to act by their own internally held values, with their own internal energy supply and means of controlling it. Moreover, the actions they take are primarily and necessarily on their own behalf (though


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they may also coincide with human values); the purpose of those actions is to maintain their own existence, to survive. Only after survival is assured can teleological agents engage in optional actions: This is because without survival, the agents do not exist, and non– existent agents cannot act. Moreover, it is why only survival actions are necessitated; survival actions are necessitated precisely because they are required for survival (and hence cause all future actions of any kind); whereas optional actions are not necessarily part of the causal chain required for survival, hence the term optional.

These primary and fundamental differences in the nature of acting agents lead to some additional differences in the way actions occur in computer agents vs. teleological agents in:

• The way actions are defined : Actions may be defined by human programmers based on knowledge of life– forms (so evolution does not have to be recapitulated), or by simulated life–forms themselves based on their survival needs and by stringing simple actions together into complex actions.

• The way actions are selected: Actions are selected by simulated life–forms using their life as the standard to calculate and prioritize the possible alternatives, and then the action with the greatest potential survival value is selected first, followed by others of lower priority.

• The nature of automatic actions: Automatic action selection is by means of a goal–directed, simulated pleasure–pain system that simulates that biological subsystem which evolved to safe–guard the lives of biological life–forms by providing rapid calculations of


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the relative value of objects perceived by a life–form and communicating the results to the consciousness of the life–form with pleasure and pain feelings.

• The existence of “optional” actions: When survival needs are met, and only then, does the survival value of actions calculate much closer to being equal because the life–form has built up some energy reserves and there are no immediate threats to its life. Optional actions that are not part of the causal chain required for a life– form’s survival, and hence it matters little, if at all, which one a life–form selects and performs, though these actions can become crucial to survival in the future of a life–form.

State of the art AI and AL computer programs are not designed in the manner just described.

5.2.2 The Starting Point for Describing the Invention

Just as it is necessary to start with the hardware, then move to the operating system, application programs, and so on to build a state of the art computer system, it is necessary to start by simulating the electro–chemical and physical process of life–forms, followed by all the layers of subsystems to build a simulation of consciousness. The layers of subsystems not only contain implementations of all the necessary and sufficient concepts that must be in the system in order for it to work, but the layers must be causally linked as well.

This can be accomplished by causally substituting and linking a computer simulation system shown in layers 1-4 of Table 5-1 to animate the objects and relationships being simulated, namely the digital life–forms in layers 5-


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7. The animation is of a different form than its biological counter–parts, but it is functionally similar enough to serve the purpose of this invention.

One cannot run a modern application program directly on computer hardware, leaving out the operating system layer; it is necessary to proceed through all the subsystem layers in between without leaving any out to have an unbroken causal chain that connects simulated consciousness to reality.


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Let’s look at Table 5-1 again, this time with the necessary

Biological life–forms Digital life–forms

Layer 7 Conceptual Consciousness (Reason) Simulated Conceptual Consciousness:

open-ended categories formed by

calculating differences and similarities

using perceptual measurement ranges, symbolized by natural language

words, connected to percepts through

calculation chains of other less

abstract concepts, volitional actions,

virtual self for self–consciousness

Layer 6 Perceptual Consciousness Simulated Perceptual Consciousness:

automatic feature identifi cation, unit

economy, reality interface, physical

actions to cause changes in reality

Layer 5 Goal–directed Cellular Processes Simulated goal–directed behavior:

automatic self-regulation (in the

biological sense), self-generated

energy, values and value signifi cance,

survival, optional behavior





and sufficient concepts added.

In the case of digital life–forms, this means simulating the more complex form of causality on which they depend for their existence, goal–directed behavior (layer 5), followed by perceptual consciousness (layer 6) and conceptual consciousness (layer 7), to simulate rational self– consciousness, a capacity that is an attribute of human beings. It is only in this way that a consciousness


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simulator can be built that will come as close as possible to behaving like a biological life–form that mimics some human–like traits.

Moreover, to simulate consciousness in a manner that resembles what is observed in higher animals and humans (a goal directed process), a teleological program (layer 4) must overlay the mechanistic state of the art computer system (layers 1-3) to serve as an interface between the two forms of processing because they depend on different logic: the mechanistic, logical computer processing (layers 1-3) and the inherently conditional, teleological form of processing (layers 5-7) of life and consciousness.

Note - Layer 5 also makes optional behavior possible, and it is optional behavior that both makes concept formation possible and forms the foundation for volitional behavior, or free will, in digital life–forms. The “how” of this will be explained in detail later in this chapter.

Just as the analog world of the electronic components of a computer cannot interact directly with the digital world of its programs without the processor as the intermediary, so the mechanistic world of the digital program cannot interact directly with the teleological world of a simulated life–form without the teleological program as an intermediary or interface between mechanistic and teleological process logic.

Now that it is clear where the description of the invention must begin, I can continue and describe how to build a computer system to simulate consciousness as an attribute of a simulated life–form.


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The content of the description depends on the axiom that consciousness is a metaphysical primary, that it is a relationship between a certain type of life–form and reality that cannot be broken down into simpler components in order to reduce it to mechanistic causality, and that this is true even though consciousness is ultimately based on mechanistic causality. Consciousness is a relationship that either exists or not, period.

Consciousness as a state of awareness just is, is what it is (A is A), and is conscious. It is an attribute of certain life– forms; it is the relationship of awareness of reality as a collection of objects as opposed to awareness as discrete sensations or to non–awareness (meaning “nothing” or “void,” not a contrary kind of relationship). Consciousness is one of a number of relationships that the life–forms that possess it have with the world.20

The content of the description is also based on the corollary axiom that consciousness is causal, that it is a process with a specific identity that interacts with the identities of other objects in specific ways; in other words, consciousness is an active, limited, teleological process like life itself.21

Consciousness is caused by one set of teleological subprocesses, and life is caused by a different set, but both phenomena are caused processes. When some of the subprocesses of awareness are active, consciousness exists; when they are not, a life–form which has the attribute of consciousness is unconscious. This is analogous to the way a life–form is alive when its teleological life subprocesses are operating, and dead when they no longer function.


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The description of my invention will show that to simulate automatic consciousness amounts to enacting its biological causes in the form of measurements and logic in a teleological computer simulation system that has an interface with reality that is as causally equivalent as is technically possible to that of a biological life–form. In other words, the state of simulated consciousness will be an attribute of a simulated life–form in a simulation system.

In addition, the description will show that simulated rational consciousness is a virtual subprocess of simulated consciousness that emerges from simulated automatic (perceptual) consciousness reorganizing itself with “data structures” called simulated concepts that are formed using optional mental actions. Further, the description will show how optional mental actions, the identity of concepts, and their logical relationships make simulated volition and simulated natural language possible.

Finally, the description will show how the repeated use of simple, first level simulated concepts leads to the emergence of simulated self–consciousness, how this latter leads to the simulated ability to process simple natural language sentences, which in turn enables the simulation to achieve the power of simulated volition and the capacity to initiate first causes.

In other words, the description will show how the simulation system bootstraps itself.


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A System Design for Simulating Conscious Life–forms

5.3 A System Design for Simulating Conscious Life–forms

If a life–form simulator had to be designed by simulating all the mechanistic process on which life depends, such as various chemical and molecular processes, the DNA processes, the RNA processes, protein synthesis processes, ATP cycle processes, and so on in exact detail, building such a simulator would be very, very difficult indeed. Luckily, this difficulty can be avoided by using something called causality substitution.

I explained this topic briefly in previous chapters, and will describe it in more detail in this section.

5.3.1 A Computer Network Analogy

In the field of computer networking, there is a model for network design specified by the International Standards Organization called the OSI model. The OSI model is a layered model similar to the one shown in Table 5-1 for life–forms in that the upper layers depend on the lower ones, except that the OSI model, shown in Table 5-2, describes network technology.22

The OSI model is interesting in this context because it provides for something called layer substitution, a concept that is very useful for network design and which I have extended for use in the field of life–form simulation.

The bottom two layers of the OSI model are the Physical layer and the Data Link layer. These subsystems and how they are used in network design is the idea that led me to think of the idea of causality substitution. So that the reader may understand how I arrived at this idea as well,


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we will make a brief digression with an example of how layer substitution works in the field of network design before continuing.

The example is as follows: In networks that follow the OSI model, the Physical layer is the network cable (or other medium such as radio frequency or infra–red light), along with its interface card that physically puts the binary bits on and off the media, and the Data Link layer is the software program that sends and receives data packets from one network node to another using a protocol such as Ethernet or Token Ring or some other proprietary network protocol.

Each layer in the model causes the functions that occur in the layers above it.

Layer Name Function

7 Application Layer Program-to-program communication.

6 Presentation Layer Performs data representation conversions.

5 Session Layer Opens and maintains communication channels.

4 Transport Layer Maintains end-to-end integrity of the data transmission.

3 Network Layer Routes data from one node to another.

2 Data Link Layer Transmits bits as packets from one network node to another.

1 Physical Layer An interface card that puts data on & off the physical media.

Table 5-2 The OSI Reference Model

There are various kinds of network cables such as twisted pair, coax, or fiber optic, and there are other media such as radio frequency or infra–red light, each requiring a different computer interface card.


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5.3 A System Design for Simulating Conscious Life–forms

Layer substitution in network technology allows for the mixing and matching of these different technologies, depending on the needs of a given network application.

For example, a company using a proprietary network protocol may have twisted pair cables; they may wish to replace these with fiber optic cables which have a much larger capacity, effectively substituting one type of Physical layer for another. To effect the change, they would replace their twisted-pair cable with fiber optic cable. They would also need to change the interface cards because fiber-optic cable uses light to carry the bits, whereas twisted pair uses electricity.

They may run their original, proprietary network protocol at this point and their network would function, but they may also want to change to the more common Ethernet protocol. In order for the network to function with this second change, the Data Link layer must be substituted in addition to the physical layer. This is necessary because different software is needed to manage sending Ethernet data packets from one network node to another vs. whatever software routines their proprietary protocol used.

Once the substitution of the Data Link layer software is made, network users would probably notice better performance as the only difference, because the upper five layers of their network as specified by the OSI model would be unchanged, and their network would continue to function as before, only faster. The substituted bottom two layers simply cause the same effects as before in the upper network layers, but in a different manner, one that uses a different kind of network packet and uses light instead of electricity to physically transfer the data bits.


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In other words, one set of causes is substituted for another set, which lead to the same effects in the overall system, and this is accomplished by substituting layers or subsystems.

5.3.2 Substituting Layers

In a similar manner, I have extended this idea to build and animate a life–form simulator using a computer simulation system.

A causality substitution can be made to simplify the simulation of the mechanistic processes that make biological life possible in the real world; instead of attempting to simulate chemical and other molecular processes directly, standard mechanistic computer code is substituted to run the teleological software, which in turn animates the simulated life–form.









As shown in Table 5-1 above, to accomplish this goal, the mechanistic computer causality of the bottom four layers of digital life–forms is substituted for the biochemical mechanistic causality of biological life–forms.


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A System Design for Simulating Conscious Life–forms

The fact that the mechanistic processes involved operate differently is irrelevant in the same way and for the same reasons as in the computer network example. It is not the differences that are important between the biological processes in say, layer 3 of ATP synthesis and an object– oriented programming environment, it is one key similarity that is important: The fact that both of these processes are mechanistic; the same is true of all the other processes in layers 1-4. The processes on both the biological and DLF sides of the table are mechanistic processes that serve to animate the life–forms. That fact makes them functionally equivalent for the purposes and in the context of this invention.

Hence, one set of causes can simply be substituted for another set, which have the similar effects at higher levels in the system, namely the effects of animating it. The substituted causes and effects function in a manner that imitates or is similar enough to the biological causes and effects in the final outcome that, for the purposes of the simulating life–forms, one can be substituted for the other. As with computer networks, it makes no difference causally whether you use fiber optic cable and Ethernet or twisted pair and Token Ring (two very different systems), the information packets still get sent transparently from computer A to computer B as far as network users are concerned.

So far as I am aware, this concept of causality substitution is a new idea to the field of AL. The point of the concept is to demonstrate a means of simulating the animating effects of a tremendously complex molecular chemistry by means of using a computer system to supply the causes.


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In biological life–forms, molecular and other mechanistic cellular processes cause energy to be generated and stored. This animates the life–form on a short–term basis. In a similar manner, the electrical energy of the computer hardware, the functions of the operating system, and software application environment and its programs will animate digital life–forms on a short–term basis just as molecular processes do in biological life–forms. Causality substitution makes the animation possible using existing computer technology so the digital life–forms can be made to imitate or function as much like biological life– forms as is technically possible without using chemistry.

The more interesting processes to simulate are those in the teleological, goal–directed behavior layers that sustain energy generation and life–form animation over the long– term. These involve more complex forms of causality. They are the subject of the next section.


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Setting Goals in a Computer Simulation System

5.4 Setting Goals in a Computer Simulation System

Having explained the substitution of the lower layers in the model shown in Table 5-1, it is now time to describe the processes in detail that occur in the upper layers that simulate the actions of biological life–forms and that make the long–term “survival” of digital life–forms possible.

This section and the other sections that follow contain additional new ideas not in the current AI or AL state of the art, but they are ideas that a skilled programmer can implement on any reasonably powerful desktop computer, using an object–oriented programming environment.

5.4.1 What is Goal–Directed Behavior?

On the one hand, it should be noted that the goal–directed or teleological behavior of life–forms is caused behavior, not some kind of intrinsic vitality or other sort of miracle. On the other hand, goal–directed causes are not merely the simplistic, “billiard ball” kind of mechanistic causes either: The causes of goal–directed behavior are of a different form that is more complex than mechanistic causes, but still, it is important to understand that teleological causation is based on mechanistic causation and could not exist without it, just as a computer network cannot exist without its lower layers that put binary bits on and off some wire or other physical media. The essence of goal–directed behavior is that it is a form of causality that makes possible the existence and animation of conditional objects: life–forms (as opposed to the mechanistic causality by which everything else in


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existence operates, and which also serves as the foundation for teleological causality as shown below in Table 5-3).

Mechanistic Causality Teleological Causality

Objects existence type Unconditional: objects will

exist until natural erosion or

other mechanical, chemical,

or nuclear processes break

objects down into simpler

parts. They may last seconds

or billions of years.

Conditional: life-forms will die

and cease to function

immediately if specifi c

conditions are not maintained

or at the end of a predictable

life-span, then putrefy within

hours of death and

disintegrate in days to weeks.

Source of action External Internal, self-generated

Need for action None Must act to remain alive and

in existence, self-sustaining

Source of energy None or external (excluding

nuclear reactions as in stars)

Internal, but derived from an

external source by own action

Locus of control None Internal

Values and needs None Value energy and other

things their conditional

existence requires to sustain

Value signifi cance of

environment

None Crucial: without gaining values such as food and

avoiding disvalues such as

predators, death and non-

existence result very soon.

Goal None Survival: which is achieved by means of continuous

internally generated,

sustained, and controlled

action to gain and keep

values and avoid disvalues.

Table 5-3 Mechanistic vs. teleological forms of causality


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A goal–directed process is self–generated as well as self– sustaining, which means it must be internally powered and controlled. It causes its own future existence in spiral fashion: A life–form’s actions in the present cause its survival in the next instant, and its survival enables it to act again in the following instant, and then the cycle continues, so each instance of action can be said to cause all future instances of action. Just one break in that chain, and the life–form will die and cease to exist.

Mechanistic causality, on the other hand, is a simpler form because the existence of the natural, non-living objects it enables to operate exist unconditionally; non– living objects just are. Causes do not have to be continually enacted to keep non-living objects in existence, to keep them part of reality. In addition, the actions of non–living objects are externally powered and controlled only by the mechanistic laws of chemistry and physics; they have no internal means to act (unless put there by humans as in the case of powered machines).

Note - Time–frames vary and are relative to the type of object as to when natural non–living objects will break down into simpler ones. Notice, however, that they have no capacity to prevent the break down, whereas life–forms do (recall the example of the ice cube and the earthworm). Some non–living objects can even “grow,” such as crystals, fires, or storms, but these changes are driven from the outside, not controlled from the inside as they are with life–forms. Stars are internally powered by nuclear processes, but they are certainly not alive.


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According to biologist Walter Bock, life–forms have three essential characteristics:23

1. “Living organisms take in materials and energy from the environment.

2. They use the appropriate materials and energy for self– maintenance, self–repair, and self–reproduction.

3. Once they have died, they cannot be reconstituted – failure is irreversible.”

In other words, life–forms exist on the condition they take the actions necessary to cause their own future existence (survival), and they cause their future existence by enacting the causes listed in items 1 and 2 above. As pointed out earlier, life–forms are inherently unstable and must continuously act to maintain their existence.

Since life–forms need materials and energy to survive and only certain specific types of these items will cause survival (others cause death), some things in any environment are values life–forms must act to acquire, and other, alternative things are disvalues to be avoided. Goals, therefore, are values to be acquired (values that are not in hand); they are the standards or criteria for action selection needed for survival, to get the life causing things in an environment. These are the things that have value–significance to life–forms based on the alternative of value vs. disvalue, which translates into life vs. death.

To review the list Dr. Harry Binswanger has identified as the conditions for goal–directed action that I introduced earlier in this book:

1. “The action is self–generated.


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2. The action’s goal has value–significance to the agent.

3. The action is caused by the goal’s value–significance to the agent.”

Note - The term “agent” means teleological agent, as in life–form or some major system thereof, not mechanistic automaton as is does in the current state of the art.

It is these three conditions that distinguish the goal– directed actions of life–forms from the mechanistically caused actions of non–living objects and computer automatons. Life–forms’ continued existence depends on values, the attainment and maintenance of which are caused by their own actions. The result is survival if the actions succeed, and death if they do not.24

5.4.2 Interfacing Computer Systems to Value Systems

Computer systems as they are designed in the current state of the art are machines; they are not alive and do not have values. They are governed by the simpler, mechanistic form of causality.

How then, can conditional objects who’s existence depends on values be simulated on a mechanistic computer system? They can be because the more complex causality of conditional objects depends ultimately on combinations of unconditional objects and mechanistic causality: non–living molecules and the laws of chemistry and physics that govern them. All teleological systems are based ultimately upon mechanistic, logical systems.


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That this is so can be shown by a simple existence proof: It can be inferred from the fact that the goal–directed behavior of biological life–forms is already “implemented” on the unconditional molecules of chemistry and their mechanistic causal processes. The fact that life is based on (not reducible to) physics and chemistry, means life is based on non-conditional objects and mechanistic causes and effects at its lowest levels. Life exists, so as a corollary, an interface between goal– directed causality and mechanistic causality must also exist for biological life–forms.

Humans form concepts and use language to represent reality symbolically, then manipulate the symbols with logic to produce objective descriptions of reality in the form of language. One purpose conceptual tools are often used for is imagining or mentally simulating parts of reality. When done by humans, this is a slow, manual process consisting of many, many choices about the properties and values of the objects being simulated and their relationships. Computer simulations are the automation of that process using symbols.

Computer systems are causality simulators that can simulate any aspect of reality using man–made symbol systems that have been translated into binary arithmetic and are manipulated by electricity. Computer–based simulations can be made to operate at whatever level of detail is desired.

Moreover, there are no restrictions as to which ideas, which conceptual, symbolic representations of reality can be simulated given sufficient computer resources and


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time, though some can be simulated better than others. The objective description of goal–directed causality is one such man–made symbol system.

It is possible, provided teleology is used (rather than ordinary logic), to write a computer program to serve as an interface layer between the goal–directed causality of digital life–forms and the mechanistic causality of the computer systems that animate them. Such a program is the symbolic analog of the natural interface between teleological and mechanistic causality that exists in biological life–forms as part of a living cell, and it can enable a computer simulation system to imitate teleological causality.

Writing a computer program to simulate goal–directed behavior is a matter of reproducing the teleo–logic of the objective description of the complex form of causality evident in life–forms, including their values and the fact of value–significance, and building it into the computer simulation program so the system is functionally similar (or functionally equivalent, if technically possible) to the real process observed in biological life–forms. Doing so creates an interface between the two forms of causality, and when implemented on a computer simulation system, it effectively imitates life processes by substituting the mechanistic binary causes of the computer simulation program for the mechanistic molecular causes of real life–forms. (This means in effect that, in Table 5-1, layers 1-4 in the right column are substituted for layers 1-4 in the left column.)

The result is still a simulation, the manipulation of symbols in a computer system, not a life–form, but a teleological as well as a logical manipulation that imitates


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life processes. A digital life–form is a virtual object (an object in the form of symbols, of information and its relation to human consciousness) and not a real life–form, but its behavior can be made very similar to that of a biological life–form if the teleological causality is duplicated by the logic of a computer program, plus the digital life–form’s interaction with reality. A properly designed and programmed digital life–form will satisfy Dr. Binswanger’s three conditions for goal–directed behavior cited earlier, and by doing so, will be capable of causing its own future survival just like a biological life– form.

Of course, an objection could be made that this causal process is unnecessary, since the Digital Life–Form’s (DLF) simulation data could be backed up, and used to simply “bring it back to life” if the DLF “died.” And while it is true that this is possible, doing so would undermine the main benefit of conditionality: namely, its ability to drive independent action and to eliminate anti– life behaviors.

Simulated “death” is the primary means this invention uses to solve the problem of the apparent need to pre– define a simulated life–form’s future actions. Simulated “death” solves this problem because only pro–life actions get repeated in the long–term.

5.4.3 Goal–Directed Simulation Logic: Teleologic

Conditional causes are not new to the art of computer programming. Programmers use conditional programming structures all the time. To simulate a life–form on a computer system requires only that the conditionals that are part of the computer system be used in a manner that


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simulates the way biological life–forms operate so that the simulated life–forms behave as close to real ones as is technically possible. Their primary purpose cannot be to achieve human goals, which is how conditional programming structures are used in all state of the art computer programs, but the goals of the DLFs themselves.

In other words, the use of conditional causes in a program to simulate a life–form must also simulate values and value–significance to digital life–forms, as well as internal energy and its controlled use for survival. It is these ideas that provide digital life–forms with the capacity to select their actions and the motivation to use that power: Upon “pain” of simulated death.

This means recreating the inherently unstable nature of life using conditional programming structures as a basis for making virtual life–forms that are themselves inherently conditional objects, and therefore able to simulate the conditional nature of real life–forms. So DLFs must be logically structured to take action to maintain their existence, and that they must be deleted if their survival actions fail.

Of course, it could be objected that unlike biological life– forms (except humans), DLFs could learn to manipulate the computer system they run on and prevent their own “death,” or they could learn to “resurrect” themselves from backed up files containing their data. It is unlikely that human simulation system managers might need to take steps to prevent these kinds of actions, though it is somewhat early in the development of DLF Simulation Technology to know this for sure.


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Goal–directed behavior of biological life–forms involves the repetition of certain actions such as finding and eating food (a value), avoiding predators (a disvalue), maintaining vegetative functions like digestion and blood circulation (a value), and so on. The goal is survival for the life–form (the source of value–significance), the causes are the actions taken to attain that goal.

The process and the life–form it sustains is conditional because if the actions are taken (causes are enacted), then the life–form survives to act again in the future; if such actions are not taken or they fail for some reason, then the life–form dies (the process stops breaking the causal chain) and the life–form ceases to exist in the future; it has stopped causing its own survival. The need for action is a primary attribute of living objects.

The process, described by Dr. Harry Binswanger, that needs to be programmed to simulate the goal–directed behavior of a life–form is therefore as follows:25

Life–form Identity --> “action1 --> goal1 --> survival1 --> action2 --> goal2 --> survival2” --> actionn --> goaln --> survivaln and so on, until the end of its life–span.

Note - Read the arrow “-->” as meaning “leads to.”

Each instance of survival occurs at a later time and is caused by the action with the same number subscript. The sequence can be thought of as spiraling into the future, causing the life–form’s own future survival. If this sequence is interrupted in a biological life–form, it dies and ceases to exist. A simulated life–form must likewise


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be erased from the computer program (or otherwise made inactive) to simulate its death and status as a conditional object.

Notice the source of the causal complexity here:

• Whereas the mechanistic causal processes of non–living objects involve a simple Object Identity --> Some Action sequence;

• The complex, teleological causal process of living objects involves not only the first step (1) the Life–form Identity -->> Action sequence, but also (2) a goal, and (3) maintaining the state of survival in every instance (meaning the goal is successfully achieved and the causal chain is unbroken). In addition, teleological causality is a cyclic process, with each new cycle depending on all the previous ones to create the spiral effect as time passes.

Teleological causality is more complex not because of some intrinsic “vitality” or supernatural power, but because it involves additional simple steps in a sequence that must be continuously maintained, as opposed to the simpler “Identity -->> Action” sequence of mechanistic causality. The additional steps explain the behavior observed in biological life–forms, and these steps must be duplicated in a digital life–form simulation program for it to work successfully, for it to simulate life.

The actions involved in a life–form simulation program are any action that a biological life–form would normally take to causally maintain its existence or reproduce itself, to select, attain, and maintain its values. As with mechanistic causality, the causal or action capacity of a


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digital life–form stems from its identity. The sequence is the same for all actions and is shown in a traditional computer programmer’s flow chart in Figure 5-1 below.

Actionx

Goalx

x = x + 1

Death (Deactivate)

No Success?

Yes

Survivalx Calculate needs

Select another action from a list of basic alternatives based on greatest survival need.

Figure 5-1 Causal sequence for simulating goal–directed action


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Failure must always lead to death (erasure or deactivation) because life depends on values (disvalues always cause death) and success always leads to survival because values are also causes, and the same causes always have the same effects.

This process is the first layer of the complex causality of life–forms and the interface between mechanistic and goal–directed causality as a form of action. The process is the same for simple biological life–forms such as single– celled organisms or complex, multi–cellular life–forms such as plants, animals, or humans.

The process is complex causality because it must run continuously to maintain a DLF’s existence, and it is internal to the DLF. The teleologic of this system is different from a simpler, state of the art, mechanistic automaton or agent because their programs do not need to be acting continuously to maintain their own existence.

Notice that in goal–directed behavior, actions may or may not be pre–defined, but are not pre–selected; rather any action or action sequence is permitted so long as it aids in the goal of survival, and the action selection process occurs in each cycle of goal–directed action; actions are only limited by death. It is the need to avoid death above all, that serves as the criteria for action selection. Goal– directed behavior is primarily active; it is forward looking and solves the problem of action pre–definition by setting life as the standard of action selection . The arrangement is built into the pleasure–pain systems of biological life– forms that allow them to function automatically (in the biological sense); it enables them to proactively do whatever is causally possible to survive.


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This fact and mode of operation is what makes automatic teleological action different from automatic mechanistic action; the identity of life processes differentiates a living cell from a mechanistic automaton.

And there is another important difference in teleologic: Only the actions required for survival are necessitated, meaning necessitated by survival, by the need to act to stay in existence against the alternative of death and non– existence. This is the case because of all possible actions open to a life–form, some actions cause life, some cause death, some cause neither life nor death in certain contexts, but can be neutral, and neutral actions are optional. They have that status because survival is assured for some period of time (by necessitated actions that were taken in the past), and therefore no necessitated actions are required in the present.

Note - Actually, value–significance is a highly contextual idea. In principle, everything has either positive or negative value–significance to a life–form because everything has the potential to influence its life for better or worse. However, such evaluations can be calculated in many ways depending on immediate contextual circumstances, allowing for neutral evaluations and optional behavior. For example, putrid water or urine is of negative value–significance to a healthy life– form with a good water supply, but of positive value–significance to one that is dying of thirst. Some contexts, therefore, especially time related ones, determine which actions are neutral to survival and hence, optional.


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For example, an animal near starvation in the wilderness is necessitated to live hand–to–mouth in order to survive. However, an animal living in a rich environment with no predators could afford to do nearly nothing for the its entire life–span and still lead a comfortable life (as some people’s pets do). In the former case, nearly every action is necessitated by the scarcity of values; in the latter case, whether the animal slept or played or ran around a tree until it was exhausted is optional because of the special circumstances of its context: The animal is in a safe environment of abundance in which little action is required to get the values needed to maintain its life, so it has many options.

Since actions that cause death are eliminated from the DNA of life–forms over time (the dead life–forms are not around to repeat the actions), ultimately, only two types of action remain available to any given life–form: survival actions (which are necessitated), and optional actions (which are not).

This fact is the source of optional behaviors in life–forms, and ultimately, volition in human beings.

5.4.4 How to Write Your Own Goal–Directed Program

A skilled programmer needs only a reasonably powerful desktop computer with at least 64 MB of memory, a 1 GB hard disk drive, and an object–oriented programming environment to write a goal–directed program that simulates a simple digital life–form.

Writing a program to simulate goal–directed behavior on a computer system amounts to creating a Digital Life Form (DLF) and a simulated environment in which the


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DLF will live. Simple simulations involving a few thousands of simulated percepts and 100 or so simulated conceptual chains would require less computer resources and could be done on a high–end PC, but complex simulations of higher life–forms that involve millions of percepts and 20,000 plus conceptual calculation chains for simulated natural language understanding could require a more powerful computer system such as are used for large Internet servers.

Note - A simulated or virtual environment can be made very sophisticated and is easier and less expensive than using a real one because it can exist entirely in a computer’s memory, so no external sensors or actuators are needed. To simulate high order functions such as rational consciousness accurately, a DLF would eventually have to interact with the same world human beings do, including interaction with people, but simulations of simpler DLFs do not require real world contact. However, both simple and complex simulations that use external robot technologies are possible with today’s technology, and will become even more realistic in the technical improvements that will come in the near future.

To create a DLF using an object–oriented computer programming environment requires a digital life–form program object be defined with suitable attributes such as a name, an age, an initial supply of Energy Packets (EPs) to simulate ATP (the “fuel” of biological life–forms), and so on, to enable it to act by its own internally controlled power source so it can sustain its own future actions.


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Many other attributes or properties can be added to make more complex DLFs, but the ones just listed are sufficient for a simple life–form simulation.

The novel idea here is to replicate the essence of the physical design of biological life–forms, namely the facts of their conditionality, having an internal fuel source, and having internal control of action, in virtual form as part of a teleological system; the teleological system can then be animated by the mechanistic causality of a computer in a manner similar to the way in which biological life– forms are animated by the mechanistic causality of physics and chemistry.

Given this design, as with their biological counter–parts, maintaining an adequate energy supply becomes the basis for all other actions a DLF may be capable of performing; therefore once the DLF programming object has been created and defined, processes called methods (object oriented–computer programming code) must be defined to enable the DLF to take action and an action selection method to enable internal control of its actions to find simulated food in its simulated environment to generate more energy packets. This must be a continuous process to enable the DLF to survive, just like a biological life– form.

Some action methods such as Find Food, Eat, and so on are pre–defined and designed to be just like the ability of animals to move their sensors and limbs (so the simulation system does not have to recapitulate evolution), but action selection by the DLF is not pre– determined to any specific action. The goal implicit in the program is for the DLF to take the action necessary for it to survive, to select an action from several alternatives at


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each iteration of the goal–action cycle, but which specific action it activates is determined indirectly by the DLF’s action selection methods based on its life status, its values, and other strategies explained in later sections. These methods are automatic (in the teleological sense), but provide a range of alternative options, and the control is internal to the DLF as is the energy to take the actions.

DLF Name: 00001 Age: 0 Starting EPs: 100 EPs: 0 Actions: Find food, eat, stop, die.

Action Methods Look: program code Find food: program code Eat: program code Stop: program code Die: program code

Simulated Environment

not food

food

words

Figure 5-2 DLF and a simulated environment

As with the DLF program object itself, the program objects in the DLF’s simulated environment must be created and defined (to save resources and make the


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system simpler during initial development), but since these objects are non-conditional (non–living), most need few action methods for simple reality simulations. More complex and sophisticated simulated environments in which non–living objects are animated (or contain other DLFs), would however, require coding extensive action methods for those objects.

In the example shown here in Figure 5-2, food is the shaded objects or word objects, the other objects are not food and will not generate EPs if “consumed” by the DLF. Or, alternatively, all objects could be food, but be assigned different values so some objects are more “nutritious” to the DLF than others. How the environment is defined depends on the programmer’s purpose for creating the simulation (provided that definition is reasonably consistent with reality). So keep in mind that the environment objects shown are just suggested examples, not required specifications.

To survive, the DLF needs only to act to find food and eat it, thus generating fuel and sustaining itself for future action; these actions are necessitated by its conditional nature. The computer program code for finding food is to “look” and perform a simple object search in the simulated environment to identify food objects.

Note - The ability of a DLF to “Look” for food assumes a working sensory/perceptual system (automatic consciousness), which is explained in a later section of this chapter.


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The program code for the Eat method can automatically include digestion, generating EPs, and the simulated feeling of being “full.” The code for the Stop method is a simple loop that continuously tests for feeling of fullness, and stops the Eat method when that condition is met. The code for the Death method erases the current DLF from the computer’s memory and calls the Birth method which increments the DLF name attribute by one and resets the other attributes to initial conditions. An example flowchart is shown in the example in Figure 5-3.

One objection to a teleological design that could be made is, why go to all this complexity, when a mechanistic agent could do some task more simply and directly? After all, an automaton that is specifically designed to do something, just does it, without need for food, digestion, a pleasure–pain system, simulated death, and so on.

The answer is that this is true, so long as the mechanistic agent only faces tasks that its programmer has foreseen and put into such an agent’s design.

The advantage of the complexity of teleological agents is that by interacting with reality they can find ways to do tasks for which they were not programmed, find ways of ordering their basic, pre–defined actions in new sequences for which they have no pre–defined actions in their design. What is their motivation? The DLF’s need to survive.

Teleological agents can do so, while mechanistic autonomous agents cannot, because to act is their primary imperative and their simulated lives depend on it, literally. In this regard, teleological agents are like the


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earthworm in Dr. Binswanger’s example described in the introduction to this chapter, and mechanistic autonomous agents are like the ice cube.

In other words, the complexity is necessary in teleological agents to build in the imperative to act for survival, without specifying more than a few alternative actions in advance. Whereas, mechanistic agents may be simpler, but they can only perform actions that are specified in advance by their human designers.

Modern jet fighter designs provide an analogous, though mechanistic, example: One might ask: Why design an inherently unstable plane that requires a fly by wire flight control system which uses millions of dollars worth of computers to make the plane possible to fly, when a more standard design can be flown with a simple hydraulic flight control system? The answer, of course, is that the fly by wire system is the only type that can be used with the inherently unstable designs of modern fighter jets, designs that are of value because of the maneuverability they offer pilots in dog fights.

Though teleological, simulated life–forms are inherently unstable too, and likewise require more complex system designs in order to survive and do things that mechanistic automatons cannot.


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Perceive Objects in world

Identify Food by memory comparison

Get food object properties from simulated world


No

Find

Look Method

No

Zero EPs?*

Yes

Food?*

Yes

Eat Method

Death Method

Delete DLF from memory

Digest food by calc. nutrition of object & convert to EPs

Call DLF birth method

No Yes Full?*

Stop Eating & call Perceive method (& other actions)

* These are part of the Evaluation method (not shown).

Figure 5-3 Flowchart for simulated eating by a DLF


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So unlike state of the art automatons, survival for a DLF means action by goal–directed behavior only, which is achieved by the flow chart shown in Figure 5-3; there are no other alternatives. The goal is to generate EPs to build up and maintain an internal energy supply, and the actions it must select and cause are to find food and eat until full. Just as with a biological life–form, securing the energy to maintain the ability to self–generate future actions must be its most fundamental priority; it is a necessary action (as opposed to other actions that are not necessitated). These and only these actions lead to survival (and indirectly make other actions possible). All other actions lead to death, and hence never get repeated.

Note - This does not mean that behavior which is not goal–directed can never occur, it simply means that optional behavior can only occur on a short–term basis, and after a reserve of energy has been accumulated. Survival actions must occur often enough to maintain survival.

The computer program methods described in this section may seem simple, even artificial as mentioned above, because simulating biological life is so complex as compared to using a mechanistic automaton, but their teleological design provides a DLF the necessary limits by which to select actions and with its own, internal motivation or imperative to take those actions in the first place. By replicating the essential processes of biological life, the design makes the DLF self–generating, self– selecting, and self–motivating; these are attributes state of the art computer simulations systems lack.


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Another objection could be raised that a programmer gives the goal of taking survival actions to the DLFs and therefore they are no different from other state of the art AL programs. This objection assumes that no human intervention is what defines teleology, that humans cannot provide goals to DLFs, but that is not the case. As with state of the art AI and AL mechanistic agents, DLFs are being created for human purposes and some of the actions of DLFs will therefore be for human values. However, this is not what makes DLFs teleological; what makes them teleological is that:

1. DLFs’ actions are self–generated: Each action a DLF takes is caused by its own internal supply of EPs; if there are no EPs, no action is caused because the DLF no longer exists.

2. DLFs’ actions each have value–significance to the acting DLF: An action such as eating gains a value for the DLF by increasing the supply of EPs, causing future action potential; if a DLF is low on EPs and does nothing or selects an action that does not find food, its supply of EPs is further reduced and this causes its simulated hunger “pain” to increase (value– significance). The DLF needs the EPs to survive.

3. DLFs’ actions are caused by the value–significance of the action to the DLF acting: An increase in EPs causes a decrease in hunger, survival, and the potential for the selection of actions other than eating, some of which may be optional actions; if a DLF is low on EPs and does nothing or selects an action that does not find food, its supply of EPs is further reduced and that


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ultimately causes “death.” So survival (necessitated) actions are selected because of their value–significance to the DLF.

These are the three essential causal aspects of teleological or goal directed behavior, and the DLF program meets all of them. The fact that the author shares the value of wanting DLF’s to live and prosper or that a programmer programmed the values is irrelevant. What is relevant is that it is necessitated that the DLFs gain values for themselves, or die, and they control the selection of and provide the energy for the actions to do so.

DLFs are driven by their own internal needs that result from the interaction of the alternatives in their program design with reality over a period of time, as opposed to mechanistic agents which are driven by the actions specified in their program design alone.

The methods described for DLFs effectively add another layer of causal complexity, teleology, to standard, mechanistic object–oriented computer software: In this simulation system, the existence of DLFs is conditional and only DLFs that conform to the teleological criteria are able to live and act. Therefore all other possibilities are eliminated, and all other types of behavior are eliminated over the long–term; thus there is no need for DLFs’ behavior to be pre–defined, other than to provide them with a few basic actions and the imperative to act.

In biology, this is the function of death, to eliminate non– survival or anti–life behavior, and it works precisely because life is conditional. This simulation system simply mimics the natural process in digital form.


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Note - The programmer will also need to determine how to pass on pro–life behaviors learned by DLFs and maintain them between generations. This is an issue I have not addressed in detail. It could be done several ways and may require some experimentation. For example, it could be done by not erasing pro–life behaviors from memory at simulated death, simulating genetic evolution to carry the behaviors forward to the next generation of DLFs by simulating a “reproduction” system, or by some other means. The main thing is that the anti–life behaviors must be erased when a DLF “dies” or the process will be self defeating.

5.4.5 How a DLF Differs from the Current State of the Art

Now that I have given a specific description of a teleological simulation system, the basic problem it solves in the fields of AL and AI can be more clearly stated and contrasted with current state of the art.

The difference between the DLFs in this program design and current state of the art AL and AI programs, is that the DLFs are teleological by design, whereas state of the art mechanistic automatons that may appear similar in some ways, but the similarities are only superficial because they are not the result of intentional teleological design, but from copying an isolated aspect of a life– form, and life–forms are inherently teleological.

The problem of simulating a life–form is not primarily one of designing a system that behaves like an animal eating by plugging into the wall to recharge its battery,


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behaves like a human being playing chess, that senses and exhibits human facial expressions, that acts like a group of animals or people solving a problem as a team, modeling genetic evolution in a simulated eco–system, or in the words of Patti Maes, “fast, reactive behavior” that “models life as it could be.” These are interesting computer programs that model some isolated actions of life–forms, but they are not essential to the problem that needs to be solved to animate an intelligent life–form using a computer simulation system.

The problem is not primarily one of specifying and automating specific, isolated behaviors.

The problem is to limit behaviors, to create a certain kind of relationship (conditionality) between the simulated life–form and the world it lives in, between the life–form and reality that is similar to that of biological life–forms, and then programming the DLF with the means to maintain the conditions its life requires. The problem is one of limiting the endless number of behaviors a simulation system is capable of to only goal–directed behaviors without having the impossible task of having to specify them all, and all the conditions in which such behaviors will or will not be activated.

This is what goal–directed behavior does for biological life–forms: It automatically limits their behaviors to only those that cause survival. And it does so by the drastic means of simply eliminating any life–forms that perform any other kinds of behaviors.

Life–forms have the imperative to act to satisfy their own greatest needs, whereas automatons are the simple cascade of a set of program instructions being executed.


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The kind of relationship that this invention provides between DLFs and their world is the same kind that biological life–forms have with reality: a teleological relationship. To quote Dr. Binswanger: “What underlies goal–causation? The fact that only valuable actions get repeated. Why do only valuable actions get repeated? Because the value here is survival value, and to repeat the action, the agent must survive.”26

It is the conditional, teleological relationship between life–forms and their environment that automatically limits their behavior to specific action capacities and makes them what they are. The only actions that get repeated long–term are the valuable actions; life–forms that repeat any other kind of actions simply get wiped out and no longer exist.

That is what the teleological software of this invention brings to the current state of the art: A means of automatically limiting the endless number of actions a computer simulation program is capable of, without directly specifying what actions will be taken, where they will be taken, and when they will be taken. As I pointed out earlier, while the suggested simple action methods are pre–defined in the flow charts shown above, the specific action a DLF will select is not predefined, to say nothing of complex actions that could be created by DLFs by selecting various different actions for each action event, thus stringing together a complex action such as Find Food, Eat, Stop, Eat, Stop, and so on to build up a reserve of EPs without triggering the “pain” of being too “full.” An action such as that is not pre–defined in the DLF system. Only the imperative to act to satisfy needs is specified, to select some action from a basic set of


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alternatives, but which specific action or string of them is not specified. That is determined by the internal needs of the DLF.

The ultimate locus of action control for a DLF is therefore internal; they can be guided from the outside, but not controlled like a robot; rather, DLFs must be trained like animals. As with biological life–forms, DLFs that perform any other kind of behavior besides goal– directed behavior are simply wiped out of memory, and therefore never get to repeat their actions in the future.

The AL and robotics researchers attempting to emulate biology cannot help but to include some teleological aspects into their software because biological life–forms are inherently teleological. Their problem is that they are focused on specifying and controlling behaviors using mechanistic programming techniques rather than limiting behaviors by replicating the essential relationship between biological life–forms and reality, by using conditional, self–generating, self–sustaining program objects as per the three test criteria listed above.

The AI researchers ignore the theory of teleology and the nature of consciousness, attempting to simply explain them away as a form of billiard ball causality that can be recreated using an ordinary computer program, and therefore attempt to specify and automate intelligent behaviors of life–forms with ordinary computer software. For controlling behaviors they use either standard programming techniques or cybernetics (negative feedback control systems), neither of which are consistent with life values and goal–directedness, or in the words of Patti Maes:


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“Complex behavior is the result of interaction dynamics (feedback loops) at three different levels: interactions between the agent and the environment, between the different modules inside the agent, and between multiple agents.

For example, a simple Braitenberg creature which “loves” the user can be built by making it move in the direction of the user with a speed proportional to the distance from the user. As an example of complex multi-agent interaction, Reynold’s creatures demonstrate flocking behavior through the use of simple local rules followed by each of the creatures in the flock.” a

These state of the art design and programming techniques are just mechanistic causality; the cybernetics (the negative feedback loop) is a form of stasis maintenance that reduces an error to zero and seeks to keep it there (like a thermostat).

Life, however, is neither mechanistic nor static; life is active. The imperative for cybernetics is error reduction and the maintenance of a static condition, whereas the imperative of life and teleology is action with life as the standard.

What is needed to emulate intelligent life on a computer simulation system or as part of a robot system is causality substitution, to substitute the mechanistic causality of a computer system or robot for the mechanistic causality of molecular chemistry and physics. In addition, and running in a layer over a conventional computer program, teleological software that simulates the causality of the conditional relationship that biological life–forms have


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with reality, software which insures that only goal– directed behavior with life as the standard of value is possible to a simulated life–form.

The DLF Simulation Technology design represents an advance in the state of the art for AL and AI because it does precisely that: The DLF design emulates the conditional nature of biological life in virtual form.

5.4.6 Creating More Complex DLFs

Simulating simple life–forms is interesting, but simulating more complex, higher life–forms that possess consciousness is much more interesting. In order to do that a mind must be simulated in addition to a body.

In biological life–forms, the mind is an attribute of the body, and as such, mind cannot exist independently of the body. The two are integrated causally. There is no mind– body dichotomy.27

Likewise in digital life–forms, simulated consciousness (the virtual mind) must be part of a DLF’s teleological operation, as a state of being aware of reality, a relationship to reality that results from the DLF’s own causal sequencing. Simulated consciousness in a DLF depends on a layer of automatic subconscious processes and the goal–directed subsystem layer below it in order to function. This situation is analogous to the way human consciousness depends on the functioning of the physical brain. In fact, the very reason simulated consciousness exists at all in the DLF design is to support the goal– directed process of simulated life by making it easier and


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more efficient for the DLF to find simulated food and attain other values, just as real consciousness aids biological life–forms in these same pursuits.

Writing the program methods to simulate consciousness is as straight forward as it is for simulating the conditional nature of life, as the next section will show.

5.5 Adding Perceptual Consciousness to a DLF

Perception is the ability of some life–forms to see reality (touch, smell, hear, and so on) as a collection of objects with various properties, and then to use that information to guide their actions for survival, as opposed to using unprocessed sensor output (data bits) as state of the art AI and AL systems do.

Unlike sensations of individual energies or forces such as a plant sensing light, moisture, or gravity, percepts integrate many sensor outputs over time and space into a foreground of discrete objects, each with its own set of attributes (properties and values), which are the objects’ identities; these identities are the focus of consciousness and are seen against a background of other objects.

The identities of perceived objects are not names or symbolic representations. Names and symbolic representations are conceptual; they are part of concepts (as defined by Ayn Rand) which are another type of “data structure” that is more abstract; one that is formed by comparing percepts. (How concepts can be simulated will be explained later.)


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Perceptual identities are preconceptual ; they are the content, the data from which concepts can be formed with further processing. Nor are perceptual identities images. They are the objects of perception (what is sensed) that have been processed into a different form from which the objects exist outside the perceiving life–form, and while that form is a kind of information, it is not a reproduction.

No one yet knows the exact form perceptual identities have in the brains of biological life–forms; sensations and their integration into percepts are neuro–physiological processes that function automatically and subconsciously in the brains of certain life–forms. In a computer simulation of a human perceptual system, however, the most likely form of perceptual identities will be as lists of attributes (characteristics) consisting of properties and measurement values. How to transform sensor output to perceptual identities will be described in detail shortly. Suffice it to say at this point, that percepts are much more compact than raw sensations, which are very long strings of data bits that are usually processed as X,Y coordinates.

Perception is the first level of consciousness, an automatic form of it that allows higher animals to have awareness of the world around them in order to find food, avoid danger, and fulfill other survival needs. Perceptual consciousness is an attribute of the biological life–forms that possess it; it is part of their goal–directed behavior repertoire and is inherently teleological.28 Therefore, perceptual consciousness must be programmed as another form of goal–directed action in any computer simulation of it.

Perception exists in biological life–forms because it offers a distinct survival advantage: Perception reduces the amount of data a life–form must deal with and that in turn


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saves valuable time. Instead of millions of sensor outputs, a life–form possessing perceptual consciousness sees, hears, smells, or feels food or danger directly and quickly. Having to visually process a piece of fruit or an insect that is automatically made distinct from its background by its perceptual identity is much easier and faster than attempting to identify it as sensations. To be able to recognize and avoid predators almost instantly is an even greater advantage. Imagine yourself in the jungle: Would you like to try to identify a poisonous snake or a tiger using only sound bits or pixels, or would you rather be able to hear the hiss of the snake or see the stripes and fangs of the tiger before taking “evasive action?”

Perceptual consciousness makes survival easier by reducing the number of units of data a life–form must deal with by integrating sensations into percepts. In addition, once the most commonly perceived objects are stored in a life–form’s memory, they serve as a baseline for future perceptual comparisons. Since consciousness is largely a difference detector, changes can be easily identified in the relatively small number of objects in any perceptual scene. These same advantages will accrue for a DLF that simulates perceptual consciousness for the goal of its own survival.

5.5.1 Sensing and Acting in a World

The program code for sensing the environment will differ greatly depending on whether the environment for a DLF is simulated or real. The two types of environment are essentially equivalent, except that real sensors sensing reality provide much more accurate and detailed real–time data of the world, whereas simulated worlds are limited to human imagination and computing resources. Simulated


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environments are primarily useful for developing, testing, and proving program methods while conserving resources. Sophisticated DLF simulations intended for practical uses will need to interact with the real world you and I inhabit to be effective.

Note - Since the use of real sensors and the computer code to operate them is well established in both AI and AL, this description will focus on a simulated environment. Suffice it to say that real sensors and their software could easily be substituted for the simulated environment to be described herein, by a skilled computer programmer with knowledge of robotics.

In a simulated environment created with an object– oriented programming language, sensing amounts to reading the attributes of the environment’s objects. This information is then transferred by the DLFs sensing method from the simulated environment to the DLF’s perceptual processing methods. The transfer itself simulates sensors in a simulated environment.

Simulating Perception and the Identification of Objects

The identities of objects consist of some number of attributes (These are also called characteristics or features in the fields of AI and AL.), and each attribute consists of a property and its associated measurement value. Attributes must be calculated from the sensor data and integrated into an identity, which is a list of properties and values, to be a percept. Foreground objects must also be distinguished or differentiated from background


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objects to be identified. Consciousness operates by detecting differences in sensory data and then focusing on the identities of foreground objects.

Much work has been done in the AI and AL fields to devise programming methods to accomplish these tasks, generically known as feature, attribute, property, or characteristic extraction and recognition. In the current state of the art, however, feature extraction and recognition is performed only to satisfy the human goals of targeting weapons or building robots. These processes are not designed to attain the sensing system’s own goals (the systems have none), but the human goals of hitting targets or exploring a landscape on Mars, for example. By contrast, while DLFs may be designed to share similar human values and used for the similar purposes, human values will be secondary ; the primary values of a DLF must always be to gain and keep what is necessary to maintain its simulated life, such as the identification of objects in the world that can be used for survival. Remember, the locus of control for DLFs is internal, not external like it is for machines.

Note - A lack of values is true even in the case of robots that are designed to plug themselves into electrical outlets to “eat” or those designed to “work together” in packs for “common goals.” In all such cases I have seen to date, to whatever degree the design of these systems is teleological, it is that way by accident because they are emulating one or two isolated behaviors of real life–forms, but the human values of acting like a life–form or accomplishing some common goal are still the


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primary values of such robots, instead of survival with their own simulated life as the standard of value, as it is in biological life– forms; hence, what extant systems exhibit is not goal–directed behavior. As a consequence such robots are mechanistic devices created to achieve the human goal of building a robot. The designs do not satisfy Dr. Binswanger’s three criteria for a being a life–form. There are no “values,” “value significance.” or “actions caused by value–significance to the agent” in the extant system designs. Their “survival” is caused externally by their human builders, not internally initiated, controlled and sustained. Whereas with the design of DLFs, their own survival is their primary goal, with the energy and control originating from inside the DLF; any human goals DLFs may share are secondary to them.

For the purposes of simulating consciousness, the point is that the overall reason for differentiating objects from a background and identifying them in terms of their attributes is that perception provides a data unit economy to the perceiver; it reduces the number of data units the system must process to aid it in its identification of the reality, making it easier for the system to maintain its own survival.

In general, therefore, the process of simulating perception is to use various means to identify attributes in a scene, identify the objects to which the attributes belong as distinguished from a background of other objects, and to produce a list of these attributes for each object in a given


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perceptual scene (or other perceptual grouping if non– visual). The result is a simulated perceptual “image” or “snapshot” of reality consisting of lists of other lists; that is, a list of the objects that exist and were perceived, each of which is itself a list of attributes or properties and the particular measurements for each attribute.

Percepts generated in this way are not reproductions of the perceived objects, but rather they are transformations of the objects from real objects to informational objects. This transformation is possible so long as the objects’ identity is conserved as their new informational (virtual) forms are calculated from their physical forms.

In a simulated environment or world using object– oriented programming (such as I am limiting this description to), a computer interface window can be used as the background for a visual scene, and various objects drawn in the window can simulate real world objects in the foreground. Obviously, more complex arrangements can also be devised where certain objects themselves form the background and others the foreground that a DLF has in perceptual focus, such as non–food objects vs. food objects, as well as dynamic scenes in which objects move in various ways. But the simplest case is all that is needed to explain how perception can be simulated. The essential issue here is to show how the identity of the objects is acquired from reality and transformed into information, into virtual form.

In the typical computer environment, objects drawn in windows are stored as X,Y coordinates, and these coordinates are analogous to sensations in biological life– forms. They are the processing units of simulated sensations.


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Note - Extant systems in the state of the art for sensing the real world ultimately transduce the sensed data into some form of X,Y coordinates or other numbering systems. In fact, all types of computer “sensations” are transduced into some form of measurement system, so that every sensation produces one or more numbers or measurement values in one or more dimensions.

Perception in a simulated environment amounts to reading the X,Y coordinates (sensing) and identifying by calculation the attributes or properties the coordinates contain according to well known, established mathematical techniques.

To make a program method to perceive a simulated world, a computer programmer would create a loop that simply repeats the steps of sensing and extracting the attributes of the objects sensed, each of which provides the objects with a unique identity. The result is a cycle of repeated perceptual events, or simulated perceptual consciousness of the simulated world; that is, the awareness by the system of the objects as objects, not as X,Y coordinates.

For example, if a window simulating the real world contains drawings of several different sized circles, squares, rectangles, and triangles, these will be stored in the content attribute of the window in an object–oriented programming environment. It is a simple matter for a programmer to write a method to read the content attribute of a window to get the lists of X,Y coordinates for each object to simulate “sensing this world.”


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Note - This fact is also why it makes little difference for simple simulations of consciousness if the real world or a world simulated in a computer system is used as a data set. Any real world objects sensed by a consciousness simulator, would end up as ((X, Y,)... N) coordinates anyway. The only essential difference is the amount and complexity of the data, with the real world producing copious amounts of complex data, as opposed to a simulated one.

Once the X,Y coordinates have been “sensed,” the next step is to calculate the attributes of the objects. This process involves straight–forward methods of determining if the objects are simple (such as a circle) or composite (such as a triangle consisting of 3 lines) and identifying other attributes (such as if a line is straight or curved, its slope if it is straight, its length, its direction, and so on).

Each of these attributes is a category of measurement and each has a specific value because every object that exists is unique in its properties, its identity. So every object processed by these perceptual methods will produce a unique informational identity consisting of a list of attributes or properties, each of which itself has a unique measurement value associated with it because every property of an object can be measured by some standard. In fact, for a given type of object, it turns out that there is a range of measurements that is typical for each property, but more on that later.

The example I have used here is of a simple visual scene, but it makes no difference for complex scenes or other sensory modalities. The process is the same for all scenes


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or other sensory modalities such as sound, touch, smell and so on. In every case, reality is sensed, some unique percept is the result that identifies some particular aspect of reality. It is true that not every sensory modality produces percepts of objects (such as sounds or smells), but these can be integrated across sensory modalities as additional attributes of objects perceived in the visual modality. These facts are common knowledge in the field of animal and human psychology.

Vision is the central sensory modality of the highest life– forms. Objects are parts of reality, but reality itself is one integrated whole; it is a plenum (Aristotle). That whole is only broken into objects because consciousness itself is an identity; it is a limited, specific process and cannot take in the world as a single piece. The form information takes as it is processed is the result of the identity of conscious processes; the form of reality is transformed from its natural state to informational or virtual form, but the content itself, the identity of reality, is conserved in this process.

For any aspect of reality that can be sensed with sensors, objects (by means of their attributes) can be differentiated, and a unique identity can be calculated as a means of simulating perceptual consciousness. The simulated environment, the DLF, and the simulated perceptual process can be shown graphically in a conventional diagram and flowchart as in the following figures: Figure 5-4 and Figure 5-5.


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DLF Name: 00001 Age: 0 Starting EPs: 100 EPs: 0 Actions: Look, sense, perceive, pass to next method

Simulated Environment

2

a c

1 b

Action Methods Look: program code Sense: program code Perceive: program code Pass: program code

(In computer screen window)

Figure 5-4 Simulated world scene and DLF

The program code for the Look method for a simulated world is simply to select one or more objects in the “reality window” and to “focus” the simulated consciousness on its object(s). This process is analogous to what a human computer user does when selecting an object on a computer screen with a mouse before issuing a command from a menu. The human cannot think of every object on the screen or every command at once and must focus on a specific ones to do something. Likewise the computer cannot process every object at once using


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every command at once, so the user must select the object(s) and the command the computer is to use for processing them.

The simulated consciousness of a DLF cannot process all of reality at once either, so it must also focus (select or “Look”) at only what objects are to be perceived (processed); it must delimit its field of perception to less than or equal to its processing capacity and/or what is relevant to its purpose. This, by the way, is an example of how it can be the case that while a basic set of actions may be pre–defined for DLFs by human programmers so that evolution does not have to be recapitulated, both the action and the content the action processes are not pre– defined, but are determined by the DLF based on its own simulated life needs at the time the action is selected for execution.

Note - In human beings, the choice to focus or not is the essence of volition or free will.29 How focus and volition can be simulated will be described in detail below; the short description is that simulated focus and volition are forms of optional mental actions for a DLF.

Once the objects are selected, sensing is simulated by a Get method that retrieves the X,Y coordinates that are the objects and passes them to the method which in turn calculates their properties and values as lists, and then a Store method that stores these in lists in memory. The resulting lists are simulated percepts: Simulated percepts are a list of one or more object instances, each of which itself contains a list of properties and values; the lists are


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the perceived objects’ identities. These simulated percept lists are the processing units of simulated perceptual consciousness in a DLF; they are its content.

The simulation of perceptual consciousness is all of these methods operating together as a continuous process, constantly repeated by a loop, processing X,Y coordinate lists into the identities of objects, into lists of attributes of objects (properties and measurement values).

Note - The term “object” can be confusing because it is used in two different senses here. An object– oriented programmer would create classes of program objects of which the objects in the DLF’s simulated world and its simulated consciousness would be instances. It is important to keep programming vs. DLF simulation contexts of this term clear and the different meanings of the term “object” separated in your mind.


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Look at world (select objects)

Get X,Y coord. Lists

Calc Attr. & Values


Focus

Sense

Perceive

Store Attr. & Values

Pass to next method

Figure 5-5 Flowchart of simulated perception

For example, the following explanation shows how objects 1 and 2 in the simulated world shown in Figure 5- 4, could be converted to simulated percepts (lists of lists) for storage in a DLF’s memory as (P1, P2,... Pn). The length, slope and curvature attributes of these data for objects 1 and 2 in this example were generated using the DLF Program described in Chapter 3.30 The shapes were drawn in the DLF’s simulated reality window by the author using a mouse, and then the attributes were


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calculated based on the X,Y coordinates. Other attributes of line thickness, intersection angles, and object fill could be calculated to have additional or alternative attributes.

Figure 5-6 below, shows the actual X,Y coordinates for a circle drawn in the DLF Program by the author to look like the circle (object 2) shown in Figure 5-4 (without the fill pattern). The circle is small, only about a quarter inch or so in diameter on the computer screen. Obviously a large circle and one with a complicated fill pattern would generate many, many more coordinate pairs. In addition, different strategies would be needed to calculate attributes from filled shapes as opposed to line shapes.

The point of this example, however, is to show one way in which attributes can be calculated from the actual X,Y coordinate pairs of real objects that the author drew in a DLF Program window. In the form of simulated sensations, which are X,Y coordinate lists, objects such as the ones used in this example would be processed by computer sensing software as lists of one or two hundred coordinate pairs, or fewer (as in Figure 5-6). In the case of the small objects in this simple example, the unit economy gain of storing the objects’ identities as attribute lists instead of X,Y pairs would also be small. However, for more complex objects or those sensed in the real world using digital cameras and microphones, the coordinates thus acquired could have three or more dimensions and could number in the millions to billions, so the unit economy of converting the objects to simulated percepts (lists of lists of attributes (properties and values)) before storing them and doing additional processing of them would be significant.


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In a properly designed simulator, the coordinates could be recovered as needed later by reproducing them from the attribute lists, or by perceiving the original object again.

Figure 5-6 Circle X,Y coordinate pairs (simulated sensations)


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Figure 5-7 shows the attributes for the example triangle and circle (objects 1 and 2 from Figure 5-4) calculated from their respective X,Y coordinate sets. The closure attribute is FALSE for the three lines of the triangle (a,b,c) because the “composite shape” method was not yet working in the DLF Program at the time the author used it to produce these data. There was also no method for calculating the attributes of angles; however, the program was working well enough at the time to demonstrate the idea of calculating attributes from the X,Y coordinates used to simulate sensations.

The unit economy gained from this method is content– oriented data compression because it results from the way the content is processed and from its final form, rather than the analysis of the data’s bit patterns.


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Figure 5-7 Attributes calculated from X,Y coordinate pairs

Note - The very large number for the slope of line “a” simulates the “infinite” slope of a vertical line.


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The process of simulated perceptual consciousness described herein, is not the same as how the consciousness of biological life–forms processes the data they sense in the real world. However, within the context defined for simulating perceptual consciousness, the process described here is causally equivalent insofar as the objects’ identities are transformed from their form in reality to the form of information inside the simulation system, as they are converted from real objects to various kinds of processing units (informational objects). Given objects to sense, a DLF will always produce simulated percepts of those objects as lists of properties and measurement values. The motivation for the DLF to perform the simulated perceptual process is teleological, but the means is mathematical and always certain to produce a result.

Note - The form of the percept of an object is different from the form of the object itself, though its identity is conserved. (See the references31 on the form/object distinction.)

It is a well known fact of mathematics that given X,Y coordinates of objects, various properties can always be calculated. Since computer sensors with proper software will always produce coordinates if given objects to sense and process, an identity in the form of a simulated percept will always be calculated for any object “sensed” in this manner, so long as the program methods are properly designed.

I pointed out earlier that Ayn Rand observed that “Existence is Identity.” This statement means in effect that to be an object is to be some properties and values of


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some kind. In other words, every object is valid content for consciousness because every object is some set of properties and values and will therefore always produce a processing unit that falls somewhere in the appropriate measurement range for a given type of object. Not to do so is not to exist.

Ayn Rand also observed that “Consciousness is Identification.” 32 Since perceived objects are the content (data) of consciousness, this statement means that consciousness transforms the identity of the objects it perceives into a form of information, into a mental, epistemological form in the human mind, as opposed to a metaphysical form. The objects exist in reality; the information exists in consciousness as content; it exists in the form of information and is true about the world so long as each perceived object’s identity is conserved by conscious processing. In other words, the objects are metaphysical (part of reality), the percepts are epistemological (part of the content of consciousness), with latter being the information that makes up a conscious entity’s knowledge.

Information IS identity in conscious form, as the content of the consciousness attribute of some life–forms. Moreover, it is specifically in reference to human consciousness that the term information gets its meaning.

When you or I look at objects 1 and 2 in Figure 5-4, the objects are converted into percepts that are stored in our memories by our perceptual consciousness; the identities of objects 1 and 2 are the content of that particular conscious event for us. The properties and values we see as the identity of the objects are stored in the form of perceptual information in our memories; they are the


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processing units at this level of consciousness. This process is what a DLF’s simulated perception process imitates using various calculation and storage methods.

The method of the simulation of consciousness I have just described in this section similarly enables a DLF that is animated by a computer system to convert the identity of objects that exist in reality (simulated or real) into identities in the form of simulated (calculated) percepts, which like their counterparts in the consciousness of you and I, are also a form of information. In the case of the DLF, the processing is performed by a computer simulation system, and the result is stored in the computer’s memory instead of the mind of a biological life–form, but the end result is causally equivalent as long as identity is conserved as the following example shows. Though strictly speaking, it is only the context of human consciousness that makes this content “information,” in a simulated sense, it functions in a manner similar to how it would in a person’s mind: It enables the DLF to be “aware” of what is in its world by conserving the identities of objects and then storing them in memory.

Let’s assume for a moment that a DLF is using a digital camera to take pictures of the real world human beings see instead of sensing a simulated world, and that objects 1 and 2 in Figure 5-4 are in the view field of the camera: The identities of objects in the field of the camera are converted from real attributes into measurements; that is, attributes in the form of real objects (the objects’ physical, visual identity) are transferred to the camera by light waves and are converted into pixels that consist of (X,Y) coordinates plus color by the digital camera’s software (one form of information); in effect, the information about the objects identities carried by the


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light to the camera is transduced and stored as pixels in the camera’s memory. The pixels are then output to the DLF and are further processed by the DLF’s simulated perception software into attribute lists consisting of properties and measurement values (another form of information), and this is the form in which the DLF is aware of the objects in the field of the camera; all of the information about the objects carried by the pixels to the DLF’s simulation of perceptual consciousness is now in the form of attribute lists, simulated percepts to the DLF, but the identity of the objects in the camera’s field is all still there, just in a different form. Though the specific means of processing and holding visual information may differ in biological and digital life–forms, it is still information about objects 1 and 2 as shown in Figure 5-4, information in which the identity of objects 1 and 2 has been processed, conserved, and stored in memory.

Evaluating Objects

Once various objects have been perceived by a DLF, they must be evaluated with the DLF’s life as the standard of value. To a biological life–form, since its continued existence is conditional, every percept is either a value or a disvalue relative to its life; that is, every percept has value–significance to the life–form as being information about its world that is either for or against its life. In order for a DLF to be an accurate simulation of a life– form therefore, a DLF must also be able to determine the value–significance of its perceptions.

Note - As noted earlier, everything has value– significance to life–forms, but how it is determined is highly contextual.


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The pleasure/pain systems of biological life–forms are automatic, built–in value systems. In general, things that are good for a life–form cause it to feel pleasure, and things that are bad for it, cause it pain (either physical, emotional, or both). These are well known facts of biology and psychology.

In order to create a digital simulation of a life–form, a similar automatic, built–in evaluation system is required, and like a DLF’s pre–defined actions, may be copied from biological life–forms and pre–defined so evolution does not have to be recapitulated. Since computer systems are not biological, but digital, simulated pleasure and pain must be calculated based on simulated values which serve as standards with the life of a DLF being the ultimate standard; this is the essence of life and goal–directedness. The idea is to make simulated evaluations as causally and functionally equivalent to the biological ones as is technically possible.

Note - Obviously, a computer simulation can never actually “feel” pleasure or pain as it is not conscious, only an imitation of consciousness; a simulator can only calculate simulated pleasure or pain measurements. And while this fact will not prevent the practical application of DLFs to many useful functions, it means DLFs can never have quite the same perspective as biological life–forms. Only experimentation can tell us how this fact will affect a DLF’s behavior.


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For example, in the flowchart in Figure 5-3 above there are decision boxes that test if a DLF “feels full” or not. What this means in computational terms is that a method must be written that compares the number of Energy Packets (EPs) that a DLF has with the range that its simulated life requires. Having EPs is a value to a DLF’s life; without them the DLF will “die” just as a biological life–form will die without food.

Note - EPs can be thought of as the digital equivalent of Adenosine Triphosphate (ATP), which is the “fuel” of biological life–forms. Reminder: The term “value” as used in this context (as in value/disvalue pair) means value to life, and does not mean “value” as in number.

When a DLF is “born,” it starts out with some number of EPs, say 100. From then on every action it takes uses EPs, and every time it “eats,” EPs are replenished. If the DLF adds the same as or more EPs than it consumes it survives and “lives” on; if not, the DLF “dies.” What the evaluation system does in the form of simulated feelings of hunger and fullness is tell the simulated consciousness of the DLF in a form of information it can process and use, its life status at a given time, like a fuel gauge.

Biological life–forms (including humans) are not aware of their life status directly, but through feelings such as hunger vs. fullness, and other value/disvalue pairs. Hunger is experienced as pain which eating makes go away. Hunger is replaced with fullness, which is pleasurable, unless the life–form eats too much, in which case fullness can become pain as well. If a DLF is given a range of EP values from 100 to 1000 say, with 400-600


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EPs being defined to generate a neutral feeling, having less than 400 EPs will produce stronger and stronger feelings of simulated hunger, and having over 600 EPs will produce stronger and stronger feelings of simulated fullness.

A simulated feeling can be calculated for any number of EPs a DLF has at any specific time by comparing the number it actually has to this range. The simulated feelings could range from say -9 for starving to +9 for too full, and 7 or 8 for comfortable fullness. According to the definitions just described, if a DLF had 250 EPs a simulated feeling of hunger of -5 would be generated as shown in Figure 5-8, if it had 700 EPs, the DLF would feel full and feel at perhaps a +8, and if it had 950 EPs it would feel the “pain” of greater than +9 fullness.


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Get current EPs (250)

Compare to value std range (100-1000)

Calc Feeling (-5)

Store in attribute

Call next method

Figure 5-8 Simulated hunger calculation flowchart

Similarly, simulated feelings can be calculated for any number of other value–disvalue pairs, such as interest vs. boredom, company vs. loneliness, clarity vs. confusion, activity vs. laziness, confidence vs. fear, and so on. Any simulated feeling calculation would compare some numeric standard of value (usually a range of numbers) against whatever number a DLF has at any given time relative to the state of that value in its simulated life, and the result will be the simulated feeling. Pain can be


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simulated by using negative numbers (or positive numbers beyond a certain range), neutral feelings by zero, and pleasure by positive numbers in a range from -9 to +9 (or any other range) as suggested above. In addition, the overall “happiness” of a DLF can be calculated as the average of all of its other simulated feelings.

Note - Reminder: The term “value” as used in this context (as in property and value list) is a computer programming term, and does mean “value” as in number, a measurement value.

A programmer skilled in object–oriented programming can make simulated feelings attributes of a class of DLF program objects. For any instance of a DLF, the property and value list might look like, though would not be limited to, the following:

1. Name: 006023

2. Age: 84

3. Starting EPs: 100

4. EPs: 350

5. Current percepts: P1, P2,... Pn

6. Actions Available: Look, Find Food, Eat, Stop

7. Simulated Feelings:

a. Hunger/Fullness: -2

b. Interest/Boredom: +3

c. Company/Loneliness: +2

d. Clarity/Confusion: +5


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e. Activity/Laziness: -1

f. Confidence/Fear: +2

g. Happiness: 1.5

The simulated feelings give the DLF an instantaneous indication of its life status, (and if put into a window on the computer screen as part of a DLF program interface, a human observer can see the same status). By being conscious of its own life status, a DLF can take actions to cause its future survival, since it would have the information that is a prerequisite to such actions. Simulated feelings are the simplest form of simulated self–awareness or self–consciousness, though at this level a DLF is not aware that it is “aware” of itself.

Actions and Objects

Life and consciousness are processes; active processes that consist of a series of actions. Biological life–forms continuously act upon objects to maintain their survival by finding and eating food, finding shelter from weather objects (such as raindrops and lightning bolts), running away from predators, and so on.

Similarly, DLFs need to act out comparable processes to maintain their simulated lives in order to maintain causal equivalence with biological life–forms. In fact, as pointed out earlier, at the level of perceptual consciousness, speedy action selection is the main survival advantage consciousness has to offer biological life–forms.

Actions are not pre–selected, but selected by the simulated perceptual consciousness process, and as with its biological counterpart, this process is an automatic one (in the teleological sense): There is no other basis for


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making selections because options are limited at the perceptual level. However, action selection is teleological because its goal is a DLF’s survival, the DLF’s simulated life is the standard, and it, therefore, cannot be explained as simple, mechanistic causality.

DLFs must have a list of actions they can invoke in their simulated world to effect their survival, depending on what they perceive at a given time, such as look, eat, move an object, compare objects, draw an object, type a word, and so on, just as a biological life–form does, such as nest building or swimming. These basic actions and more complex composite actions DLFs may construct by stringing basic actions together are primarily used for survival and secondarily for optional behaviors or to accomplish secondary, human values that DLFs may share with people.

Note - Remember, what I am talking about here are the processes internal to the DLF that simulate life, processes that must be maintained by the DLF to survive (Dr. Binswanger’s 3 criteria), and that this fact is what makes teleological causality a more complex form than mechanistic causality.

Actions are selected based on how a given percept and the life status of actions capable of changing that percept are evaluated. The efficiency with which a DLF can process percepts has a direct effect on a DLF’s life: The more efficient its processing of simulated percepts, the more likely it is the DLF will survive. Early in a DLF’s life, when there are few examples of percepts and how the DLFs previous actions changed them, most of the DLF’s


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Adding Perceptual Consciousness to a DLF

actions will be selected by trial and error. However, after perhaps six months of life and many thousands of perception–action events, the action selection methods will have much more data to use and will therefore be able to select actions with the greatest survival value more efficiently.

The simulated feelings a DLF calculates for itself, as described in the previous section, are the primary data for a DLF’s action selection process, along with its simulated perceptions of its world. Actions that are followed by increases in positive simulated feelings can be rated with a positive index or associated with the positive simulated feelings to make them more likely to be selected in the future in similar conscious events; the opposite is true for actions that result in negative simulated feelings. As with real feelings in biological life–forms, the simulated feelings of pleasure and pain calculated by DLFs provide an instantaneous indication to the DLF of its life status.

There are a number of strategies and measurements for implementing such strategies that a skilled programmer can use to simulate automatic action selection in a DLF, and more than one will be needed for a DLF to survive even in a simulated world, because life thrives on alternatives. Some examples of action selection strategies that have been observed in biological life–forms (including the author) and copied so DLFs do no have to recapitulate evolution are as follows:

• Continue the last action: This is a useful strategy when an action is succeeding in improving simulated feelings (such as eating to reduce hunger).


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• Select the action that resulted in pleasure in the past when a given object was perceived: This option is similar to the previous one, but is recalled from a memory association from farther in the past.

• Select no action: This is a useful option when all simulated feelings are positive and no action is required to change them. It is also an example of an optional action.

• Follow a pre-programmed process (when a given object is perceived, as with instinctual behavior in biological life–forms such as nest building (or habits in humans)): This option is a good strategy for a goal requiring complex actions or series of actions.

• Random action selection: This option is analogous to trial and error actions observed in biological life–forms and useful for new situations when no other action gets selected. It is another example of an optional action.

Note - Recall the screen shots in Chapter 3 from the DLF Program of one example of how to implement these strategies.

Other selection strategies could be invented and used, but these are sufficient for a skilled programmer to write an action selection method that will enable a DLF to always be able to select some action for any given percept and therefore simulate the biological imperative to act. The latter is important because life (simulated or real) is a process that cannot stop: Life–forms that have no active process are dead.


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While it is up to an expert programmer to determine the specifics, the most probable design for an action selection method is a series of program conditionals. An example of how action selection can be simulated is shown in Figure 5-9 below. Remember however, that actions must be selected based on the needs of the DLF’s life, not pre– programmed specifications, which are effectively arbitrary from the perspective of the DLF’s simulated life.

The DLF perceives its simulated reality or world and calculates its simulated feelings as described in previous sections above to determine its life needs at that moment. Then a conditional compares its simulated feelings to find if any are in the “near death” or “very unhappy” part of the range, and hence require “emergency” action. If so, emergency actions are taken to insure the DLF’s survival, such as eating if the DLF is almost out of EPs.

If not, the DLF’s happiness value is compared to its typical range of values, and if it is near the highest value, no action may be necessary (for survival) so optional actions are possible, or a random action may be indicated to provide the DLF with some new perceptions. A calculation is done, and either the No_Act method (a method which does nothing) is selected, or the Random_Act method is selected (which selects an action from the DLF’s action list using a random number generated by the computer). The calculation can be based on the length of time since previous new activity has occurred or some other standard; the specifics are up to the programmer and the design requirements of the simulation system that is being created.


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If the DLF’s happiness value is in its mid–range, then a method is called which calculates an action based on the other strategies listed in the bullet items explained above with various similar conditionals to select between them, such as continuing its current action, calling an action that is associated with an increasing positive simulated feelings in the past, a complex, pre–programmed action, and so on.

The important points to grasp here are as follows:

• The DLF’s action selection method as described insures that the system is closed and that some action is always selected for any perceptual event. Hence the DLF’s imperative to act is maintained.

• The action selection method is teleological in that its goal is causing the survival of the DLF with its simulated life as the standard of value, and it does so by increasing the DLF’s simulated happiness. It provides the DLF with a means of self–regulation using its own energy to cause its own future goals to be achieved. In other words, only necessitated survival actions and pro– life optional actions get repeated in the long–term; all others are effectively eliminated from its repertoire by the “death” method as explained earlier.

• The No_Act and Random_Act methods allow a DLF to maintain its simulated happiness for a time, provide for trial and error actions, and allow for the “unexpected” or the novel to be simulated in a DLF’s life, as well as optional actions which are not necessitated by survival, but are caused by the DLF simulation system.

• At the perceptual level of simulated consciousness, a DLF is capable of difficult to predict behavior due to its complexity and that optional actions possible, but it is


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not capable of unpredictable, volitional behavior. Its actions, though purposeful, are largely predictable, with the exception of the No–Act and Random–Act methods. And even these are predictable, but for a narrow range.

• These strategies taken together provide a basic group of behaviors, the specifics of which are calculated at the time of their execution, and that enable a DLF to respond to conditions in its simulated world. They are analogous to the automatic, genetically determined behaviors found in many biological life–forms that have evolved over many thousands or millions of years, as opposed to the ontogenetic, learned behaviors such life– forms may acquire during their lifetimes. Since we know these behaviors work in real life–forms, it is safe to copy them and pre–define them in DFLs, so long as care is taken to maintain their teleology.

Note - The issue concerning what happens to behavior and other memories when a DLF dies will be discussed in the section on memory.

• A key idea in this invention is that of leveraging the results of millions of years of biological evolution; this is precisely the point of directly programming the simulation of the perception/action and pleasure/pain systems of biological life–forms in DLFs. These systems of automatic consciousness and action selection are the causal layer that support and cause the rational intelligence of the conceptual layer above. The hard work has already been done by biology; we need only reverse engineer it.


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Evaluate, calculate simulated feelings


Perceive Objects in world

Near Death?

No

Happy?

Yes

Yes

No

words

Simulated Reality

Survival Actions (Find food, Eat)

Call action method associated with increasing current most negative simulated feeling, or other strategy

Calc need to act

Yes No No_Act

Random_Act, call Perceive method

Figure 5-9 Action selection example


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Once an action is selected, its method is called, any necessary data is retrieved or calculated, and the action is executed by the DLF to cause changes in its simulated reality (or the real world, depending on the simulator design), thus closing the system.

Memories

In order for a DLF to be a realistic simulation of a biological life–form, it needs to have memories of its past perceptions, feelings, and actions, of its simulated life.

For example, using the DLF described in the section on simulated feelings, we have the information shown in that section, plus the action selected (which would be the Eat method in this case because Hunger is its most negative feeling). The memory for the DLF for that instant in its simulated consciousness is therefore:

1. Name: 006023

2. Age: 84

3. Starting EPs: 100

4. EPs: 350

5. Current percepts: P1, P2,... Pn

6. Actions Available: Look, Find Food, Eat, Stop

7. Action Selected: Eat

8. Simulated Feelings:

a. Hunger/Fullness: -2

b. Interest/Boredom: +3

c. Company/Loneliness: +2


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d. Clarity/Confusion: +5

e. Activity/Laziness: -1

f. Confidence/Fear: +2

g. Happiness: 1.5

One example of a memory record for this DLF at this instant in its life is as follows:

(006023, 84,100,350, P1, P2,... Pn, Look, Find Food, Eat, Stop, Eat, -2, +3, +2, +5, -1, +2, 1.5).

Note - To make such memories possible, a programmer could use any of a large number of standard database record formats and processing methods to store the DLF’s percepts, simulated feelings, the actions it selected, and so on; the specifics of these choices are up to the programmer. (The Eat method is listed twice because it is one of the available actions and the action selected to be implemented in this instance of consciousness.)

With the older technology of the recent past, the amount of data a DLF generates as memories over its simulated life would have been a storage problem, but that is no longer the case with the huge amount of storage space available now, even with today’s personal computers.

Still, a skilled programmer must be smart about how the DLF’s memory is designed to make the most efficient use of its memory. If a DLF perceives its world X many times every minute, it will generate X+ many percepts, most of which will be the same because the world will not have


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changed since the previous percept. Methods for changing the simulated world and deciding how many duplicate percepts should be kept in memory and for how long will need to be determined by the programmer, and will depend largely on the purpose of a given simulation and the memory resources available. This is not an issue that is relevant to the simulation of consciousness as such.

Note - Lack of change will be less of a problem for simulations using real world data because the real world is constantly changing; however, the amount of data generated by sensing the real world will be significantly greater than that of a simulated world.

For example, the memory may need to be broken into short and long–term storage, with much of the duplicate short term content deleted or over–written after some period of time and only the non–duplicate content save to long–term memory. Other strategies can also be employed, such as indexing schemes that are commonly used in databases to make any memory easier to find, and so on.

It is also important for the programmer to make sure the memory is stored in such a way as it will persist as long as a given DLF is alive, since in the object–oriented programming environment, not all data persists when an object instance is not active. Memories can be stored as class attributes of DLFs or in some other form of persistent data structure.


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Another issue is what happens to a DLF’s memories when it “dies.” With biological life–forms all memories are lost, which is a terrible waste of information. On the other hand, the very purpose of death in evolution is to eliminate behaviors from memory which are not productive of survival or which are anti–life, so that they are never repeated. I have not included “reproduction” in this description because how pro–life information and actions are maintained in the simulation for future DLFs is a practical, programming matter, and it is not crucial to the description. The exception to this is that however the “death” simulation method is designed, it must be designed such that it eliminates anti–life behaviors from the action repertoire of DLFs and meets the criteria described earlier of being teleological.

Since this invention is a form of virtual reality, it will be up to the programmer who writes the methods for the DLF system to decide what strategy to use to simulate death. In biological life–forms, long term behavior memory is controlled by evolution through the gene pool. Ontogenetic behaviors, those learned during a lifetime, are controlled and modified by the pleasure/pain system. To deal with this issue, either an approximation of natural selection could be simulated, or some other method could be devised to cull unproductive behaviors from a DLF’s memory when it dies, while leaving other, valuable information in place to be available for future DLFs so each new DLF does not have to re–learn everything its “ancestor” DLFs had already “discovered.”

Finally, there is the issue of processing unit economy. Even though simulated percepts of objects offer much greater unit economy than X,Y coordinates or pixels, there will still be a huge and always growing number of


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them, even if memory conservation strategies are employed. (Another means of processing unit economy will be explained in a later section that will make DLFs more efficient at processing content by dramatically reducing the number of units they must process and will expand the capacity of their simulated consciousness.)

Action in a DLF’sWorld

Once the results of the processing of the Perception, Evaluation, and Action Selection methods have been stored in memory by the Memory method, the Action method executes the action selected during that simulated conscious event and causes changes in the DLF’s world.

Executing an action involves calling the action method selected earlier in the conscious event and effecting whatever change that method is designed to make in the DLF’s world. For example, the Look method would perform a search for an object, the Move method would move an object, the Eat method would extract “nutrition” from an object, the Draw method would draw an object, the Type method would type a character or a word into the DLF’s world, and so on.

Note - Some of these methods, such as Draw or Type, could be easily created by simply calling methods that are already part of an object– oriented programming environment and providing them with the appropriate data. Others, such as the Eat method, would have to be written completely by a programmer. And remember, it is why an action method is selected and its content is specified by a DLF relative to its own survival needs that


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determines its goal–directedness, not if the action is reverse engineered and pre–defined by a programmer.

The data for what object to draw or what characters to type would come from memories of previous perceptions of these objects in the DLF’s world and the DLF’s immediate life needs. Just as a human child first draws or writes what it sees around it, so would a DLF at an early stage of its development; in later stages, the data to be drawn or typed could be combinations it makes from its memories of percepts, combinations that it may have never actually observed; such “made up” combinations would be a form of simulated imagination on the part of a DLF.

When an action such as Draw or Type is selected, part of the action selection process must be to find the appropriate data for that method in the DLF’s memory, data acquired by previous perceptions. If no data is available, the action selection process fails and a different action for which data is available must be selected. This ensures that every action selected is a valid action and can be executed.

A method such as Draw, is simply the reversal of the process of simulated consciousness: The Draw method takes the identity of an object, which is stored in the form of attribute information in the DLF’s memory, and transforms that content back into an identity in the form of a real object or a change to a real object in the DLF’s world. As with simulated perception, except for changes to objects, identity is conserved in a DLF’s actions just as it is in its perceptions.


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A flowchart of the essential functions of the Action method is shown below in Figure 5-10.

Call Action Method

Get Data (if required.)

Execute Action in DLF’s World

Call Life Functions

Call Perceive method

Figure 5-10 The DLF Action Method

Note - If the DLF’s world is the real world, the Draw action could activate a printer or plotter to physically draw an object, or in addition, the DLF might have an action called Make, which


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could employ machine tools to transform the DLF’s perception of an object into an actual, physical object or change an existing one.

The final function of the Action Method is to call the DLF’s life processes in the subsystem layer below it (layer 5 in the model shown in Table 5-1). The life processes then calculate the EPs used by the action just executed, the number of EPs remaining, and if the DLF has enough to survive, as well as calculate other life needs. If there are not enough EPs, the DLF suffers a simulated death (which is not shown in Figure 5-10, but was described earlier in this chapter). If the DLF survives, control is passed back up to layer 6, and the Perceive method is called to begin the next perception of the DLF’s world. The percepts that result will close the system by showing the DLF the effects (changes) to its world that the action it just executed have caused.

5.5.2 The Conscious Event Cycle

From the description in the previous section, it should now be clear that simulated consciousness is a series of discrete, causal steps performed by program methods that repeat or cycle, operations a programmer turns into a process by putting them into a loop internal to the DLF to simulate its life and consciousness; the program continuously cycles through these several program methods, thus effecting the simulation. The process steps to simulate consciousness run in a subsystem layer above those of the DLF’s simulated life processes (layer 6, see Table 5-1) and the program methods that implement them are:


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1. Perceive

2. Evaluate

3. Action Selection

4. Memory

5. Act (then call Perceive again, ad infinitum if alive)

To simulate consciousness, the methods in the list are continually repeated so long as a DLF survives, as shown in the flowchart in Figure 5-11 below, in something I call a Conscious Event Cycle (also called the C.Event cycle), and each process run through the loop is a simulated conscious event, or “C.Event.”


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Perceive World

Evaluate

Reality

Select Action

Store This C.Event

Yes

No Survive?

words

Call Action method

Die

Figure 5-11 The C.Event Cycle Flowchart for a DLF

From the perspective of a DLF, the seeming continuous nature of consciousness comes from the rapid repetition of C.Events in this process, like the seeming continuous nature of a movie or video comes from the rapid changing of frames of still pictures, from the perspective of human consciousness. This process can continue indefinitely as long as the DLF is alive.


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From the perspective of human beings, the process just described is the simulation of biological life and its attribute of consciousness, a process animated by a computer simulation system that is causally equivalent to certain processes in biological life–forms (or as nearly equivalent as it is technically possible to make it).

Biological life–forms continuously face survival issues in every situation of their lives, issues that imply certain questions these life–forms must figuratively answer with quite literal actions: What to do next? How to choose specific actions from the seemingly unlimited number of potential actions open to them? A DLF simulates these issues and actions because the simulation program system interacts with reality based on the same teleologic as the biological life–forms do.

Perception automatically answers questions implicit in any situation a life–form may face, such as:

• What are the objects I see?

• What direction can I move?

• Is there any food in sight?

Evaluation automatically answers questions implicit in the identity information provided by perception with simulated feelings of pleasure/pain such as:

• Do I have enough EPs to explore more? (Fullness)

• Should I look for food now? (Hunger)

• Should I see if a new object is food? (Curiosity)

• How do I feel overall? (Happiness)


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Action Selection automatically answers the question implicit in the simulated feelings of pleasure/pain provided by evaluation, which is: What should I do next? The answer is based on the DLFs values, its built–in survival strategies that have been reverse engineered from biological life–forms, and its current life status: Of all the potential actions available to a DLF, the actions that it can perform in answer to the implicit question above are limited to necessitated survival actions and optional actions. Why, because simulated death prevents any other kind of actions from getting repeated in the long–term by DLFs.

The Action method closes the loop with Reality by effecting the causes of the actions selected in the C.Event, including the simulated life processes of the DLF for which the C.Event is an instance of simulated consciousness.

When functioning, a teleological system that simulates life and consciousness involves some number of C.Events per unit time (say one per second, more or less), and these transform the identity of objects in a real or simulated world into information in a DLF’s simulated consciousness. The result is a growing number of memories for a DLF, memories that constitute its perceptual information about whatever kind of world it senses.

Note - The number of C.Events per unit time could vary substantially, depending on how much processing occurs in a given event. A long memory search, for example, to recognize an


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object could cause a single C.Event to last a few minutes or more in a DLF with a large memory store of objects.

Note - Research at Princeton University (1999) on macaque monkeys has shown that thousands of new neurons are formed in the monkeys’ brains each day, neurons that travel to the cerebral cortex where higher intellectual functions and personality are stored in humans. These neurons are then specialized in various ways.33 This process is continuous and could explain some aspects of consciousness as a cycle and the physical basis of learning new subconscious processes in monkeys and potentially in humans. It could also explain how conscious events are stored in memory.

Since some of the information in a DLF’s memory (perceptual information) corresponds to the identity of the objects in the DLF’s world (simulated or real), the DLF can use the information to act in its world to attain its goals of survival and simulated happiness.

Simulated consciousness and the information it provides to the Evaluation and Action Selection methods is causal, and therefore it has survival value to the DLF just as real consciousness has survival value to real life–forms. There is an unbroken chain of causality from the world through perception, through the DLF and its actions, back to the world, an unbroken chain of the identity information that is conserved as the content of simulated consciousness.


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Note - Consciousness of relationships and other complex phenomenon will be described later.

In biological life–forms, consciousness is an active, teleological, life process that transforms the identities of objects in reality into the form of perceptual information, a form of identity suitable for storage and processing inside the life–form.34

Actions performed by life–forms in reality transform the identities of objects in the form of information in a life– form’s memory back into the form of objects in reality, or at least changes to such objects. Consciousness is a causal process; it has causal efficacy; this follows directly from the fact that consciousness is a limited process with a specific identity and hence a specific action capacity.35

As simulated in this invention, consciousness has an analogous function because it provides the DLFs in the computer simulation system with a form of causal efficacy that is different from state of the art computer programs.

Note - The author’s view contrasts sharply with the view that consciousness is either mystical or a totally transparent, empty process that lacks any identity. In the science of the current state of the art, only the latter view is taken seriously (since computers are not supernatural). That view translates into the false idea consciousness equates with brain function, and that therefore (as Herb Simon and others have suggested), that computer hardware is the “brain” with software being


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the “mind.” This false idea is then further extended to mean that an ordinary computer program can somehow become conscious without considering the complex causality and teleology of life processes or considering the identities of the objects which are the content of consciousness.

The attribute of simulated consciousness provides a DLF with a relationship to reality, a level of awareness, that is analogous to that of conscious biological life–forms, as opposed to entities which do not have that level awareness, such as viruses or state of the art computer systems.

Described in this way, it can be seen that simulating consciousness is a straight forward process that a skilled programmer can reproduce with the appropriate computer hardware and program code to interact with reality, provided the rules of the complex causality of goal– directed behavior are followed as part of the system’s design.

The key to a successful design is causality substitution, to separate the various types of causal processes into subsystem layers as shown in Table 5-1 (reproduced below).

Then it is to substitute the appropriate computer hardware and software for the mechanistic causality that underlies real life–forms, in order to animate the entire system.


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With this type of design, the software and interactions with the world that simulates the complex causal functions of teleology and consciousness in layers 5-7 in Table 5-1 are supported by mechanistic causality of non- biological kinds in layers 1-4 shown on the right side of the table, but supported in a manner analogous (meaning causally similar or equivalent, depending on the process and level of technology) to their biological counter–parts (taken collectively) shown in layers 1-4 on the left side of the table.

The net effect of the design is that in both columns, the processes of simulated life and consciousness are causally connected to the world in all respects by a causal chain. There is simply no magic involved, only levels of causal complexity supported by physical reality in each case:

• With biological life, the behaviors of conscious animals and other higher forms of life are caused by the teleological behaviors of cells, the teleological processes of cells are internally driven by Dr. Binswanger’s three criteria of the cells’ actions being


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self–generated, the cells’ actions have value– significance to the cells, and the cells’ actions are caused and internally controlled by there value– significance to the cells. All of these teleological causal forms are themselves caused by the simpler mechanistic causal forms of the electro–chemical and mechanical processes by which the cells physically operate, such as those listed in the left column in layers 1-4.

• Likewise, with DLFs, their simulated consciousness and other behaviors are caused by the teleological simulation methods running in the simulation system, and these are driven by Dr. Binswanger’s three criteria of the DLFs’ actions being self–generated, the DLFs’ actions have value–significance to the DLFs, and the DLFs’ actions are caused and internally controlled by their value–significance to the DLFs. All of these teleological causal forms are themselves caused by the simpler mechanistic causal forms of the logical, electronic, and mechanical processes by which the computer simulation system physically operates such as those listed in the right column in layers 1-4.

The bottom line is that the causality in both columns of Table 5-1 parallel each other.

5.5.3 Automatic Survival is at the Foundation of Life

In the simulation of consciousness, the automatic (in the teleological sense) and efficient survival value provided by perceptual consciousness is the foundation for the development of the simulation of more complex conscious functions such as forming abstract concepts, generalized ideas, more complex information, and the use of natural language as the tool to accomplish actions.


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As a basis for the description of the simulation of these processes, there are some key points that need to be emphasized.

Interacting with Memory

Much of the processing for the simulation of more complex conscious functions involves much more interaction with memory than occurs at the perceptual level of consciousness, and subsequent processing of those memory contents.

For example, if suitable recognition and memory association methods are written for a DLF’s action repertoire (to enable it to compare its memories of different C.Events and thereby recognize objects and feelings it has had in the past, and to associate the past successful actions with specific objects and feelings), then it will be possible for a DLF to remember a past C.Event such when it felt “fullness” from eating. This fact will lead to purposeful behavior in a developing DLF because such associations will result in simulated feelings of desire to repeat successful past actions, actions that brought the DLF simulated pleasure in the past.

Recognition and Purposeful Action

As memories accumulate, recognition of objects, scenes, relationships, past actions, and situations become more complex and important to action selection. The purposes of actions also become more complex.

For example, if in a given C.Event a DLF feels hunger, it may automatically recall associated events or actions in the past and then the action strategy method processing of


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that memory can calculate a simulated desire for feeling “fullness.” One of the action strategies built into the system and explained earlier is: Select the action that resulted in pleasure in the past when a given object was perceived.

To be conscious of the “desire to eat” as a human is, a “desire” simulation method would need to be written that calculates a generalized simulated feeling of “desire.” The new feeling is a general causal factor for any kind of purposeful action, usually to bring other simulated feelings back into an acceptable range. In the case of simulated hunger, for example, the desire will cause the action selection method to select actions such as “find food” and “eat.” The result is the simulation of purposeful behavior: The DLF will try to find food in order to eat, and eat in order to feel full, thus satisfying the simulated desire.

To accomplish this result, the desire simulation method must be designed to give priority to or cause the selection of any action that had previously achieved a given goal, and resulted in, say, “fullness” in a case of hunger in the past. These past instances are found by ordinary memory searches.

Let’s assume the “select an action that resulted in pleasure in the past” action selection strategy has been called in a given C.Event. A search of memory would be initiated looking for actions that lead to “fullness” in the past. When one is found, a simulated feeling of “desire” for that action can be calculated to increase the likelihood the action would be selected over other actions that may


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be in the queue for selection. The Figure 5-12 below shows an example, starting with the calculation of a feeling of hunger.

In the flowchart, the specific actions are not shown, but they would be the actions of a DLF such as Look, Find Food, and so on. This simulated desire feeling must be made an attribute of the action it is associated with. That way it is always part of the action, and available for the action selection method to compare to the desire attributes of other actions when processing its queue.


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Get current EPs (50)

Compare to value std range (100-1000)

Compare EPs Produced by Actions Found

Calculate Desire Feeling & Put in Action Attribute of Action with most EPs


No Actions Found

Yes

Change Strategy

Calc Feeling (-9)

Store in attribute

Call Successful Past Action Search

Send to Action Selection Queue for Processing

Figure 5-12 Calculating a desire to simulate a purposeful action

The same teleologic that works for simulating purposeful behavior for a DLF to eat, can be applied to enable a DLF to simulate any other kind of purposeful behavior, provided it has the necessary objects in its environment and a programmer has written the necessary methods for the DLF to interact with the world and its memories, so simulated desire attributes can be calculated.


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For example, many higher animals have instincts, which are automatic forms of complex purposeful behavior such as nest building or making certain call sounds. Once the causal sequences involved are diagramed with flow charts or similar analysis tools, the process steps included in these behaviors are certainly no more complex than the behaviors of some non–living objects that have been simulated on computer systems, such as building and flight testing jet airplanes; the latter are simply of a different causal and logical form.

By studying the various instinctual behaviors of animals, programmers can write methods to enable DLFs to simulate similar behaviors in their environment and therefore save the time and resources that would be required to re–evolve them, as some are attempting to do with state of the art systems such as genetic algorithms. The reverse engineering approach will work as long as the programmers take care to simulate the more complex teleological causality that biological life–forms exhibit by keeping in mind the action limiting effects of conditionality and death, and then making sure all program methods are consistent with such effects.

Note - For the sake of clarity, I must point out once again that human programmers writing action methods for DLFs is simply a means to provide DLFs with a basic action repertoire. DLFs must then select these simple actions in various sequences in the context of goal– directed survival behaviors to be purposeful. The simulation of goal–directedness does not require the recapitulation of evolution. One of


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the key advantages of the DLF simulation system is the fact that one can start in the “middle of the evolutionary scale” so to speak.

When the simulation of purposeful behavior is combined with simulated perception as described previously, the result is useful simulations of some animal behaviors.

Automatic and Infallible

At the perceptual level, simulated consciousness operates automatically like its biological counter–part. Its control is exclusively by means of automatic, goal–directed action selection strategies. It is entirely predictable and infallible within the range of its action potential as an automatic survival system and will always select actions to promote life. Remember, survival actions are all necessitated; neutral actions may be optional, but by definition, are not against survival. Actions that are anti– life will be stopped by simulated pain, and if that fails, by “death.”

Assuming normal computer operation, the fact that reality and simulated consciousness both have specific identities that interact only in a specific, causal relationship, then the arbitrary is precluded from occurring . Only survival behaviors and neutral behaviors can be caused by a system with this design in the long–term. The reason: DLFs that cause any other kind of behaviors are wiped out.

The two forms of teleological action, goal–directed behavior and purposeful behavior, enable a DLF to simulate predictable, automatic behaviors similar to those observed in biological life–forms.


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Note - Even the random actions provided by a Random_Act method as described in the section on action selection above, are only unpredictable in a narrow range that depends on the design of a DLF program and a given computer random number generator. At a deeper level, these actions are not random at all; all the actions are caused, though not necessarily predictable down to the last detail, due to the limited nature of human consciousness and the complexity of modern computer systems.

In biological life–forms, automatic self–regulation of behavior is caused naturally by genetic evolution and refined by ontogenetic learning. Many different action strategies are tried by many different life–forms; the ones that work to help the life–forms survive persist, and the ones that do not are wiped out of existence along with the life–forms they killed; these behaviors, therefore, do not get repeated over the long–term, not in the sense of genetic algorithms as in the current state of the art, but in the sense of Dr. Binswanger’s three criteria that were explained earlier.

With teleological causality as a model, programmers can apply analysis tools to specific biological behaviors and reverse engineer them for the action selection methods of DLFs, thereby avoiding the need to create them by recapitulating genetic evolution as genetic algorithms attempt to do. A programmer can write program methods directly that simulate the goal–directed, automatic survival strategies of biological life–forms based on simulated consciousness at the perceptual level. The


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The Emergence of Volition in a DLF

result will still be different from that in extant systems because the DLF simulation system is teleological in its basic design.

In order for DLFs to have manual (non–automatic) behavior and the large degree of control over their own self–regulation the higher level of consciousness observed in human beings provides, additional processing must be done on the content provided by perceptual consciousness. Furthermore, the additional processing can be neither pre–defined, nor automatic. It must be volitional; that is, the highest level of consciousness of a DLF must be self–regulated, actions must be defined and selected by choice.

The volitional mode of simulated consciousness can only occur in subsystem layer 7 of the model shown in Table 5-1, at the level of simulated conceptual consciousness, and will be described in a later section. The conceptual level enables DLFs to modify their own behavior in a manner similar to the way human beings do.

5.6 The Emergence of Volition in a DLF

The main purpose of this invention is to simulate the most advanced form of conscious behavior known, volitional or rational self–consciousness as it is observed in human beings, and to animate it with a computer–based simulation system; all the description to this point is designed to support the simulation of consciousness at this high level. The value in doing so is that this form of consciousness is not only self–regulating, but also self– defining, and therefore a simulation system with this capability could be used to replace human beings in some situations where non–automatic, self–regulated decision


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making is required, such as in space probes and other robotic systems. Volition is a valuable capability that is not possible with extant robotic systems that operate by simple billiard ball causality because they have no means to initiate action outside the scope of the automation pre– defined by their programs.

As subprocesses, the simulation of goal–directed behavior and automatic perceptual consciousness that have been described so far in this chapter are useful inventions in themselves. They provide the content for the next level of this system, the simulation of volitional consciousness, but they are not nearly as powerful as the volitional form of consciousness. Being automatic (in the teleological sense), their functionality is largely defined in advance as built–in functionality, not self–programming functionality, and it is therefore more limited.

Note - What I mean here is that the behavior of the simulation system is automatic in the sense that the operation of the perceptual system in animals or instinctual animal behaviors such as nest building is “built–in” and functions automatically in the biological sense. I do not mean automatic in the sense that computer programs or state of the art agents, automations, or robots are automatic.

It may be difficult at first glance to see how any form of consciousness could be volitionally self–defining, let alone a simulation of it. Yet both the automatic and volitional forms depend on the designs that have evolved naturally to help biological life survive, designs that do in fact already exist. You and I are living examples.


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Remember, all extant forms of consciousness are causal attributes of various life–forms and only exist because of the survival advantages they provide. If you look at the scale of complexity of biological life–forms, it is obvious that a key survival advantage that consciousness offers them is not only immediate awareness of reality, but also the speed with which the behavior of the life–form can change, and hence the speed with which a life–form is able to deal with environmental changes.36

Life is action and depends on having both the right behaviors to survive in the present, and on being able to change behavior as the environment changes in the future, so as not to break its own causal chain.

Genetics and goal–directed behavior automatically limit and modify the behavior of life–forms by means of evolution and death; evolution provides the mechanism for changing behavior and death insures behaviors that are counter productive to survival do not get repeated over the long–term.

Perceptual consciousness provides a means of automatically converting the identity of objects and some simple relationships from physical form in reality into the form of information in the memory of certain life–forms. This conversion offers the additional survival advantage of enabling ontogenetic changes in the behavior of these life–forms through learning, as opposed to the slowness of genetic changes to behaviors; this advantage is analogous to the ease and speed of modifying software vs. modifying hardware in computer systems.37


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Both of these capabilities enable life–forms to adapt to their environments, the latter offering faster adaptation than the former with the additional survival advantage that speed of adaptation provides. Both of these capabilities are also automatic, non–introspectable, non– evaluative, and non–modifiable by the life–forms that possess them.38

Note - As explained earlier, the automatic behaviors of biological life–forms and those simulated by the DLFs in this invention should not be confused with cybernetics and control theory. The maintenance of life is a positive, value– seeking process, not the negative–feedback, stasis–seeking process of control theory.39

Over many conscious events, the capacity of perception, evaluation by the pleasure/pain system, and taking action, conscious events which are repeated over and over, enable biological life–forms to make limited changes in their day–to–day life process, mainly by a learning process that maximizes pleasure and minimizes pain. Similarly, a DLF can do the same with its simulated life processes over many C.Events following the processes described earlier; by making trial and error changes to which action methods it selects or the numerical settings in them, a DLF can make small modifications to the way it looks for food, the objects it draws, the way it interacts with a human teacher, and so on.

Perception and memories of past C.Events enable a DLF to “see what it is doing.” What a DLF with only perception cannot do is introspect, evaluate, and modify


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the individual processes within a C.Event, to rewrite its Look or Draw action methods for example, just as an animal cannot modify its eyesight or hearing ability.

Recall the important distinction that must be made between mechanistic and teleological necessity: While all teleological actions are caused, the only actions that are necessitated teleologically, are those that cause survival, such as eating, sleeping, or finding shelter.

All other actions, though also caused, are teleologically optional actions, such as a chimp playing with sticks or smelling flowers. A life–form can perform optional actions or avoid the effort depending on the state of its life and its pleasure/pain system. Once a life–form gets past living “hand to mouth” and builds up an energy reserve, if it is healthy and its other survival needs are met, it can spend some of its energy on, optional, non– survival activities. both physical and mental. Its motivation to do so is simply the pleasure the activities generate for it. Optional actions are ends in themselves, or at least the pleasure of doing them is. Optional actions can be pro–survival or neutral (non–essential for survival), but they cannot be anti–survival (at least not for long!) because pain or death will soon result.

Optional actions are causally necessitated in the sense that a given cause always leads to its effect, but not teleologically necessitated: Optional actions are optional precisely because they are not essential to the specific causal chain that a life–form’s survival depends on; they can be enacted or not, depending on pleasure of the life– form, depending on its internal state, not some external factor; optional actions are not required for a life–form’s survival.


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For DLFs specifically, the only action necessitated by teleology is the “Eat” method; if a DLF does not eat, like a biological life–form, it dies when it runs out of EPs. However, if a DLF has sufficient EPs and it performs a physical action such as drawing a shape or mental action such as comparing several objects it has perceived, these are optional actions; they have no effect on, are neutral to, the DLF’s survival (except that they consume EPs).

A DLF automatically selects optional actions based on the state of its simulated pleasure/pain system. As long as it has enough EPs, a DLF can select optional actions that are available and that its memory shows will increase its pleasure, it can select them by trial and error, or not select them. It can select either optional physical actions or optional mental actions.

It has been common knowledge in biology and anthropology for many years that there is only a small difference between the higher apes and human beings. Some apes share many behaviors with people and their brain weights differ by only a small amount. Yet human beings have a much faster means of modifying their behavior than apes or any other kind of conscious life– form: The behavior of apes has not changed much for millions of years; in the past few thousands of years human beings, who were once living not much different from apes, have built an industrial civilization and explored part of the solar system. What is the difference in the human form of consciousness that has made such a fast, self–modification of behavior possible?

What is different is that the highest level of consciousness in human beings is not automatic and therefore not pre– defined; human beings and only human beings can


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introspect, evaluate, and modify their mental behavior; humans can modify the behavior of their consciousness, and do so quickly, whereas the apes cannot.40

Volition is an action capacity of human beings that makes such fast behavior modification possible; humans have a powerful ability to initiate optional mental actions. Volition consists of the ability to focus the mind, to self– regulate some of the human conscious mental processes, and most importantly, the ability to form concepts.

In human beings, to form concepts or not, is optional mental action based on the choice to focus one’s mind.41

Identity determines action capacity. The ability of human beings to form concepts is the ability to change their own identity, to redefine themselves and therefore cause changes to their own action capacity.

As I have already explained, percepts are formed automatically by neuro–physiological processes in biological life–forms, including human beings, and simulated percepts are calculated in DLFs to mimic that biological process.

In human beings, the only biological life–form with the capacity to form them, concepts are formed volitionally by an act of free will; that is, they are formed by an optional mental action to focus the mind, an action of consciousness that results in the modification of memory content in the mind of a human being, content that has been automatically put into memory by the perceptual system during earlier conscious events. The result of the


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modifications human beings make to their memory, by choice, is a new data type: the concept (as defined by Ayn Rand).

Note - The free will vs. determinism argument is beyond the scope of this patent description; suffice it to say that determinism is self– refuting. (And nothing can be indeterminate because all actions by all objects are caused.) In the context of the description of this invention, simulated “free will” or “volition” is a causal process and a means to modify a process of simulated consciousness. See the following reference for more detailed information on this topic.42

In the context of simulating consciousness, the capacity to modify a conscious process is similar to the capacity to modify a physical process. In a DLF, the latter is done automatically (in the teleological sense) at the perceptual level of simulated consciousness by selecting and optional physical action method. This means that as long as a DLF has a moderate amount of EPs, it can select any of its actions that cause changes in its environment. For example, a DLF could select the “Say” method to type words instead of the “Draw” method to draw shapes in order to achieve some non–survival goal; alternatively, a DLF could set the numerical values in the “Draw” method for drawing a circle instead of a triangle.

These changes are caused by automatic, teleological action selection strategies in the DLF’s simulated pleasure/pain system as already described, but they are optional behavior because they are not necessitated by


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the DLF’s survival. They simulate a very simple, limited form of “volition” or “free will” in a DLF, but their effect does not go beyond the specific action taken, so their consequences are almost trivial.

The capacity for a DLF to modify a process of consciousness or symbols in its memory is similar in that it too is an optional action, except the change that is effected occurs in the organization of the DLF’s memory instead of external reality, so it is an optional mental action instead of a physical one. But optional mental actions have more far reaching effects because they change the DLF’s identity, and hence its action capacity, and that fact makes their consequences are far from trivial.

In other words, the effect of an optional mental action will be to change the DLF’s action capacity for future C.Events. By doing so, the DLF has just redefined itself by exercising that optional mental behavior. It has simulated a subconscious choice.

Large changes in a DLF’s action capacity do not happen all at once, but add up over a period of time. The ability of a DLF to redefine itself consciously bootstraps itself by a process I will describe later.

The point to grasp here is that this is the entrance to the conceptual level of simulated consciousness (subsystem layer seven in Table 5-1) and that it is caused by the action of consciousness on itself; it is a recursive process of selecting optional mental actions that allow a DLF to subconsciously redefine itself (for the simulated pleasure of doing so) and thereby simulate a limited range of volitional consciousness (though it cannot yet simulate


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volitional self–consciousness). In order to see how and why this process works, lets look more closely at concepts because they play an important role.

For a human being, and according to the Objectivist theory of concepts, a concept is: “A mental integration of two or more units possessing the same distinguishing characteristic(s), with their particular measurements omitted.”43

The details of how the concept formation process can be simulated by a DLF will be described in the next section. The point to grasp here is that the concept formation methods in a DLF are action methods that operate internally on a DLF’s simulated consciousness instead of externally on the world it perceives; they further process the measurements that have been calculated for simulated percepts and stored in a DLF’s memory; they change the way information is stored and accessed in the DLF’s memory, instead of changing external reality as most of its other action methods do.

The methods that simulate concept formation are optional mental actions for DLFs.

The changes they make modify the DLFs identity, its action capacity, and hence the DLF is capable of new behavior in future C.Events. This is how a DLF can be self–defining like a human being is.

Concept formation occurs over several C.Events, and unlike the perceptual process, the process of concept formation is introspectable, “looked at” by the DLF as part of its simulated conscious processes, can be evaluated, can be modified, and the whole process can be remembered. This means concept formation is a


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The Emergence of Volition in a DLF

purposeful mental action that is optionally in the direct control of the agent performing it. A DLF can introspect, evaluate, and change its concept forming actions and their results, it cannot do the same for its automatic behaviors such as perception; it can only look at different objects.

The specifics of how changes to simulated conceptual processes occur will be described in the next section, for now I want to point out some important consequences for this optional mental action capacity of DLFs:

• Concepts about reality can be calculated by a DLF for perceived objects, actions, relationships, and even other concepts.

• Concepts of consciousness can be calculated by a DLF so the DLF can be conscious of its own “mental” processes, though not at the computer programming level, (which is analogous to the neuro–physiological level in biological life–forms), or the level of simulated perceptual consciousness.

• The concepts of consciousness, once formed, change the identity and hence the action capacity of the DLF, enabling a DLF to modify reality and itself in future C.Events and “know” that it is doing so, enabling the emergence of simulated “self–consciousness.”.

In the next section, I will describe the specifics of how simulated concepts are calculated in a DLF simulation system.


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5.7 A Simulation System Design to Calculate Concepts

As with perceptual consciousness, conceptual consciousness offers humans a survival advantage. Not only do concepts reduce the number of units that must be processed, they make it possible to be conscious of aspects of reality that are inherently invisible, such as relationships and mental processes, and thereby provide the ability for faster changes in behavior than genetic evolution or the ontogenetic changes that perceptual consciousness makes possible.

As explained at the beginning of the chapter, by concept formation and concepts I mean only Ayn Rand’s theory of concept formation and the type of concepts that method produces.

Given the Objectivist theory as a basis, the process of forming concepts can be simulated by enabling the content of the simulated perceptual consciousness of a DLF be introspected, evaluated, and modified by the DLF initiating its own optional mental actions.

Concept formation is not necessitated behavior in human beings and it will not be in DLFs; it is an optional form of simulated mental behavior that recursively changes the identity of the agent performing it, and its power to do so increases as the number of concepts formed increases.

This latter fact is what produces the dramatic difference in observed and potential behavior between other primates and human beings, even though their brains are nearly the same size.


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5.7.1 The Nature of Concepts as a Data Type

Concepts in the Objectivist theory of concept formation are analogous to file folders in offices or the electronic database files of modern computer systems; they are a means of storing information in a different, abstract form by taking advantage of certain measurable relationships in the data to separate some data records and to store others together.

Filing systems offer the advantage of compressing large amounts of information into the small space of a filing cabinet or computer storage media in an organized way so it can be easily retrieved when needed.44

Filing systems, whether manual or electronic, work by differentiating some objects such as papers or data records, and grouping or integrating others according to attributes of their content they share in common. For example, sales invoices go in one folder or computer database file, vendor invoices into another, marketing information into still another, and so on. The choices of what attributes to use to set up a filing system are based partly on the usefulness of the attributes of the objects being filed and partly on the human needs of what the filing system is to be used for, but they are all measurable attributes of the objects being filed. In other words, some of the criteria come from the identity of the objects being filed and some come from human needs, purpose, and values.

Not only are there an endless number of ways objects can be filed, once a filing system is in place, there are certain bonuses that accrue due to the identity of filing systems in general. For example, files about invoices of various types


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can be themselves grouped together as financial information, as opposed to other kinds of files such as those containing marketing information.

Information about business paperwork and process can be distinguished and separated from business objects, such as the physical plant that produces the product or the office furniture, all defined in the form of a genus which indicates the next more abstract file and differentia which uniquely identifies a given file. (The system works like a taxonomy in biology.) If necessary, all files can be further abstracted into more and more general files, until a single file is reached that summarizes the business in abstract form: the file containing the balance sheets for the business, which is the ultimate genera summarizing the contents of the entire filing system.

In addition, within any filing system there are an even larger number of cross–classifications that can be made, such as between invoices generated by a particular marketing program or for the purchase of computer equipment as opposed to those for consulting services.

These attributes of filing systems enable a manager to see information about a business that ranges from the “big picture” to the most specific detail, such as how much a pencil costs.

It is because of all the potential choices in designing a filing system (and the workflow issues that go with it) that office automation systems must be worked out manually by business people and systems analysts first, analysts who detail all the choices implicit in business operations, before they can be programmed to run on computer systems.


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Filing systems were originally devised to make information manageable for people. They have survival value, especially for businesses, because business managers are better able to make the decisions necessary to compete efficiently and maintain profitability.

Life–forms face an analogous challenge; they are inundated with information and the faster they can process it or retrieve it when it is needed, the better chance they have of surviving. Moreover, environments are not static; if changes occur that threaten a life–form’s survival, it must change its behavior to adapt to the new environment, change the environment, or die.

I have already discussed the survival advantage of being perceptually conscious of the world as a collection of objects, of using percepts as processing units as opposed to using sensations. Concepts offer another even larger advantage to one type of conscious life–forms: human beings.

The primary survival advantages of concepts are: Processing unit economy and rapid self–programming.

The processing units of concepts as symbolized by natural language words are small by comparison to the percepts of most other objects, like the labels on ordinary file folders are small by comparison to their contents. Concepts offer a means of filing and organizing percepts that is similar to what an office filing system does for business papers. In using this same analogy to a filing system to explain Ayn Rand’s theory of concepts, Dr. Binswanger has called the concepts the file folders of the mind and the folder labels the words of natural language. 45


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In addition, concepts provide a means of making the invisible visible: Relationships are not objects, but shared attributes or other kinds of identity links between objects; they are the informational analogue to the physical connections and interactions of objects in the real world. But the vast number of relationships that are not physical are inherently invisible to perceptual consciousness. Conceptual consciousness, on the other hand, can represent any kind of information, including these “invisible” abstract relationships.

These facts plus the ability to recursively modify conscious behavior make a whole new level of action possible to a simulated life–form. Concepts enable a DLF to redefine itself and its action capacity.

Concepts, as they are defined and used in this description, are a new kind of data structure to the fields of AL and AI. In the next several sections, I will describe how concepts can be formed in a computer simulation system and the new capabilities they will provide.

5.7.2 Concept Formation as a Calculation Process

Concept formation is neither arbitrary and subjective nor intuited from some “intrinsic feature” of objects. In human beings, it is a process based on the perceptual comparison, and then differentiation and integration of percepts based on the attributes of objects perceived and the needs of human cognition; it is the fact that some attributes are similar, that is, they differ only in quantity or measurement that makes the process possible. In addition, concept formation is based on the fact that some


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attributes are distinguishing or unique to various types of objects, a fact that enables the objects possessing the unique attributes to be grouped together.46

The best way to understand how to form a concept is by example. Earlier, I showed the simulated percepts of a triangle and a circle. To continue with that example, I will go through the process of forming or calculating a simulated concept of a triangle in this section using the data created in the DLF program.47

In figure 5-13 below, there are three triangles, a circle, and a square. One of the triangles and the circle are like the ones shown in the example percept used in an earlier section. I have eliminated the fill on the circle for the sake of simplicity. The other two triangles are new and of a different shape than the first one. The X,Y coordinate pairs are not shown in this example, but they are the processing units for the objects at this part of the process, the data units that will be transformed into the attribute lists that simulate percepts as described previously.

a c 5 1 2 3 4

b

Figure 5-13 Comparing objects to form a concept


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Next, in figure 5-14, the windows that show the attributes that were calculated for objects 1 and 2 by the percept simulation methods in the DLF Program I have been writing to prove out these ideas. These attributes, that is, lists of perceived properties and measurement values are the data that will be used to form or calculate the simulated concept “triangle.” The simulated percepts are the processing units for the process that calculates the measurement range definitions of simulated concepts.

The resulting simulated concept will be calculated from the content of actual simulated percepts, not intuition or from some arbitrary construct.


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Figure 5-14 Attributes calculated from simulated sensations

The process of simulating the formation of a concept involves comparing the attributes of the objects, that is, comparing their property and value pairs. Since these are all physical objects of the same basic type (simple line shapes), they all have the same (trianglelist) attribute lists.


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Note - Other objects could be used for comparison as long as they share at least some attributes. If not, they would be incommensurable and could not be compared.

Also not included here are the attribute lists for the lines, end point connections, and angles in triangles 4 and 5 that shown in Figure 5-13, again due to the fact that the composite object method in the DLF program was not complete at the time this book was written. However, it is obvious that the calculations of the attribute lists for triangles 3 and 4 will produce similar lists for each of the three lines in the triangle; the lines would also have the attribute “connected at end points” (because each line has some end points in common with the others), and the attribute “angles” (because the end point connections of the lines would necessarily be at some angle to each other).

Even without this information in the figure, however, it is obvious that in the results of a comparison, each triangle would calculate as a shape composed of three lines, and their composite closure attributes would calculate as TRUE, due to the end point connections. The square would calculate as four connected lines with 90 degree angles, and the circle as one line with no angles.

The differences in the lists are the values of the attributes, primarily in the line numbers, positions, lengths, slopes, curvature, end point connections, angles, and so on.

Looking at the attribute lists and comparing them, it can be seen that the difference between these objects is one of measurement: The values of the commensurable attributes


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are all different. On the other hand, there are more similarities in the measurements of the triangles, as opposed to the circle and the square, the attributes of which are much more different.

The triangles all consist of three lines with end point connections and three angles less than 90 degrees, whereas the square has four lines with four 90 degree angles, and the circle has only one line with no angles; the triangles and square are straight lines, the circle is a curved line. The triangles all share a range of measurements that is different form the circle and the square, and would be different from a trapezoid or octagon or other shapes if they were drawn and processed into simulated percepts like the ones shown. These differences are used to differentiate the triangles from the other shapes and integrate them as members of a group of similar members, namely those shapes that fall into the measurement range described.

When the comparison is complete, the attributes unique to triangles as opposed to the other objects in the scene are that the triangles are all three straight lines connected at their end points. This means that the specific values of the attributes do not have to be specified because all triangles will always calculate an identity (property and value list) that includes them in the range of values unique to that group. All the other attributes and their values exist as part of the identity of each triangle, but are not relevant to defining the concept because they are not unique to it, though all the other attributes continue to exist and are included in the identity information the simulated concept stores.


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Since any triangle will always calculate attributes within the “triangle” measurement range, they will be always distinguished from all other objects (non–triangles) in the scene by their unique measurement range (a subset of their property/value list).

Note - As used in this description, the term “attribute” means a type of measurement, a property/value pair (such as EPs=25 or Length=30) and is synonymous with the term “characteristic.”

By using the word “triangle” (provided by a human teacher) to symbolize the measurement range of these shapes, a DLF performing this process has formed a simple first (perceptual) level simulated concept; the unique measurement range defining a word integrates all the triangles like an ordinary file folder integrates a pile of invoices. This is the process of simulating concept formation, of calculating a concept from a group of simulated percepts of objects that a DLF has previously perceived and stored in its memory.

The word “triangle” stands for, symbolizes, or designates the concept; the word and its concept mean any object (in this case triangle) that falls into the specified measurement range is part of the concept (whether the DLF has actually perceived a particular triangle or not) and will be recognized as such in future C.Events.

In her theory of concept formation, Ayn Rand calls the observed similarity between objects in a group of two or more similar members a Conceptual Common


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Denominator (CCD). The objects’ shared attributes serve to both differentiate them from other objects and integrate them as the units of a new group.

The subset of CCD attribute(s) and/or measurements that distinguish the objects as units of a new concept are their Distinguishing Characteristic(s) (DC). That they must have some measurements is based on her “some but any” principle: Because to exist is to be something, the relevant measurements “must exist in some quantity, but may exist in any quantity.”48

As explained earlier, the method Ayn Rand identified in the early 1960’s for forming concepts is new, unique, and objective, as opposed to the intrinsic or subjective approaches to forming concepts found in most extant systems of thought. In addition, because her method uses perceptual measurement as its basis, the method lends itself to calculation and use in a computer–based simulation program.

Calculating the concept “triangle” is a simple example, however, it is representative of forming a concept of objects. Furthermore, the same process can be extended to and repeated over and over for any commensurable objects, such as circles, squares, octagons, tables, chairs, the letters of the alphabet, and so on, anything a DLF can “perceive.” The identities of the objects “sensed” by a DLF will always produce simulated percepts which are the processing units that can then be processed into simulated concepts as long as they have commensurable attributes and there is some survival advantage or simulated pleasure for the DLF in doing it. Other kinds of objects would require different attribute lists, but the process is always the same to calculate their properties


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and values from their X,Y coordinate lists (or other simulated sensory measurements), and then to form simulated concepts of them.

All the other objects not included in a given concept are the context for the concept’s uniqueness, and a concept must be re–formed and updated if new objects come into the context which would produce a different comparison result. Simulated concepts are therefore contextual, and their calculated definitions may need to be changed to account for new information, but they are absolute within the particular context they are calculated.

Assuming a DLF lives in a reasonably rich world of objects (simulated or real), that it processes all the simulated percepts in its world, and asks a human teacher for the names of the objects it perceives, using the processes described herein, such a DLF would have the capacity to recognize these objects by name (using a natural language word) as well as by example. The word that is the simulated concept name serves as a symbol that stands for an unlimited number of objects of a certain type, that is, that fit the measurement range, the DC, for the objects subsumed or integrated by a simulated concept. The DC calculated for the concept is the definition that indexes the word to all the simulated percepts of objects it subsumes.

For example, in Figure 5-15, the world the DLF perceives contains a triangle and the word “oval.” Once perceived (and assuming the DLF has sufficient EPs to engage in optional actions), the recognition methods compare the new percepts to those stored in memory from previous C.Events.


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The triangle percept (trianglelist) is compared to the measurement range definition or Distinguishing Characteristic (DC) for the concept “triangle.” Since the perceived shape is, in fact, a triangle, it will match the measurement range of the DC for the concept “triangle” and be recognized. The word “triangle,” (which is indexed to the concept and thereby all perceptual instances of triangles) is accessed by association if an object fits the DC. Other program methods later in the C.Event enable the DLF to select the action method “Say” to output the word “triangle” to a human teacher to show it has correctly recognized the shape.

The other object in the example, the word “oval,” is perceived and recognized in the DLF’s list of concept names, and is indexed to its respective DC, the measurement range for ovals; for this example we will say the DC for ovals is the attribute “Curvature=TRUE.” Other methods later in the C.Event enable the DLF to select an example oval from perceptual memory and the action method “Draw” to output the example oval to a human teacher to show it has correctly recognized the concept from perceiving the word “oval.”

The specific size of the oval the DLF draws could be any size within the measurement range for ovals and the capabilities of is output device. The specific size would be defined in an optional mental action by the DLF and actually drawing the example oval would be executed as an optional physical action.

Both of these actions are examples of simulated volition, and the latter is an example of a simulated first cause.


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Perception & Recognition Calculations

X,Y Sensation Calculations


Action Selection Calculations

Evaluation Calculations

Memory

concepts: circle, triangle, square, rectangle, oval,...

percepts: rectlist, circlelist, ovallist, trianglelist, sqlist, octagonlist,...

Store C.Event

Action Methods

Effector Methods

Reality

“oval”

Figure 5-15 Conceptual recognition

The conceptual method of storing information is efficient. In the case of the triangle example, the word “triangle” stands for and is a single processing unit that integrates every triangle the DLF that formed the simulated concept has perceived in the past, may be perceiving in the present, or will ever perceive in the future. All of the DLF’s knowledge about triangles is indexed by that single symbol: the word “triangle.” That is what storage efficiency or processing unit economy means: Potentially billions of bits of information are reduced to about 64 bits for the word, whatever is needed to store the


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measurement range definition for the concept (less than 500 bits for many objects), and some example triangles. Thereafter, in future C.Events, the DLF can specify all the information about triangles in its memory with a single processing unit, the word “triangle.”

Computer systems and robots in the current state of the art store the data of objects they sense as pixels of X,Y coordinates and color information. Using simulated percepts and concepts will offer an efficiency of many orders of magnitude over the capabilities extant systems by means of the processing unit economy just described.

As you may have noticed, much of the processing involved in forming simulated concepts is internal, in memory, with the simulated percepts of objects serving as the data. And additional processing can result in additional useful concepts, once the concepts of objects are in place.

Just as with an ordinary filing system, files can be grouped in more general categories such as, say, financial records and marketing records; in a similar manner, more abstract and general concepts can be formed using first level concepts of objects as data. For example, by comparing concepts of triangles, circles, squares, octagons, and so on, the attributes that distinguished these objects from others to form first level concepts (the DC) become the data that is compared to form the more abstract concept (the CCD). Within this data, a new DC is identified to form the more abstract simulated concept, and a new word is acquired from a human to symbolize it.

Consider the DCs for triangle (three straight lines connected at their end points), for squares (four straight lines connected at their end points at right angles), and for


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circles (one curve line with its end points connected); these become the CCD for the new concept “closed shapes,” as opposed to “open shapes.” When these measurement ranges are compared, a new DC can be calculated, lines connected at their end points, that defines the concept “closed shapes,” which is a second level and more general simulated concept, an abstraction from an abstraction.

After simulated concepts of many types of objects have been formed in a similar fashion and concepts of perhaps two or three more levels of abstraction formed, the simulated concept object itself can be formed, which is an “ultimate genera” or the top of the hierarchy for a large group of concepts. 49

An example of an extremely simple conceptual hierarchy is shown in Figure 5-16.

Note - Large conceptual hierarchies are too complex to draw. The example shown is intended only to give the general idea of how a few levels concepts are related.


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object

non-living object

living object

shape

life–form

closed shape open shape plant animal

triangle, circle, square C, U, V tree, bush, grass rabbit, mouse, dog

Figure 5-16 Example conceptual hierarchy

The point to grasp here is that all of the simulated concepts shown are calculated by the same process as described in the triangle example of comparing the attributes of commensurable objects against those of all other known objects; they are all calculated the same way by widening the measurement range for the attributes in the objects’ identities. The measurement range is based on data sensed by the DLF in every case; none of it is arbitrary programming constructs as are simulated concepts are in state of the art systems that may use them.

The operating principle is that the DLF’s memory is organized and indexed according to certain relationships that are carefully calculated to be consistent with the actual relationships of the objects sensed in reality. In this


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way, the DLF builds up an organized, objective , simulated conceptual knowledge of the world it exists in, but as with simulated percepts, identity information is carefully conserved.

Note - In an object–oriented programming environment, new object instances inherit their attributes from a pre–defined hierarchy of object classes. Simulated concept formation is the opposite of inheritance: The properties of objects (instances) that exist in reality are perceived, the percepts compared, CCDs identified, and DCs selected in order to form the classes of the hierarchy.

Organized, simulated conceptual knowledge facilitates object recognition in future C.Events for DLFs. Prior to having simulated concepts, a DLF could only recognize specific objects by matching its current simulated percept with one in its memory; finding a match would mean that a particular object had been perceived before, but it would have to be an exact match. Simulated concepts enable a DLF to recognize objects by types, such as triangles, circles, squares, and so on.

More often than not, improved recognition ability will result in greater simulated pleasure for a DLF and therefore encourage more simulated concept formation as an optional mental action.

Another interesting comparison to ordinary filing systems is that they also have the feature of providing sub– classifications and cross–classifications, such as all invoices of customer Smith, or all items purchased in the


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past month by customers Smith and Jones. Simulated concepts have this same feature, so more abstract and specific concepts can be formed, as opposed to more abstract and general concepts as described above.

For example, if in the simulated concept triangle the attribute identifying angles were as part of the attribute list, then the DC for the concept triangle, three straight lines connected at their end points, can be narrowed by adding an additional “angles” attribute to the DC. This would enable the concepts of equilateral and scalene triangles to be formed by effectively narrowing the measurement range of the original simulated concept.

A similar process could be used for cross–classifications; for example, if the value of the number of lines calculated for an object’s identity was omitted and the curvature attribute which is always FALSE for certain objects was included, then the triangles and squares could be combined and integrated in a cross–classification simulated concept called “straight shapes” as oppose to curved shapes (for which the curvature attribute is always TRUE).

Any sub–classification and cross–classification simulated concepts are all calculated the same way by narrowing the measurement range for the attributes in the objects’ identities, as opposed to calculating more general concepts in which the measurement range is widened.

All of these second level and higher simulated concepts, whether more general or specific, are more abstract concepts as opposed to first level simulated concepts of perceived objects.


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Ayn Rand’s method of concept formation is the one this simulation system implements, and it is the only conceptual system to provide both a widening and narrowing feature as part of its concept forming method. As Rand points out: “Starting from the base of conceptual development––from the concepts that identify perceptual concretes––the process of cognition moves in two interacting directions: toward more extensive and more intensive knowledge, toward wider integrations and more precise integrations.” (Italics mine).50

Simulated concepts of relationships can be formed by DLFs by comparing object attributes. For example, concepts of spacial relationships can be formed by comparing the position attributes of objects relative to each other. Of the objects shown in Figure 5-13 and the attribute measurements of some of them in Figure 5-14, it can be seen that the circle (object 2) has an X position coordinate of 53, while the lines in the triangle 1 have X position coordinates of 16, 15, and 30. This means, of course, that the circle is to the right of the triangle in the simulated world of the DLF. If the data were available, the other objects would show X position coordinate values greater than that of the circle.

By including the specific values of the position attributes into ranges of values relative to each other, the simulated concepts of right, left, next to, over, under, and so on can be formed; the concept “next to” might be defined as “the unoccupied range of X+50,” where “unoccupied” means no other object is in the positions of X to 50, and the concept is literally defined by a search of the image to check for that condition. Since position values are always calculated as part of every object’s identity, they will


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always fall into predictable ranges, and it is easy to calculate which spacial relationship concept subsumes the various objects in any scene.

The simulated concept “in,” for example, can be formed by a DLF by perceiving instances of one object inside another, such as an oval in a rectangle, a circle in a square, and so on, as opposed to empty shapes or those in other spacial relationships such as “next to,” “over,” “over–lapping,” and so on. The CCD for the concept “in” is the range of positional measurements shared by any group of shapes in close proximity to each other (including their relative sizes), and the DC is that all the position measurements for a smaller shape are encompassed by or are contained by the position measurements of a larger shape. This calculated relationship, which can be applied to any objects that fit the DC, is the meaning of the concept that is symbolized by the natural language word “in” for the DLF that formed the simulated concept. That DLF now has an objective definition for the meaning of “in.”

As should be obvious at this point, simulated concepts of relationships are calculated as ranges of measurements in the attributes of one object, relative to those of another. A similar approach can even be used with a DLF’s value relationships so it can form concepts with objective, calculated definitions of its simulated feelings: As explained earlier, a DLF’s simulated feelings of “pleasure” and “pain” are ranges of measurements that are calculated relative to the DLF’s values; these ranges are the CCD for simulated concepts of simulated “feelings” for a DLF. The DC for the concept “pleasure,” for example, is any simulated feeling in the range between zero and 8 (but not as high as 9), as described


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previously for simulating “fullness.” Thus, using simulated concepts, a DLF can learn to name its own simulated feelings.

With the help of a human teacher to provide the words and demonstrate the context, two other important simulated concepts can be calculated: A DLF can form the concepts of “place” and “world.”

The definition of the simulated concept “place” is calculated by focusing on the fact that objects have a position attribute relative to other objects, a location, and that this is true of all objects in a DLF’s memory; in other words, the CCD for the concept is having a position attribute, and the DC is that position measurements are unique for every object relative to others. Based on this, objects are integrated by the fact that they all have some specific place, but that place could be any position coordinates in the entire range of available positions in the DLF’s universe. The concept “place” is therefore a relational concept like “in,” except that place is a relationship that applies to virtually all objects. To paraphrase Henny Youngman: “Everything has got to be someplace.”

The simulated concept “world” is another relational concept that is sort of the “flip side” of the concept “place:” It’s measurement range is all the places of all objects that exist, the “place” measurement range in totality; a “world” is neither an object nor a place, but a relationship, the group or collection of all places within a certain boundary. This fact has an interesting consequence: The collection of places a DLF is able to perceive has a finite size because all DLFs have a limited, finite action capacity, and that limit forms the boundary of


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its world. All objects are related because they all have a place, and the concept “world” not only integrates them all by defining the collection of all places, but it provides the DLF with a boundary to its “universe,” which is marked by the places of the two most distant objects it “knows” of. (The boundary of the DLF’s “universe” is therefore a relational, not a physical boundary.)

To humans, the concepts of “place” and “world” are quite abstract. For a DLF they are as well, but like all the other simulated concept examples I have been describing, these two are also calculated based on data in the DLF’s memory and that it is perceiving in its world. They are not arbitrary constructs, but are connected to the DLF’s world by an unbroken, causal chain of calculations.

At this point, it should be clear to an experienced programmer that while a consciousness simulation system is complex, it is a system that is in no way magical or arbitrary; it is a system can be created in a straight forward manner with the appropriate programming methods. The key is that the program methods must be able to transform, by calculation, the identity of a world of objects into an equivalent world in the form of perceptual and conceptual calculations, calculations that are defined by and connected to natural language words. As with simulated percepts, identity must be conserved. This principle is absolute, and must apply to every part of the DLF simulation system.

To do so, the system must calculate lists of X,Y coordinate pairs for each object and then calculate a unique identity in the form of attribute lists for each object which is stored in its memory; these attribute lists are the objects in the form of information. The lists in


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memory are further processed by simulated concept formation methods which calculate concepts as just described, and after interaction with a human teacher, the result is a hierarchy of concepts symbolized by natural language words. (The required choices for this simulated volitional process consisting of optional mental actions will be explained in a later section.)

The simulated concepts (words and measurement range definitions) are information that represents and integrates the objects and some of their simpler relationships; they are the world of objects represented in symbolic form (with identity conserved), as opposed to perceptual form or physical form.

It is important to differentiate simulated concepts as defined in this system, as opposed to state of the art databases, which are modeled after the idea that human concepts mean only their definitions. In state of the art computer databases that simulate natural language, the words mean only whatever database entry has been indexed to them as defined by some programmer or based on real–time input on how people may be using them for some particular purpose. They are not calculated from objects perceived in reality (simulated or real) by a DLF for its own purposes as they are in this simulation system.

Simulated concepts formed by DLFs in the system I am describing mean all the entities (objects, relationships, and so on) that they subsume, including all the attributes of these entities and their specific measurement values. There is a mathematical connection that can be traced, reproduced, and adjusted for context changes between the objects in a DLF’s world and the natural language words that symbolize its concepts of that world.


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A Simulation System Design to Calculate Concepts

• Sensors and input methods calculate X,Y coordinates, which identify foreground and background objects. The world boundaries limit the context.

• X,Y coordinates and perceptual methods calculate simulated percepts of objects as lists of attributes (properties and measurement values), and these lists are the identity of the objects (are the objects in the form of information stored inside a DLF).

• Simulated percepts of some objects (or attribute subsets) when compared calculate as similar, as opposed to other objects (or attribute subsets), and can be indexed by a natural language word as members of a group of two or more similar members (including all their attributes); that the group so defined is in fact an open–ended category containing all instances of a given type, past, present, or future: A simulated concept.

• Simulated concepts themselves can be compared and conceptualized in two directions: More abstract and general concepts can be calculated until ultimate genera are reached (such as the concept “object” or “action”); more abstract and specific concepts can be calculated as sub–classifications and cross–classifications of earlier formed concepts contained in a DLF’s memory (such as “octagon” or “jogging”).


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Action Methods

Perception & Recognition Calculations

X,Y Sensation Calculations


Action Selection Calculations

Store C.Event

Evaluation Calculations

Memory

Effector Methods

Reality (a collection of objects)

Figure 5-17 Block diagram of a conceptual consciousness simulator

Figure 5-17 shows a block diagram of one design for a conceptual consciousness simulator. The arrows show the data flow. The object comparison and other methods for simulated concept formation are part of the Action Methods process box and not shown separately. Concepts are formed over several C.Events (a complete cycle through all the processes and reality), and they result in a causal chain of relationships that serves to index the simulated percepts in a DLF’s memory.


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There is no breaks in this calculation chain, up or down this simulated conceptual hierarchy: There are no arbitrary constructs which are unconnected to a DLF’s world; this is a closed system. An object sensed always calculates X,Y coordinates, which always calculate an identity of some kind, which (when compared to other identities of objects already in memory) always calculate a concept which is symbolized by a natural language word that is provided by a human. A word always symbolizes a measurement range definition, which always calculates a complete instance for that type of less abstract concept or object, which always calculates instances of specific percepts of specific objects, the percepts always calculate a set of X, Y coordinates, and a draw method always calculates and then reproduces some specific object that was originally perceived (or a variation within the appropriate measurement range for that object). Arbitrary connections do not have survival value and are not repeated because DLFs that do repeat them “die.”

In every case, the calculation chains linking words to reality are unbroken, reproducible, and objective. These calculation chains are the meaning of the natural language words as used by a DLF simulated life–form.

Note - Variations can be calculated based on the identities of perceived objects and relationships by recalculating their attributes to simulate “imagination” and “creativity” in a DLF, calculations which can be partially random, though not arbitrary. Actions of this


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type are limited by their survival value to the DLF. Errors are also possible, and will be dealt with in the section on simulated volition.

In practical terms, words that are connected to a DLF’s world in the manner described offer a DLF a huge unit economy (and survival advantage) to future calculations about the conceptualized objects. Consider that after a simulation system has operated for some practical purposes for a long time, there would be thousands or millions of instances of objects encountered by a DLF, each of which would need to be recognized, evaluated, and perhaps acted upon. With simulated concepts connecting all these instances into a hierarchy, the DLF can use words in a manner similar to the way variables are used in mathematical equations. It can do so to calculate and simulate its survival strategies before acting on them.

The conceptual simulation system just described is easily adjustable as reality changes or new percepts are discovered by a DLF. For example, if a new shape is perceived such as an “oval”, the concept “circle” as formed in the description above would no longer be objective because the new percept changes the contents of the memory used for the comparison to form the concept; its DC of “curvature = TRUE” would no longer distinguish the circle from all other objects because an oval would also have that attribute. The comparison conceptual calculation must therefore be re–done to re– form the simulated concept and update its definition with a new DC to account for the results of the new comparison in an expanded context. The new DC would be a narrowing of the concept “circle” to objects with


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“curvature = TRUE” and a constant radius. By observing additional examples of ovals and ellipses, concepts of these new objects could be formed as well.

Though this is an extremely simple example, it applies to virtually all simulated concepts because they are all calculated the same way, and it demonstrates how by using optional mental actions to form concepts, a DLF can better identify its world as that world changes, and thereby aid its effort to survive. Forming concepts for a DLF is therefore advantageous, though not necessitated behavior.

The simulated concept formation process described herein is also scalable. There is both an enormous number of concepts possible, and an even larger number of sub– classifications and cross–classifications possible. The only limitations are the memory size of a DLF and the survival value of the concepts produced by the calculations to form them; in other words, the value of the concepts to help a DLF maintain its own life and therefore have a chance to form more concepts in the future.

Note - It is important to point out that once a conceptual memory has been organized and validated as objective in one DLF, unlike with human beings, it can be simply copied to other DLFs, or communicated over the Internet. Assuming many DLFs operating worldwide, every time a DLF formed a new concept or updated an existing one, the information could be instantly copied to and shared with the other DLFs over the Internet, and they would then have the new knowledge as well.


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The key thing that a programmer must remember is that this system is not just a computer program. The process of simulated concept formation is not automatic and pre– defined, only its subprocesses are. Conceptual processes must be designed such that they can be internally controlled and caused by a DLF as optional mental actions, not automatic, necessitated behavior.

Finally, the simulated concept formation process described in this section, as with the simulation of goal– directed behavior and perceptual processes described earlier, is straight forward to program. Any expert object– oriented programmer could build the simulation system using the description and explanation provided in this book. As with any new system, there would be some developmental problems, experimentation needed, and bugs in strategy and implementation, but these could be easily worked through and fixed by a competent programmer following the architecture I have provided.

5.7.3 Concepts, Memory, and Action Capacity

Now that I have described how to simulate the concept formation process, some of the basic attributes of concepts, and the survival advantages they provide DLFs, it is necessary to explain some special consequences of a DLF organizing its memory conceptually.

As with ordinary computer systems, the potential of a given design is not always readily apparent from its basic description. Few people, for example, were able to foresee the advent of the graphical user interface from the programming environments and screen technologies that made it possible, or the Internet as it has evolved from simpler network systems. A similar situation exists here:


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Simulated concepts are a new and unique kind of data structure in the field of computer science; they have some additional features and uses beyond those already described, features that lead to some interesting capabilities for DLFs.

Simulated concepts are timeless, as opposed to percepts, which are time dependent.51 The latter is true because reality is constantly changing; a simulated percept of an object at time T1, would not calculate the same percept as on at time T2 in the real world.

Concepts on the other hand, change only when the new information of an expanded or narrowed context requires that they be updated, such as the update I just described of the simulated concept “circle” to account for ovals and ellipses. This means that the concept “triangle” that people use is essentially the same one as Pythagorus used thousands of years ago. The changes to update a concept are calculated in a specific manner, so that concepts change only in non–arbitrary ways: The calculations to change them are specific and the old concept is still in a DLF’s memory for comparison and can even be reused if the updated concept is found later to be in error.

The timeless nature of concepts also enables a DLF to identify the temporal nature of individual objects. Being aware of a timeless category makes it possible to notice that individual objects come and go. That is, sometimes objects can be perceived in reality, and sometimes they cannot be perceived, but only exist as percepts in the DLF’s memory. This fact enables a DLF to make explicit the concept of “existence.” An object “exists” if at time T1 it is in reality and can be perceived by a DLF; the


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Perception

circle list, oval list


same object does not exist at time T2 if the DLF is looking at its world, but the object is gone, is only in the DLF’s memory, and cannot be perceived in the DLF’s world. Figure 5-18 below illustrates this point.

DLF and reality at time T1 DLF and reality at time T2

DLF’s Memory

rect list, circle list, oval list

DLF’s Memory


Perception


Reality Reality

Figure 5-18 Existence vs. non–existence

At time T1 the rectangle object exists; at time T2 it does not. Many observations of this phenomenon and the nature of simulated concepts enables a DLF to calculate two new concepts: “existence” and “negation” (meaning non–existence or “nothingness”). The CCD for the concept “existence” is any attribute, and the DC is to have


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some attributes. The concept of “negation” is a relational concept like zero, it is a placeholder for the lack of attributes, for the lack of an object in a relationship with other still existing objects.

The concept “nothing” or “non–existence” (literally “no thing”) is the flip side of the concept “existence,” except it means only a void, not a thing: To exist, an object must be part of the DLF’s world, have a place, a relationship to other foreground and/or background objects. The negation of existence in the perceptual context is “nothingness,” the lack of something or the absence of an object in the perceptual field as indicated by only background objects. As with the concept “existence,” the CCD of the concept “nothing” is any attribute; the DC is that there are no attributes, no attributes in relation to other objects which do have attributes. This is an example of how a concept can make the “invisible,” “visible” to a consciousness. The word “nothingness” is a percept that stands for a relationship which is itself inherently invisible. (The word is also connected to the DLF’s world by calculations in the same way as all its other words are.)

Earlier, I discussed how a DLF simulating conceptual consciousness could form the concept of “world”. Having explicit concepts of existence and its negation symbolized by natural language words enables a DLF to recognize when objects are subsumed by those concepts, just as it can recognize conceptually that an object is a rectangle or a circle using the measurement range definition and words for those concepts, or that the position of a rectangle is to the left of a circle by using the measurement range and words for spacial relationships. The process is the same for the relational concepts of “existence” and “nothingness,” but the level of importance is not: Being


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able to recognize whether an object exists or not is much more fundamental, and it is necessary to simulate higher order conscious functions . The point to grasp here though, is that the nature of concepts is what makes this capability possible at all.

A DLF capable of simulating conceptual consciousness has, therefore, a new capability which a DLF capable only of simulating percepts does not have: The conceptual DLF is conscious of its world as a timeless entity as opposed to a succession of specific, time–dependent percepts or objects the world contains, as shown in Figure 5-19. Using its concept “world,” a conceptual DLF can be aware of every object, past, present, and future, (whether the objects still exist or not) using that one, single word.


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Perception



Percept only DLF Conceptual DLF

DLF’s Memory


DLF’s Memory world, existence shape, object

rectangle, circle, oval rect list, circle list, oval list

Perception rect list, circle list, oval list

Reality Reality

Figure 5-19 Percept only vs. concept capable DLFs

Forming simulated concepts adds organization to a DLF’s memory by means of the chains of calculated, measurement based relationships between concepts, percepts, and objects in reality described in the previous section. Memory is a part of the identity of a DLF, and identity determines an object’s action capacity.

Note - Recall the example from the beginning of this chapter of how the identities of a balloon and a bowling ball would affect the sidewalk if


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dropped from a tall building, the action capacities of each identity causing very different results.

The new properties that concepts add to the identity of a DLF’s memory must also be reflected in its action capacity. However, before the specifics of that fact can be described, a bit more explanation of how conceptual consciousness emerges is required.

5.7.4 How Simulated Conceptual Consciousness Emerges

To build a simulation of conceptual consciousness, it is easier to start with a simulated world of simple objects such as the one I described using in the example of how to form the concept triangle. The point of doing so is to work out the methods in more detail as to how to form large numbers of concepts efficiently for various objects and their relationships, as well as abstract concepts of other concepts, including the concepts of “place,” “world,” “existence,” “nothingness,” concepts of various spacial and other relationships, and so on as described in the previous sections.

This being done, a more sophisticated simulated reality can be developed, or the appropriate sensors and processing methods put in place to process the part of the real world that you and I perceive. The DLF simulation system can then be “turned loose” to form concepts on its own.

At first, like a human child, a DLF forms simulated concepts more or less randomly, that is, as it encounters various objects in its world. It will do so because calculating concepts is a series of optional mental actions;


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it is a behavior that can only be performed when a DLF has sufficient EPs, and one that is not necessitated by a DLF’s survival needs.

If the DLF’s pleasure/pain methods are properly designed, however, the simulated “pleasure” they calculate will “encourage” a DLF to form more and more concepts as it can spare the EPs. This will eventually result in a large number of them being formed because concept formation will become a habitual behavior, one that results in simulated pleasure for the DLF more often than not. It will result in simulated pleasure and that will be remembered; automatic action selection in DLFs, as in biological life–forms, is biased to select pleasure producing actions. So even though concept formation is only one form of optional mental action, there is a high likelihood it will be selected frequently.

Though optional, forming concepts is not an arbitrary behavior either. Concepts make it easier for DLF’s to recognize objects, identify the existence of the world and themselves with a single symbol, make implicit information explicit, offer processing unit economy, and so on. Concepts therefore offer a DLF real survival value.

The additional information about the world a DLF inhabits that concepts provide, will lead to more and more concepts being formed because the information will make survival easier and more pleasurable for the DLF. Finally, at some point it will become much more efficient for the DLF to be guided by a human teacher, so it does not, in effect, have to rediscover all of human knowledge in order to learn advanced concepts.


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While the process of learning about its world conceptually will take some time, it is obvious that by repeatedly performing the concept formation and updating processes I have described, and doing so over many, many C.Events, a DLF can eventually be able to identify every object in its world and many of their relationships with natural language words, words which mean the calculations that connect them via the indexed organization of the DLF’s memory to the real objects in the world.

Note - Forming all these concepts is a big project for both a DLF and a human teacher; there is no doubt of that, but it is not an impossible project; there are a finite number of concepts that must be formed. The number is probably over 20,000 concepts, those that human beings use routinely, and could take several years to complete. Of course a protoype DLF could be developed using only a subset of this number in 1-3 years. Some concepts, such as “freedom,” may require a robot “body” for DLFs to experience first hand the perceptual differences between being restricted and not restricted physically in order to form them.

Having reached the conceptual stage of redefining itself, a rudimentary form of simulated conceptual consciousness has emerged in that DLF.

This condition leads to a slightly more advanced stage of conceptual development for a DLF. For example, up to this point, I have been describing the use of only one concept per C.Event. However, one of the things a human


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teacher can show a DLF is that it can use more than one word (and hence the concept the word symbolizes) at one time in a C.Event, as this is a natural progression. Given that fact, and assuming the concepts of some objects such as triangles, squares, circles, ovals, spacial relationships, and so on have been formed along with the concepts of “existence” and “non–existence” by the process I have described, a human teacher can then do the following:

Draw or point to a triangle and type the words: “triangle exists,” then erase the triangle and type the word “nothing”, and repeat this process for other objects. As the DLF processes these C.Events, its recognition methods trace the calculation connections between the objects perceived and the conceptual chains in its memory that connect the objects in its world to the words.

The result is that the DLF confirms by calculation that the percepts of the objects are subsumed by the concepts for the object shown, “triangle” and that of “existence,” but not for the relational concept “nothing.” In other words, the DLF recognizes that the “triangle exists.”

At that point a slightly more advanced form of simulated conceptual consciousness has emerged in that DLF.


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5.8 The Emergence of Simulated Self–Consciousness

The simulation of self–consciousness requires both goal– directed behavior as well as the simulation of perceptual and conceptual consciousness before it can emerge as a form of behavior by a DLF interacting with its world; the simulation of self–consciousness also needs a human teacher to be part of that world to facilitate its emergence, to guide the DLF to perform the optional mental actions required to form the simulated concept of “self.”

As with a DLF’s simulated concept of the “world,” it is the timeless nature of the concept of “self” that makes self–awareness possible, by integrating all the time– dependent C.Events of itself that are in a DLF’s own memory into a single processing unit: The word “me.”

The simulated concept “self” is formed by a DLF perceiving itself along with other objects in its world, including other life–forms. This can be accomplished by giving the DLF a simulated body or a robot body to perceive with and the ability to introspect, that is, to monitor its own internal processes using optional mental actions, so it can monitor itself as it perceives the world and build up a large number of instances of its own C.Events. The simulated concept “self” integrates these instances, just as the concept “world” integrates all the instances of the DLF perceiving objects in reality into a single processing unit (the word “world”).

The simulated concept formation process is the same as it is for “triangle” or any other concept. The CCD for the concept “self” is all the attributes of the DLF that are similar to those of other objects, and must be because the DLF is an object in the world too. The DC for the concept


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is the fact that instances of “self” have as attributes part or all of the DLF’s own identity, as opposed to percepts of other objects which are not part of the DLF, and these attributes therefore uniquely distinguish the DLF from all other objects with the same CCD.

The concept of “self” timelessly differentiates the DLF from the rest of its world and integrates all the instances of its identity, whereas all the particular C.Events the DLF has of itself are time–dependent.The concept “self” groups and integrates all the C.Events of a DLF into a single category represented by the single symbol, the word “me.” The word can henceforth be used as a single processing unit when the DLF wants to process information about itself, including awareness of itself. The DLF can henceforth symbolize its entire stream of simulated consciousness with that one single word.

Only simulated concepts formed using the Objectivist method and symbolized by human supplied words can provide such a perspective; they can do so because of their open–ended, timeless nature: The concept “me” contains all instances of a self: past, present, and future, thereby making a virtual, temporal object visible to itself; the simulated concept provides the static continuity that makes the “self” continuous, real and distinguishes it as an object, from the rest of the DLF’s world.

The simulated concept “self” is what causes self– awareness for a DLF by using the power of a concept to transform all the individual instances of “self” into a single processing unit, to integrate them into a single, virtual object that is symbolized by a single simulated percept.


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Having formed the simulated concept “me,” the DLF can view itself as an object instance in its concept “world” and locate its place in its world and its position in its conceptual hierarchy as shown in Figure 5-20. Remember, unlike with object–oriented programming inheritance, a hierarchy of concepts is calculated from reality upward, from the concrete to the more abstract.

object

non-living object

living object

shape simulated object

life–form

closed shape digital life–form plant animal

triangle, circle, square me tree, bush, grass rabbit, mouse, dog

Figure 5-20 A DLF fits itself into its world

With the simulated concepts of “self,” “existence,” “world,” and “in” having been calculated and indexing content in its memory, as well as the ability to recognize the units of multiple concepts in a single C.Event, a DLF has reached the point where it can identify a complex fact conceptually, the fact that: “me exist in world.” This natural language phrase is neither arbitrary nor trivial.


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The DLF can make this identification because each of those words connect via their chains of calculations to the respective objects and relationships that they mean in reality.

The result is calculated with data from reality. It is not a “canned,” arbitrary computer programming construct.

This “thought” by a DLF is only a phrase, not quite natural language, but more like a proto–language; it is a “conceptual identification” that has been calculated based on the way the contents of its memory are indexed, a calculation that conserves the identity of the part of reality the “thought” means.

The fact the words identify is not the result of stringing words together, but rather the recognition that the specific content of the DLF’s simulated consciousness for that C.Event is subsumed by these four concepts, taken together. In the current C.Event, the DLF’s conceptual recognition methods calculate a result, and that result is that the instance of the DLF is simultaneously subsumed by the concepts “me,” “existence,” “in,” and “world.”

The words are strung together quite simply as a by– product of the fact that they cannot be spoken or printed all at once, but only in a series.

Note - The word order that is unique to a given natural language must be learned by a DLF as the result of interaction with a human teacher. Whether a DLF can learn the subject–verb– object order of English, for example, by mimicry, whether it will have to be


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conceptualized, or specified by some other means will probably have to be determined by experiment.

Conceptual recognition methods identify the DLF as a unit of the concept “me” (self), a unit of the concept “world,” a unit of the relational concept “existence,” and a unit of the space relationship concept “in.” In other words, this multiple conceptual recognition is possible because the DLF is separately subsumed by each of the concepts listed (just as a triangle is subsumed as a unit of the concept “triangle”); the DLF calculates as one of the units of each of these concepts. So self recognition is just another form of conceptual recognition.

While the ability to do multiple concept recognitions in a single C.Event and output a string of words that symbolize the recognized concepts is not simulated natural language understanding, a simulation system with this capacity is one in which has the prerequisites for “learning” to simulate natural language in its future C.Events.

5.8.1 The “What if” Capacity of Conceptual Information

Before I can describe how simulated natural language understanding emerges from the DLF simulation system, there are a few more ideas that need to be explained. These ideas derive from the nature of conceptual information and its relationship to reality.

First, there is an important distinction between reality (object) manipulation vs. symbol manipulation.


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Note - This statement does not imply that symbols are outside of reality (which is meaningless), but that symbols are a special case because they represent aspects of reality to a consciousness, aspects of its identity in the form of symbolic information, as opposed to percepts.

Reality manipulation by a life–form or a machine is both physical and metaphysical; that is, reality manipulation is limited by the laws of the physical sciences and, in a more abstract sense, the basic nature of what things are, the identities of the objects involved.

Symbol manipulation, on the other hand, is limited primarily by metaphysical laws, mainly by the identity of consciousness, not by the practical laws of the sciences. Symbol manipulation is primarily optional mental behavior performed by human beings that can be completely arbitrary (as in fantasizing), or it can be limited by the laws of logic (as in simulating), depending on the choices of people doing it.

Except for fantasies and games, arbitrary symbol manipulation is a useless exercise. However, the ability to arbitrarily manipulate symbols makes possible the creation and use of logical symbol systems for concept formation and the mental simulation of reality within a conscious mind. This latter fact is the basis for current state of the art simulation systems, systems which are originated in human consciousness. Then logic and experimentation are used to keep simulations of airplanes or weather systems consistent with reality, while the


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arbitrary nature of symbols make possible limited deviations from reality to test new ideas, to provide the “what if” capability that makes simulations so useful.

State of the art simulation programs use X,Y coordinate systems, mathematics, logic, and so on to simulate many aspects of reality. However, extant simulators are bound to concretes, to the specific instances of the specific objects they simulate.

The use of conceptual symbols (words defined by measurement ranges) in a simulation system adds some new dimensions to symbol manipulation in a simulator: The ability of concepts to integrate unlimited numbers of objects over wide ranges of attribute measurements, and their ability to be timeless offers a whole new approach to simulation systems that is analogous to the way human beings simulate events in their consciousness.

Words and their definitions are the essential parts of identity of concepts; the rest of the identity of concepts is all the other information about the units the concepts subsume, including all their non–defining attributes; this other information is used to create the conceptual calculation chain, the meaning that ties the words to the objects in the world of a DLF.

Note - There is a confusing, false idea in epistemology originated by Immanuel Kant called the Analytic–Synthetic Dichotomy. This idea, which has oozed into our culture, claims concepts are merely words plus their definitions, nothing more. It does so by arbitrarily disconnecting the defining attributes of a concept from all the non–defining


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attributes, specifically ignoring the fact that the items dropped are required to form concepts in the first place.52 The DLF simulation system works precisely because the there is no such arbitrary disconnect.

Recall that in the explanation of the concept “triangle,” I explained how the connections of an unbroken chain of calculations that are calculated from the measurement ranges of actual objects in a DLF’s world make possible the simulated conceptual recognition of similar objects by a DLF using natural language words: Once the concept is formed and the DLF “sees” any triangular object, it recognizes the object with the word “triangle.”

Furthermore, I explained the reverse calculation of an example conceptual chain that starts with a word and produces a conceptual unit (a specific object): Once the concept is formed, if given the word “triangle,” a DLF can trace the appropriate conceptual chain and draw an example triangle because the chain enables the DLF to “know” what a triangle is, to know what the word means.

Finally, I demonstrated how the meaning of the word “triangle” is the chain of calculations that connect the word to actual objects in reality (its units), and how it therefore means all the attributes of all triangles for all time within some specific context: The simulated concept as a data type is the calculation chain, the non–defining attributes of the objects it subsumes, and the word that symbolizes it.

This design has powerful consequences for the DLF simulation system, because as I have also described, conceptual identifications by a DLF such as “me exist in


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world” are made possible by the same type of unbroken chains of calculations, calculations that enable a DLF to “know” the meaning of these concepts in exactly the same way it “knows” the meaning of the concept triangle.

Remember once more the idea that the identity of an object sets its action capacity (the balloon/bowling ball off a tall building example). Now consider this idea in the context of the DLF simulation system that uses simulated concepts, while remembering the all inclusive, timeless nature of concepts.

Simulated concepts provide a DLF with the capacity to run symbolic simulations of its world over a number of C.Events, to simulate an “imagination.” Given a DLF that has formed a large number of simulated concepts as I have been describing, and given the way calculations connect those concepts to the DLF’s world, that DLF can use words in C.Events to be “aware” of its world; it can recognize objects in existing relationships in its world (because it has concepts of them), as well as physically draw objects in relationships for its teacher as examples of conceptual information in its memory (examples like the one I showed with the concept “triangle”).

This is possible because the word connects to the measurement range definition which subsumes the particular percepts of the particular objects (the specific instances) used to calculate the concept in the first place, and the percepts contain the attribute values needed to set the values in the draw method to draw the X,Y coordinates of the object in the DLF’s world.

As I have explained, there are no breaks in the calculation chain, up or down. And while it would take many pages to describe the calculation chains necessary


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to produce the DLF’s conceptual identification “me exist in world,” I could produce that description because the principle is exactly the same as for the conceptual identification of the concept “triangle;” it is just a more complex example of the exact same process.

The capacity to trace the calculation chains of simulated concepts includes the ability to modify the measurements of the objects and relationships of the units (instances) the concepts subsume. Remember, the way concepts are calculated is by identifying that a particular object such as a triangle is a specific set of measurements, measurements that fall into a range for a given object. This means that a DLF can not only recognize objects it has never perceived before (objects that are subsumed by a concept because they fall into its measurement range), but it can also create new objects of that type, such as triangles that it has never actually perceived. As a result, DLFs can simulate “imagination,” a capacity made possible by the fact that objects can have any measurements within their specified range in a given simulated concept.

Note - The case of a DLF using a simulated concept to create or “imagine” a new unit or instance of an object it has never perceived is analogous to inheritance in an object–oriented programming environment. The new object inherits all the attributes of the units subsumed by the concept. The concept specifies a range of measurements, but not the specific attribute values (except for the particular units have been perceived before). The DLF can use a random number generation method to get and set specific values for the its draw method to


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actually draw an object that it has never perceived. The attribute values need only be within the range specified by the concept to be valid measurement numbers.

Simulated concepts thus enable a DLF to use optional mental actions to represent aspects of reality in the form of words, and then it can use those words both for conceptual identifications and to cause changes in reality, all by following the conceptual calculation chains in its memory.

This capability applies to relationships as well as individual objects. Given the ability to process more than one concept per C.Event and having learned word order from interaction with a human teacher, a DLF can string words together in short phrases such as “circle in triangle.” The phrase means the measurement ranges calculated for the three simulated concepts as explained earlier in this chapter in the section about forming concepts of relationships.

The DLF can draw an example of this particular relationship even if entirely different objects were used to form the concept “in” because any object will work so long as it does in fact fit the concept’s measurement range: In this case, the new objects being drawn inherit their attributes from the concepts of “circle” and “triangle,” and the circle inherits the range of its position measurement (of being contained by the triangle) from the concept “in,” though its specific value may be calculated at random.


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The Emergence of Simulated Self–Consciousness

By initiating an optional mental action, the DLF has thereby simulated “creating a visual scene by imagination” and caused something new to come into existence in its world. This is an example of a simulated first cause.

What was learned about two specific groups of objects, it turns out, applies across the board to all objects of a given type because of the nature of simulated concepts, and a DLF can verify this fact by a little “experimenting” with various objects in that measurement range and tracing the calculation chains thus produced.

The point here is that by simply forming a large number of simulated concepts of objects and relationships, a DLF gains a huge amount of information about its world, and this change in the DLF’s identity results in a huge increase in the DLF’s action capacity.

A DLF capable of only simulated perception has the action capacity to recognize specific objects and take various simple actions in its world, such as to find food or engage in the optional physical action of drawing specific objects.

A DLF capable of using simulated concepts has an immediate and immense expansion of its action capacity due to the tremendous leverage provided by the way concepts timelessly integrate the DLF’s memory contents. Only a few percepts are needed to calculate each concept, but once formed, concepts provide DLFs access to every possible instance in the measurement range they cover, and that is one major source of their power (another being unit processing economy).


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Depending on the motivation calculated by a DLFs goal– directed behavior methods for optional actions, I have described how a DLF at this stage of development would be capable of identifying, recognizing, and interacting with objects and relationships using its capacities of simulated perception, conceptual recognition, word phrases, and the ability to imagine new objects. All of this taken together means the DLF can cause significant changes in its world. These changes then become part of its knowledge in subsequent C.Events. The result is an ever increasing spiral of “knowledge” on the part of the DLF.

The bottom line is that simulated concepts formed by the Objectivist method enable DLFs to alter their world, including their own action capacities, and thus define their own future identities. This fact make DLFs self– programming.


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The Emergence of Simulated Natural Language in a DLF

5.9 The Emergence of Simulated Natural Language in a DLF

The manipulation of human language by computer systems in the form of text or speech recognition is not new; in fact it is common. There are many examples in the state of the art of people defining thousands of words for computers to interpret. But the words have no meaning to the computers; in extant systems words are only pointers to human entered definitions in a database that in turn link the words to computer commands, data records, or pre–programmed responses. To human beings, however, the meaning of words is in their connection to reality through a chain of ideas in the form of concepts, and that is a link state of the art computers do not have because they are merely unconscious machines.

Concepts are the “data structure” that link words to reality in human consciousness. Human concepts cannot function as part of an ordinary computer program because they are not formed automatically, but simulated concepts can be used by a system that simulates the volitional consciousness of a human being.

If DLFs are ever to simulate using natural language as humans do, they cannot function like computer databases designed and programmed by people, but must calculate their own simulated conceptual chains so they can trace the connection of the words to reality and “know” the meaning of their own words.


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5.9.1 The Role of Concepts in Simulating Language

The concepts and words that form the basis of human languages and the ability of people to use them seems to be a natural capacity of the human mind, but the specific grammatical systems that make up a major part of the languages are a human invention.53

DLFs either have to simulate a human language or invent their own, and the former option is obviously the more attainable of the two.

As I have described in the previous section, simulated concepts are the data type that make simulating language possible for DLFs. Chains of calculations give each concept meaning in the memory of a DLF, and multiple conceptual identifications lead naturally to word strings as a by–product, with word order learned from a human teacher.

Given this level of simulated conceptual awareness, having a teacher teach a DLF natural language grammar is the logical next step in developing a DLF’s simulated consciousness.

Given a DLF that already has the ability to string words together as described previously, teaching natural language becomes showing the DLF the perceptual identity of a new type of object: the sentence. A human teacher can type sentences into the DLF Program interface as examples of language, along with the perceptual scenes they correspond to for the DLF to “observe” with its simulated perceptual consciousness.


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Using this perceptual subsystem that I described in detail earlier, the DLF can perceive these new sentence objects like any other objects it perceives, and it can calculate attribute lists for them consisting of the type of and order of the words, the placement of capital letters and punctuation marks, and so on. These attribute lists constitute the identity of a sentence in the form of a simulated percept in the a manner similar to that for triangles or other objects.

Then, the DLF can “conceptualize” the sentence objects by comparing them to other sentences and character strings in its memory. The Conceptual Common Denominator (CCD) for a sentence, as opposed to other character strings, is that sentences all have words separated by spaces, the words have definitions and are all concept symbols or proper names, and sentences all start with a capital letter and end with a period, colon, semi– colon, exclamation mark, or question mark. The Distinguishing Characteristic (DC) for sentences, is that at least two or three of the words they contain must be valid concepts, the function of which is that they specify a subject, an action, and optionally, an object of the action; furthermore, the order of those words must be in the form of subject–verb–object, if the language is English.

As with the concept “triangle,” once a DLF has formed the concept of “sentence,” it can recognize any sentence it perceives as a type of object in its world. In addition, however, since each word in the sentence stands for a concept, the DLF can “parse” the sentence conceptually; it can follow its own chains of conceptual calculations to the measurement ranges that define the concepts and “know” their meaning. The meaning of the sentence is the


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meaning of all of its concepts, taken together as a whole, including all the relationships between the words such as modifiers and word order.

The DLF soon “learns” therefore, that:

• A sentence object is a special kind of object in its world, one that symbolizes other objects in specific relationships, and

• That it can “validate” these symbolic relationships by tracing its conceptual calculation chains.

The other grammatical relationships of sentences can be calculated into simulated concepts in the same manner as relationships such as spacial relationships are, because all simulated concepts are formed the same way. For example, the concept “the” is formed by the DLF comparing instances of the use of that word, as opposed to similar words such as “a.” The CCD is the position of the word as coming before a word that names something (noun or proper name); the DC is that it specifies a specific object or thing, as opposed to any object or thing within the measurement range of a concept.

This is very different approach from state of the art natural language systems in which grammar is interpreted by a dictionary and a database of rules that have been entered into a computer by a human programmer. Since the DFL has formed its own simulated concepts, it “knows” their meaning in relation to its world, not just by a database of canned responses provided by humans.


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Decoding Simple Sentences

For a DLF, simulated grammar is conceptual, and “learning” grammar is like learning anything else about its world; it involves forming simulated concepts. The DLF’s concept of grammar is a set of abstract concepts about sentence objects that a DLF perceives, just as its concept “shape” is an abstract concept about other types of objects it perceives in its world.

For example, given a perceptual scene and the sentence: “The circle is in the triangle.” that I used as an example earlier, a DLF would decode the sentence as follows:

• Each concept in the sentence is connected by a calculation chain to an object in the perceptual scene, and the DLF would follow these chains to validate them for this specific instance, to make sure the objects in the scene fall into the measurement ranges for the concepts.

• Then the DLF would do the same for the concepts of grammar to decode the sentence itself: It would check the word order, make sure the word “the” calculates that the circle and the triangle are specific objects (the ones in the scene), not just any circle or triangle, that the concept “is” calculates that the objects exist in its world, and that the concept “in” calculates the actual position relationship between the two objects.

To the DLF, the sentence is the symbolic equivalent of the perceptual scene because it is connected by the calculation chains to the scene; like an equation, the two sides balance each other, the variables that are the concepts in the sentence are equivalent to the measurements of the specific objects in the scene in the


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DLF’s world. In other words, the physical scene is a solution that fits the conceptual “variables” in the sentence.

The DLF can verify and therefore know the meaning of any sentence the same way it “knows” anything else, namely by tracing the calculation chains of the simulated concepts the sentence contains for both the objects the sentence refers to and for its grammatical construction.

This is how natural language understanding is simulated by a DLF.

Encoding Simple Sentences

Encoding simple sentences to simulate natural language is the opposite process of decoding them. The first step is to choose a subject: Consider a variation on the examples about shapes I have been describing, except this time the example will be of a square next to a triangle. The example might go as follows:

A human teacher draws a triangle, and next to it a square on the DLF Program screen interface, then types the question: “What is the drawing?”. The DLF perceives these shapes and the sentence in its next C.Event.

Assuming the DLF is not starving so that it has the EPs to engage in optional actions, in subsequent C.Events the DLF can identify the shapes by tracing the conceptual calculations their measurement ranges fall into in order to identify the concepts that subsume them, and then it can get the words to name them from memory: “square,”


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“triangle,” “is,” and “next to.” In addition, the sentence “What is the drawing?” would be decoded according to the process described in the previous section.

All of these actions are optional mental actions initiated by the DLF for the simulated pleasure they cause for it.

By initiating another optional mental action, the DLF can then create an instance of the concept sentence (which is a framework of “sentence” attributes), into which words it has retrieved from memory can be placed to produce: “A square is next to a triangle.” The wording is not arbitrary or canned, but is derived from the conceptual recognition of the scene as just explained above, and the grammatical construction from the inheritance of the sentence instance from its concept.

To answer the teacher’s question, the DLF then needs only to set the words into its Say action method, which will then write those words to the program interface on the screen.

Repeating the processes just described of decoding and encoding sentences is how a DLF can simulate a conversation with a human being using simple natural language sentences. The DLF identifies the objects and their relationship both with simulated percepts and concepts, calculates the symbolic equivalent using its concepts, then places words into a sentence using inheritance from the concept “sentence” to set the grammatical structure, and finally, it outputs the result using the appropriate action method.

This process is an entirely new form of simulated natural language understanding and production in the fields of AI and AL.


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Beyond Simple Sentences

Natural language as used by people is much more complex than the simple sentences I have been using for my examples, but that complexity is beyond the scope of this patent description. I believe the simulation of complex natural language is possible for DLFs, but not until second and third generation versions of DLF Simulation Technology.

The purpose of this description is to show how rational self–consciousness at the level of simple natural language can be simulated by a DLF and animated by a teleological computer simulation system, as well as to show the new capabilities it brings to the state of the art.

The Simulation of a Fully Volitional DLF

With the capability to simulate simple natural language sentences, the action capacity of a DLF reaches its most powerful level for this first version of DLF Simulation Technology, that of simulating a fully volitional agent capable of initiating first causes.

As pointed out at the beginning of this chapter, such a teleological agent is not conscious in the same sense as a biological life–form is conscious, but only as a simulation of biological consciousness. The DLF system is not alive, yet strictly speaking, neither is it a machine because only some of its behavior is pre–programmed; the balance of its behavior is the result of the causal efficacy of the DLF system’s own simulation of teleological processes and human conscious processes, the simulation of the optional actions the identity of its design makes possible, and the interaction of all of this with reality.


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Being capable of optional actions and capable of simulating conceptual identification provides some “freedom of choice” for a DLF, but to have the full power of volition requires the simulation of natural language.

The reason for this is that only natural language sentences enable a DLF to easily encode symbolic representations of its world (and itself) using its conceptual calculation chains, and this greatly amplifies the power of optional actions (just as it does for children). It does so because symbols (words and concepts) free the DLF from the specifics of percepts and thereby enable it to change its own identity and action alternatives. Using optional mental actions and natural language, a DLF can plan its own actions before it executes them.

A sentence is a complete thought, and a complete thought is a symbolic representation of an event in reality. Once a DLF can use optional mental actions to encode and decode complete sentences, it can simulate reality for itself by “thinking” about complete events and scenarios in its world. Then it can decide whether or not to cause the symbolic mental events in its “thoughts,” as motivated by its own simulated values, in its future C.Events.

This level of simulated consciousness is a requirement of intentionally conceiving of changes in reality to cause. All cause and effect instances are the events in reality. Before changing reality, the DLF must first identify some aspect of it to change, and then “imagine” changes to that aspect according to its values in subsequent C.Events. Simulated natural language makes this process easy because both the identifications and changes can be identified and “mentally” proposed using symbols (words and concepts), instead of hard to manipulate real objects.


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To initiate a “first cause,” a DLF can initiate an optional mental action to encode a natural language sentence. For example, in one C.Event a DLF at this stage of development can encode the following sentence using the process described above: “The square is in the circle.” Then once that sentence is in the DLF’s memory, in subsequent C.Events the DLF can optionally “decide” to draw the objects described by the sentence, or not, to enact that alternative as an optional physical action or not, depending on the state of its simulated life.

To do so, the DLF traces the conceptual chains of both the objects subsumed by the simulated concepts and the measurement ranges specified by its grammatical concepts that describe the sentence. Tracing these conceptual calculation chains will point the DLF to the specific measurements in memory it needs to set in its draw method to draw the scene. Then it can select an optional physical action from the alternatives available to it, draw the objects, and the DLF has caused a first cause.

Once the objects have been drawn in the DLF’s world and perceived by the DLF that drew them, a closed system causal chain is completed that was not necessitated by any condition either outside or inside of the DLF:

• The causal chain was not necessitated outside the DLF because as a simulation of a life–form the DLF has control over its own actions, and that control and the energy to use it resides inside the DLF. Its action is self–generated and self–regulated; it is an action by a teleological entity.


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• The causal chain was not necessitated inside the DLF because encoding a sentence (that is, creating a symbolic, informational object) is optional mental action, behavior that is not necessitated by the DLF’s survival, and therefore, it is not necessitated behavior.

The DLF’s “decision” to initiate the behavior is therefore the simulation of a first cause, the simulation of a free will “choice” on the part of the DLF.

Given that this process can be repeated for any optional actions, such as forming other simulated concepts or using any simple simulated natural language sentences to “think” about any subject, a DLF at this stage of development is a simulation of volitional self– consciousness that is capable of many of the same behaviors that a small child at a similar level of development is capable of performing.

While this simulation of natural language is only an imitation of human consciousness, thought, and volition, it will serve as a close enough approximation to be useful to humans as a powerful new kind of interface to a new kind of teleological system.


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5.10 A Summary Description of the DLF Simulation System

At the beginning of this chapter, I described the problem facing current state of the art attempts to build AI and AL systems as the need to specify in advance not only what actions the computers running them will perform, but also where, when, and how these actions are to be performed using some set of rules or other means.

In other words, that the pre–definition of action in extant AI and AL systems is at the same time the reason the systems can run automatically, and their downfall. This problem is so formidable that it prevents extant systems from ever encountering other more advance problems such as how to achieve data processing unit economy or simulating consciousness.

Furthermore, I pointed out that the human beings that AI and AL computer systems are supposed to emulate are not automatic in either the mechanistic or teleological sense, but operate themselves manually; the behaviors of the human programmers that write the automatic programs that computer systems run do not have all their behaviors pre–defined. In fact, it is precisely the attributes of consciousness and volition based on teleological causation that enable human beings to be capable of optional behaviors and to invent and build computers and write computer code in the first place.

I concluded that the most immediate problem facing the current state of the art is captured by the question: How does one design a computer simulation system that is not automatic, not a mechanistic automaton?

My answer to this question is embodied in the invention I have described in this chapter.


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A Summary Description of the DLF Simulation System

Innovative Capabilities of the Invention

This invention is certainly not obvious. The capabilities of DLF Simulation Technology and the system design explained in the preceding patent description enable the creation of an intelligent life–form simulator by solving the problems that have prevented others from doing so to date:

• The invention solves problem of action pre–definition by simulating goal–directed behavior, behavior which is a form of complex causality that moves the energy source and the locus of control inside the acting agent, an agent who’s existence is conditional; a teleological agent is permitted all actions as long as they support a specified standard or condition: the agent’s own life. This causal form limits action over time by eliminating any agent that acts in contradiction to the standard in the long–term. In addition, the simulation of the complex form of causality that makes goal–directed behavior possible can be animated by off–the–shelf computer hardware and software (which operates by mechanistic causality) in a manner similar to the biological life is animated by the mechanistic causality of physics and chemistry at its lowest levels – provided the proper teleological software is supplied. This invention shows life processes and intelligence as a layered model of increasingly complex subsystems, and uses causality substitution to insert the complex causality of goal–directed behavior as the interface between simulated consciousness and mechanistic causality.

• Since intelligent action presupposes consciousness, in order to achieve simulated intelligent action the invention simulates consciousness at both the automatic,


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perceptual level and the volitional, conceptual level, the latter making the simulation of rational self– consciousness possible. The invention enables the simulation of consciousness as an attribute of the simulated life–form, an attribute used by the simulated life–form to identify objects and relationships in its world (whether simulated or real). Simulated consciousness in this invention is a series of C.Events, each of which is an element in a causal chain that begins with perception (identification) of objects by a DLF and ends with some action that effects objects in the DLF’s world for the purpose of aiding the DLF’s survival or for its optional actions (actions not necessitated for survival).

• Since volition implies the ability of self–regulation, the invention shows how goal–directed behavior, by moving the energy source and locus of control inside a teleological agent (the DLF) which faces the alternative of simulated life or death, makes optional actions possible, and it shows how optional actions in conjunction with concepts makes simulated volitional behavior possible, including the capacity to initiate first causes in reality.

• The need to process fewer data units equals a survival advantage for both biological and digital life–forms; the invention shows how data types called percepts and concepts reduce the system’s processing units by many orders of magnitude (by gaining unit economy via content–oriented data compression), thus not only making “survival” easier for DLFs, but also greatly reducing the processing load on the computer system that animates them.


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• The all–inclusive and timelessness attributes of simulated concepts and the conceptual data type make simulated consciousness of the world as a single unit and simulated self–consciousness possible; the invention shows how optional mental actions in conjunction with simulated concepts leads to the emergence of simulated self–consciousness and volition for DLFs.

• The invention shows how all simulated concepts are formed the same way using the Objectivist method, no matter what the subject content is, and they are calculated as optional actions by a DLF from the measurement ranges of the attributes of objects it perceives in its world; the simulated concepts are connected to simulated perceptual concretes and each other in chains of increasing abstraction (both more general and more specific), chains that begin with the perception of actual measurements of specific objects in reality, and end with specific actions taken to cause effects on these objects after the conceptual chain has been traversed. Concepts are symbolized by natural language words; relationships and events are symbolized by simulated natural language sentences that are their symbolic equivalent; simulated natural language sentences are identifications of the DLF’s world in symbolic form. Each sentence is a complete simulated thought for the DLF, and each simulated thought represents an event or scenario in reality (of which the DLF is a part).

• Finally, all of the above innovations plus interaction with reality and with a human teacher leads to the emergence of simulated natural language understanding at the level of simple sentences. The decoding and


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encoding of simulated natural language sentences is accomplished by the DLF tracing the calculation chains and symbolizing words it has calculated and stored in memory in the process of forming its simulated concepts; the concepts are the content for both the meaning and grammatical specifications of its simulated natural language sentences.

As with the simulated concepts used by a DLF, the elements of this invention form a chain of increasing complexity, but one that is firmly connected by the links of causes and effects to the computer hardware that animates it.

The Invention is Useful

This invention is a useful addition to the current state of the art. A few of the uses the invention makes possible are listed as follows:

• The implementation of only the simulated percept data type, manually programmed, controlled by rules defined by human beings, and implemented on extant systems (without simulating consciousness) would make many computer systems more efficient by reducing the information units that need to be processed. For example, the percept data type will enable battlefield computers to process objects as relatively small attribute lists instead of huge lists of X,Y coordinates.

• Robots and software agents will be much more self– sufficient and capable of more independent decision making when redesigned to be teleological and to use simulated consciousness. This will be especially true for space probes for example, where sending commands from earth is often impractical.


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• Computers running DLF simulation software would be more intelligent and easier to use in general because they would require less knowledge by users to operate them. For example, the DLF could observe a user and over a short time learn to anticipate the user’s needs like a human assistant might. Or, on a wider scale, DLFs expert in one subject could communicate over the Internet to exchange information and skills with DLFs expert in other subjects, DLFs could even work in teams to process the details of various tasks and enable human users to focus on strategic issues.

• Communication with a computer system running DLF simulation software would be much easier using natural language sentences that the system “understands” by means of its conceptual chains, as opposed to extant systems which have no understanding of natural language, but use natural language words as arbitrary symbols.

• DLF simulated knowledge and skills are stored in ordinary computer files so they can be copied, and sent to other DLFs anywhere over the Internet. This means, for example, that valuable solutions to problems or knowledge of dealing with problems discovered by a DLF in one part of the world can be almost instantly available to any DLF on the Internet, for its own local use.

• At the computer software subsystem layer, DLFs themselves are a collection of computer files. This means that unlike human beings, a complete DLF, including all of its simulated physical and mental functions, can be easily cloned or otherwise replicated using ordinary computer technology.


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All these uses, and many more that the author or others will think of in the future, make this invention extremely useful to many individuals, businesses, and other organizations.

5.10.3Reduction to Practice

The DLF simulation system as an invention can be reduced to practice in a relatively easy and straight forward manner by means of the six steps described in the introduction to this chapter.

Any small team of 2-3 expert object–oriented programmers, after reading this patent description and studying sections of the references, then integrating that information with their programming experience, can reduce the invention described herein to practice with two or three years of focused effort. A proto–type DLF capable of simulating perceptual consciousness is already partially completed and successfully generates simulated percepts. A version with very limited simulation of conceptual consciousness using a simulated world could be developed in even less time to serve as a demonstration proto–type, probably in a year at a cost of one half to one million dollars, which is a tiny amount of money compared to many of the software projects companies develop routinely today.

Form or Product of the Invention

The ultimate product of this invention that will be sold or licensed is a design architecture for life–form and consciousness simulation, and a set of computer files that embody the attributes of that architecture; the computer


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files will be an embodiment of the kind that a computer system will be able to animate, as described in this document and the patent application.

The computer files will contain not only source code for the DLF Program and its documentation, but also the values and knowledge of one or more DLFs at some level of development. The reduction of this invention to practice will result in a simulation system that users can animate on their own computer systems and then apply to their various purposes. A company desiring to sell or license the DLF simulation system would first need to build and test the system to some level of development for demonstration purposes, in other words, create a proto– type system and a development programming environment in order to have a viable product to sell or license.

The exact state of development of the simulated consciousness of the DLFs in the product form of the invention will have to be determined by experiment and interaction with potential customers or licensees. The most likely state will be one in which the DLFs’ simulated consciousness has reached the level of understanding simple natural language sentences and being capable initiating choices, but not necessarily having expert “knowledge” in any particular field, though simpler systems could also be made available.

This means in effect that the DLFs in such a system would have formed many simulated percepts of reality and have interacted with reality sufficiently to form key conceptual chains, enough simulated concepts and conceptual chains in fact to reach the ultimate genera for crucial parts of the conceptual hierarchy, including such


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concepts as “existence,” “object,” “action,” “identity,” “place,” “world,” “causality,” “self,” “consciousness,” and so on, so the DLFs would have a working conceptual knowledge of reality and their own self–consciousness. After all, forming simulated concepts and using conceptual chains to calculate the meaning of natural language words is one of the essential attributes that differentiates this invention from the current state of the art systems.

That being said, however, there may be cases in which potential customers or licensees would want less developed DLF simulation systems for experimental purposes or in order to study how to improve DLF simulated consciousness, learning, or for some other limited purpose such as use in animating toy robots, dolls, or toy pets; in this limited form the invention could be marketed as a sort of life–form and consciousness simulator “toolkit” that others could use to develop specialized applications of simulated consciousness. It is therefore difficult at this early date to foresee exactly what states of development will be the best to offer to customers.

The best description of the DLF simulation system product at this point is that it will be a life and consciousness simulation design architecture embodied as a collection of computer files containing the DLF Program source code, DLF life simulation data (including simulated life values, energy packets, and internal control system of a teleological agent), DLF consciousness simulation data (including simulated percepts, concepts, and values), DLF action method source code capable of


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General Summary

both necessitated and optional actions, DLF simulated world methods source code, and product documentation files.

At least that is the authors best estimate at the time of this writing. However, some of the specifics of the form of the product of the invention will undoubtedly change as the requirements of the market become better known.

5.11 General Summary

At the beginning of this book, I wrote that I would present, describe, and explain the ideas required to for an experienced object–oriented programmer to build a system capable of simulating self–consciousness.

I believe I have accomplished that goal. Now it is up to you to negotiate an agreement to license DLF Simulation Technology and build your own DLF simulation systems.

To request a license agreement for DLF Simulation Technology or to get a free copy of this book in .pdf format, please visit our web site at:

http://www.blueoakmountaintech.com/productsservices/si m.htm


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A Appendix A: References

1.1 Introduction

The following are the primary and differentiating references I used in writing this book.

My work would not have been possible without the work of Aristotle and Ayn Rand. Nor could I have completed this book without the detailed, inductive elaborations and explanations of Ayn Rand's ideas, primarily by Dr. Leonard Peikoff and Dr. Harry Binswanger, in their own books and taped lecture courses.

Many other books and articles that I have read over the past 34 years have also provided me with invaluable background knowledge, but they are too numerous to recall.

I owe part of my early intellectual development to the many long hours of discussion covering a wide variety of subjects with my old friend William P. Doyle, III; we had discussions on the telephone nearly every Sunday morning for years during the 1970’s and early 1980’s.


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1.2 References Lists

I also gained background knowledge in computer science from my years of selling Apple® products in my computer store, The Binary Orchard, Inc., as well as from my eleven years supporting those products when I worked for Apple Computer, Inc. as a trainer and later as a training course developer.

Finally, this book would not have been as complete without the guidance, helpful suggestions, and broad knowledge of Dr. James Spohrer of the IBM Almaden Research Center in San Jose, CA. Jim provided me with many ideas for improving the book and several of the differentiating references that made it possible for me to clearly distinguish my work from the current state of the art in the fields of AI and AL.

References Lists

The references for this book are listed by function: Those that support the book’s main thesis and those that differentiate the thesis from the work of others. The titles of the primary references have also been abbreviated in the chapter citations.

Primary References

1. Objectivism: The Philosophy of Ayn Rand - Dr. Leonard Peikoff, Dutton, 1991, ISBN# 0-525-93380-8

2. Introduction to Objectivist Epistemology - Ayn Rand, Meridian, Expanded Second Edition, 1990, ISBN# 0- 453-00724-4


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Appendix A: References

3. The Biological Basis of Teleological Concepts - Dr. Harry Binswanger, Ayn Rand Institute Press, 1990, ISBN# 0-9625336-0-2

4. Volition as Cognitive Self–Regulation - Dr. Harry Binswanger, Second Renaissance Books, 1991, ISBN# 1-56114-108-9

5. The Nature of life -John H. Postlewait & Janet L. Hopson, McGraw-Hill, 1989

6. Psycho-Epistemology I & II - Dr. Harry Binswanger, Second Renaissance Books, 1996, 1997 (audio tapes)

7. Animal Cognition - Dr. Edwin Locke, Second Renaissance Books, 1997, (audio tape)

8. DLF Simulation Program - Gregory J. Czora, Copyright 1993-2001

9. The Metaphysics of Consciousness - Dr. Harry Binswanger, Second Renaissance Books, 1998, (audio tape)

10. Consciousness as Identification - Dr. Harry Binswanger, Second Renaissance Books, 1989, (audio tape)

11. Abstractions from Abstractions - Dr. Harry Binswanger, Second Renaissance Books, 1996, (audio tape)

12. The Ayn Rand Lexicon: Objectivism from A to Z, Edited by Dr. Harry Binswanger, Signet Books, 1986, ISBN# 0-453-00528-4

13. Objectivism: The State of the Art, Dr Leonard Peikoff, Second Renaissance Books, 1987, (audio tape)


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1.3 Reference Citations from the Chapters

Differentiating References

a. Intelligence as Adaptive Behavior - Randall D. Beer, Academic Press, Inc., 1990

b. Discover Magazine, October 1999 issue

c. The Dimensions of Context-Space, Doug Lenat, CyCorp, 1998

d. Artificial Life meets Entertainment: Lifelike Autonomous Agents, Pattie Maes, 1995, MIT Media Lab, Rm. E15-305, 20 Ames Street, Cambridge, MA 02139, [email protected]

e. Artificial Intelligence (Third Edition), Patrick Henry Winston, 1999, http://www.ascent.com/books

f. Redefining Robots, Smithsonian Magazine, February 2000 issue

g. Dymanic Memory, Roger Schank, 1982, Cambridge University Press, ISBN# 0-521-24858-2

Reference Citations from the Chapters

The following are the page references used for quotes or to paraphrase the references; they are numbered or lettered consecutively by chapter.

Note - The references are abbreviated as follows: 1=OPAR, 2=ITOE, 3=BBTC, 4=VCSR, 5=TNOL, 6=PE, 7=AC, 8=DLFSP, 9=TMC, 10=CI, and 11=AA, 12=ARL, OTSOTA. Differentiating references are used for general background only, and page numbers are not specified in all cases.


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References for Chapter 1

1. OPAR - Pages 30-36

2. OTSOTA - (Audio tape), OPAR - Pages 142-151




6. ITOE - Pages 58

7. OPAR - Pages 189-193


Note - To gain a good understanding of the issues discussed in Chapters 1 & 2, a very careful reading of the first six chapters of OPAR and of all of BBTC & VCSR is strongly recommended; these will be new ideas to most readers.

Differentiating References for Chapter 1

a. Intelligence as Adaptive Behavior - Pages 2-7


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1. OPAR - Pages 144-150


3. BBTC - Pages 63-64

4. BBTC - Page 63, OPAR - Page 191

5. BBTC - Page 50

6. BBTC - Page 22

7. OPAR - Pages 207-220



10. BBTC - Pages 119-120

11. VCSR - Pages 3-4

12. AC - (Audio tape)





17. ITOE - Pages 110-111, 242-243, 299


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1. BBTC - Pages 119-120

2. BBTC - Page 11

3. VCSR - Page 8, ITOE - Pages 17-18

4. ITOE - Pages 55-61

5. ITOE - Page 5

6. TNOL - Pages 452-458

7. BBTC - Page 66-67

8. DLF Program


1. ITOE - Page 55

2. PE - (Audio tape)

3. VCSR - Page 6

4. VCSR - Page 5

5. VCSR - Page 3

6. OPAR - Page 59


8. OPAR - Pages 132-139


10. ITOE - Pages 230-233

11. PE - (Audio tape)


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12. ITOE - Page 6

13. ITOE - Page 10

14. AC - (Audio tape)

15. VCSR - Page 8


17. ITOE - Page 13

18. ITOE - Pages 282-288


20. ITOE - Pages 256-257

21. ITOE - Page 10

22. ITOE - Page 17

23. OPAR - Page 16


25. ITOE - Page 17


27. ITOE - Page 159


29. ITOE - Pages 58

30. ITOE - Pages 59


32. OPAR - Page 118


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33. ITOE - Page 261

34. PE I - (Audio tape)

35. PE I- (Audio tape)

36. PE I - (Audio tape)

37. Popular Science Magazine, March 2001 - Page 31

38. Popular Science Magazine, March 2001 - Pages 54-55


1. PE II and TMC - (audio tapes)

2. PE II - (audio tape)




6. San Jose Mercury News (10/15/99, ID# 9910190294)


8. OPAR - Pages 111-121



11. CI - (audio tape)


13. TMC - (audio tape)


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14. OPAR - Pages 142-151

15. OPAR - Page 16

16. OPAR - Pages 189-193

17. BBTC - Page 63


19. BBTC - Page 147

20. PE I (audio tape)

21. VCSR - Page 22

22. International Standards Organization, http://webopedia.internet.com/TERM/O/OSI.html

23. BBTC - Page 63

24. BBTC - Pages 115-120

25. BBTC - Page 116

26. BBTC - Page 147


28. VCSR - Page 3


30. DLFSP - (computer program)


32. ARL - Page 92

33. San Jose Mercury News (10/15/99, ID# 9910190294)

34. TMC (audio tape)


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36. VCSR - Page 3

37. VCSR - Page 2

38. VCSR - Page 8

39. VCSR - Page 2 (Footnote)

40. AC - (audio tape)


42. VCSR Pages 5-8

43. ITOE - Page 13

44. PE II (audio tape)

45. CI (audio tape)

46. CI (audio tape)

47. DLFSP (computer program)

48. CI - (audio tape)

49. AA - (audio tape)

50. ITOE - Page 19, AA - (audio tape)

51. ITOE - Pages 256-258

52. ITOE - Pages 88-121

53. ARL - Pages 245-247, AA - (audio tape)

Differentiating References for Chapter 5

a. Artificial Life Meets Entertainment - (Web file)


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b. Intelligence as Adaptive Behavior - Pages 2-7

c. Discover Magazine - October 1999 issue Pgs 67-73

d. Dynamic Memory - Page 80

e. Dynamic Memory - Pages 95, 111

f. Redefining Robots, Smithsonian Magazine, February 2000 issue - Pages 97-112

Note - All of the references pertaining to Objectivism are available from one source at reasonable prices: Second Rennaisance Books, www.RationalMind.com, (US)800-729-6149, (Canada)888-729-6149, (international)001- 860-355-7160.


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Index

Index

Symbols (X,Y) coordinates 335 “Animats” 252 “billiard ball” 206 “billiard ball” causality 50, 77, 160, 229 “Consciousness is Identification.” 334 “Existence is Identity.” 333 “Identity -->> Action” sequence 294 “mind–body” dichotomy 230 “some but any” principle 396 “Volition” (free will) 158 “What if” Capacity 429 A abstract concepts 178, 194, 404 abstract form 386 Act 358 Action 88, 139 action 201 action capacities 165, 311 action capacity 169, 177, 188, 389, 420 Action Control 87 action drivers 140 action efficiency 176 Action in a DLF’s World 354 Action Method 357 Action method 139 Action Methods 411 action potential 178


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action pre–definition 450 action repertoire 371 Action Selection 358, 361 action selection method 345, 347 Actions 37 Actions and Objects 342 Active DLF/Current Status 138 Adenosine Triphosphate (ATP) 109, 338 agent 450 AI 241, 246, 249, 254, 272, 284, 309, 449 AI research 231 AI researchers 312 AI systems 255 AI/AL State of the Art 245 AL 28, 66, 249, 254, 272, 284, 309, 312, 449 Analytic–Synthetic Dichotomy 431 animate 364 animation platform 256 anti–survival 378 application program 259 application programs 272 arbitrary 430 arbitrary (as in non–objective) 248 arbitrary constructs 412 Artificial Intelligence 249 Artificial Intelligence (AI) 10, 215, 240 Artificial Life 252 artificial life 70 Artificial Life (AL) 10, 215, 240 Artificial life programs 76 artificial life–form 268 ATP 109, 299 ATP synthesis 269, 282 attribute measurements 431


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Index

Automatic action 271 automatic action 81 Automatic Action Selection 120 Automatic and Infallible 372 automatic biological action 257 automatic comparison processing 184 automatic goal–directed behavior 160 automatic perceptual consciousness 375 automatic self–regulation 373 automatic subconscious integration 198 Automatic Survival 366 automatic, teleological action selection strategies 381 automatons 270 axiomatic concept “existence” 176 Axiomatic concepts 197 axiomatic concepts 199, 200, 203 Axiomatic Concepts as cause of Self–Awareness 196 B billiard ball causality 312 biochemical mechanistic causality 281 biological life 269 Biological vs. Digital Life–Forms 259 bootstraps 382 C C.Event 107, 140, 179, 208, 358, 428 C.Event cycle 145, 358 C.Events 87, 207, 367, 377, 382, 383, 397, 411, 423, 425, 429, 437, 447, 451 C.Events per unit time 361 calculate a concept 412 calculate an identity 412 calculate the attributes of the objects 323 calculates their properties and values 326 Calculating object attributes 97 calculation chains linking words to reality 412


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calculation chains of concepts 258 calculation that the percepts 424 calculations of the attribute lists 393 category of measurement 323 causal and logical form 371 causal chain 273, 293, 376, 447 causal chain of calculations 408 causal context 269 causal context boundary 80 causal efficacy 363, 445 causal equivalence 342 causal loop 140 causal potential 205 causal process 230 causal sequencing 314 causality 201, 263 causality as identity–action 256 causality simulators 289 causality substitution 235, 278, 281, 282, 313, 364 causally equivalent 360 causally necessitated 378 causally substituted 261 Cause an Action local 123 Cause and effect 40 causes and effects 260 CCD 400, 406, 407, 418, 425, 440, 441 cellular processes 259 chain of causality 362 chains of calculated, measurement based relationships 420 Chains of calculations 439 choices as caused 159 closed system 412, 447 Cognitive Functions 84 cognitive self–regulation 173


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Index

commensurable attributes 393 complex causality 77, 220, 364 complex causality and consciousness as a non-mystical process 22 Complex Causality as an Emergent Property 70 complex causality of life–forms 296 Complex DLFs 314 components of reality 204 computer agent 218 Computer agents 270 computer databases 409 computer hardware 259, 364 Computer Network Analogy 278 computer programming 213 computer simulation system 389 computer simulations 240 computer system as an animation platform 152 Computer systems 400 Computer systems as action platforms 228 Computer Systems vs. Teleological Systems 263 Computer vs. Teleological Action Definition 270 Conceive method 144 concept 246 concept (as defined by Ayn Rand) 381 concept “existence” 418 concept “nothing” or “non–existence” 418 concept “self” 426 concept defined 383 concept formation 226, 246, 255, 262, 423 Concept Formation as a Calculation Process 389 concept formation methods 383 concept formation process 181, 415 Concepts 21, 416 concepts 84, 222, 223, 231, 277, 380, 453 concepts as a calculated datatype 258


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Concepts as a Data Type 386 concepts as a means of making the invisible visible 389 Concepts as abstractions based on observed, measurable differences and similarities 248 Concepts as an open–ended data structures 176 Concepts as calculated 384 concepts as calculated from reality upward 427 concepts of “place” and “world" 407 Concepts of Causality 189 Concepts of Consciousness 193 concepts, as epistemological structures 164 conceptual awareness 33 conceptual calculation chain 232 conceptual calculation chains 299 conceptual chains 223, 299, 454 Conceptual Common Denominator (CCD) 395 Conceptual consciousness 197, 389 conceptual consciousness 163, 207, 223, 263, 274 conceptual consciousness as an active and fallible process 177 conceptual consciousness simulator 411 conceptual hierarchy 401, 412, 427 conceptual identification 446 Conceptual knowledge 238 conceptual level of consciousness 182 conceptual memory 414 conceptual processes 384 conceptual recognition 428 Conceptual recognition methods 429 conceptual simulation system 413 conceptual symbols 226, 431 Conceptualize class 144 conceptualizing sentence objects 440 conditional 264, 266, 268, 293, 308, 312, 450 conditional nature of biological life 314


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Index

conditional object 294 conditional objects 284, 288 conditional processes 266 conditional, teleological relationship 311 Conditionality causes evolution 267 Conscious Event Cycle 358 conscious identification of reality 255 conscious mental processes 380 conscious processes 244 Consciousness 24, 30, 314

The “Movie” 145 consciousness 199, 201, 229, 269, 276, 365 Consciousness as a limited process 230 consciousness as a limited, quantify–able, causal process 244 consciousness as a relational process 40 Consciousness as a relationship 105 Consciousness as an action 193 consciousness as an active process 230 consciousness as causally connected to the world 365 consciousness as mystical or transparent 363 consciousness simulation 235 consciousness simulation system 408 consciousness simulator 269 consciousness transforms the identity 334 conserving the identity information 99 content of consciousness 188, 238, 364 content–oriented data compression 331, 451 Context Boundaries 69 creativity 412 criteria for action selection 296 cross–classifications 403, 414 current C.Event 111 cybernetics 313 cybernetics (negative feedback control systems) 312


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cycle of repeated perceptual events 322 D Data of Reality 31 DC 397, 404, 406, 407, 413, 418, 425, 440 decoding and encoding of natural language 452 Decoding Simple Sentences 442 Default Action Controller 121 define their own future identities 437 definitions 431 design architecture for life–form and consciousness simulation 455 desire attributes 370 desktop computer 284, 298 detector 180 determinism 381 differentiation and integration 389 Digital “Biology” 71 digital camera 335 Digital Life–form (DLF) Program 67 Digital life–form Simulation Program 274 Digital Life–forms 65 Digital life–forms 75 Distinguishing Characteristic(s) (DC) 396 disvalue 336 disvalues 287 DLF 300, 324, 384, 431 DLF Mind 102, 111, 153 DLF Mind instance 125 DLF Mind/Feeling Calc 116 DLF physical functions 109 DLF Program 91, 120, 145, 179 DLF program 173, 328, 391, 456 DLF simulation software 454 DLF simulation system 408, 457 DLF system design 450


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Index

DLF’s identity 436 DLF’s perception software 336 DLF’s world 452 DLFs 259, 456 DLFs’ actions 307 DNA 259, 298 DNA processes 269 E efficiency of awareness 174 electro–chemical and physical process 272 electro–chemical processes 269 Emergence of Conceptual Consciousness 154 Emergence of Natural Language 438 Emergence of Simulated Self–Consciousness 425 emergence of simulated self–consciousness 452 emergence of the conceptual level of consciousness 184 Emergence of Volition 374 Emergent Properties 54 Encoding Simple Sentences 443 Energy 38 Energy Packets 174, 182, 299 Energy Packets (EPs) 109, 338 Energy Transfer and Sensing 90 Energy Usage local 139 English 440 entity 201 entrance to the conceptual level 382 epistemological 12, 334 epistemology 431 EPs 139, 174, 299, 302, 306, 307, 338, 357 Evaluate 358 Evaluating Objects 336 Evaluation 87 evaluation system 337


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evolution 337, 348 Existence 38, 201, 238 existence 199, 424 Existence Window 91 Existence window 124, 140, 173, 179, 196 F figure–ground images 183 filing and organizing percepts 388 filing system 400, 403 Filing systems 386 Find and Watch methods 145 first cause 169, 205, 447, 448 First causes 84 first causes 206 first level concepts 177, 181, 195, 196 first level of consciousness 316 flow chart 295 flowchart 324, 356, 358, 369 form of causality 244 Form or Product of the Invention 455 Free will 158 Free will as caused 161 free–will 216, 231, 275, 381, 448 full volitional control 172, 201 functionally equivalent 261, 282 G Genetic algorithms 217 genetic algorithms 215, 219 genetically determined 348 Genetics 376 goal of survival 296 goal–action cycle 301 goal–causation 267 goal–directed 268


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Index

Goal–directed Action 55 goal–directed action 57 Goal–directed behavior 220, 293 goal–directed behavior 14, 75, 166, 218, 225, 283, 310, 375, 376, 450 goal–directed causality 289 Goal–directed causality as cyclical 87 goal–directed cause and effect 108 goal–directed processes 269 Goal–Directed Simulation Logic 291 goal–directedness 267, 269 grammar 204, 439, 441 grammatical concepts 447 grammatical specifications 453 H happiness value 346 hardware 272, 364, 376, 450 hierarchy and context 258 How Conceptual Consciousness Emerges 421 How DLFs Form Concepts 178 human consciousness 263 human teacher 407 I Idealism 19 Idealists 48 identities 315 identities of objects 318 identity 199, 201, 229, 244, 264, 324, 336 identity conserved 409 Identity determines action capacity 380 identity information 360 identity is conserved 335 identity links 389 identity of consciousness 206 identity of objects 362


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identity of reality 324 Identity Transfer 90 imagination 412, 436 imagine 434 imitation of biological consciousness 257 implicit 360 implicit concept 191 implicit concepts 201 implicit measurements 36 Implicit Strategy Comparator 123 Implicit Strategy Processor, cases 128 incommensurable identities 261 independent decision making 453 Information IS identity in conscious form 334 Ingest Class 144 instincts 83, 371 instinctual behaviors 371 integrating simulated sensations 102 Interacting with Memory 367 interface 260, 269, 275, 289 interface layer 290 Interfacing Computer Systems to Value Systems 288 internal locus of control 268 internally powered and controlled causation 267 intrinsic 389, 396 Intrinsic concepts 247 intrinsicism 22 introspect 198, 377 Introspect Class 144 introspectable 383 intuition 391 isolate and focus 191 J Java programming environment 68


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Index

Java™ 67 L language as a tool of concepts and thinking 206 Layer Substitution 68 layer substitution 278 layered model 75, 155 Layered Model of Complex Causality 156 Layered Models 67 layers of causes 269 level of awareness 364 levels of abstraction 176 life status 343 life–form simulator 237 Life–forms 270 Lifelike Autonomous Agents 253 Light Interaction 93 logical form 235 Look class 145 loop 327, 357, 358, 361 M man–made 238 Man–made Objects 65 manual (non–automatic) behavior 374 Materialism 19 materialism 49 Materialistic idea 77 mathematical connection 409 measurement range 180, 181, 334, 391, 395, 397, 418, 434, 441 measurement value 323 measurement values 391 mechanistic automaton 297 mechanistic autonomous agents 303 mechanistic basis 269 mechanistic causal processes 294


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Mechanistic causality 286 mechanistic causality 221, 240, 256, 264, 266, 269, 276, 289, 300, 313, 364 mechanistic causation 284 mechanistic cause and effect 106 mechanistic computer causality 281 mechanistic layers 262 mechanistic processes 269 mechanistic programming 312 mechanistic simulation 13 mechanistic systems 260 memories 350 Memory 87, 135, 358 Memory method 135 memory record 351 metaphysical 12, 238, 264, 430 metaphysical primary 276 methods of a C.Event 88 mind–body dichotomy 314 mysticism 49 N narrowing the measurement range 404 natural human languages 176 Natural Language 202 Natural language 202, 224, 445 natural language 186, 204, 206, 226, 233, 258, 277, 299, 388, 409, 428, 447, 452 natural language word 412 natural language words 454 negative–feedback, stasis–seeking process 377 neural networks 219 neuro–chemical energy 99 neuro–physiological processes 316, 380 neutral (non–essential for survival) 378 neutral behaviors 372 No-Act method 145


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Index

Non-existence 39 non–existence 424 Non–living and living 37 non–living objects 371 O object 327 objective 396 Objective concepts 248 Objective conceptual knowledge 239 objective method 249, 258 objective, conceptual knowledge 403 Objectivism 17, 246 Objectivist Epistemology 246 Objectivist epistemology 21 Objectivist ideas 255 Objectivist method 258 Objectivist theory 385 object–oriented computer programming environment 299 Object-Oriented Prog. Environment 274 object–oriented programmer 415 object–oriented programming 235, 341 object–oriented programming environmen 282 object–oriented programming environment 213, 284, 403 object–oriented programming inheritance 427 Objects 35 objects’ identities as properties and values 105 observation of objects 184 ontogenetic 348, 373, 376 Ontogenetic behaviors 353 open–ended categories 231 open–ended quality of concepts 176 operating system 259, 272 optional action 271, 345 optional action capacity 384


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Optional actions 378 optional actions 346, 378, 437 Optional actions are actions 272 optional behavior 145, 208, 275, 277, 381, 415, 425, 430, 448, 451 optional behaviors 298, 343, 383, 445 Optional Mental Actions 159 optional physical action 169 optional, goal–directed actions 258 ordinary computer files 454 oriented programming environment 225 P parse 440 patent application 456 Perceive 358 Perceive objects 104 percept data type 453 Percept formation event 103 percept method 100 Perception 87, 101 perception 262, 360 percepts 223, 299 Percepts as a list 326 percepts as time dependent 177 perceptual comparison 389 Perceptual Consciousness 315 Perceptual consciousness 168, 316, 376 perceptual consciousness 198, 229, 274, 327, 389 perceptual consciousness as a passive, automatic, and infallible process 177 perceptual identities 316 perceptual information 362 perceptual system 36 Philosophical Context 16 Philosophy, Biology, and Consciousness 46 physical 430


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Index

Physical laws and “digital” laws 165 physical molecular processes 269 pixels 336, 353, 400 Pleasure & Pain 87 Pleasure/Pain method 110 pleasure/pain system 378, 379 pleasure/pain systems 337 plenum 18 positive, value–seeking process 377 preconceptual 316 prerequisites 15 Primacy of Consciousness, fallacy of 48 processing units 321, 327, 390 program code 364 program conditionals 346 programming its own identity 189 Prograph™ 67 properties 323 properties and measurement values 327, 336, 410 properties and values 333 property and value list 341 pro–survival 378 protein 269 proto–language 428 prototype DLF 455 psychology 337 Purposeful Action 367 Purposeful action 58 R range of measurements 323 range of values 179 range of values unique to a group 394 rational self–consciousness 269, 274, 445, 451 Reality 18, 29


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reality 324 reality (object) manipulation 429 reality is transformed 324 reality simulator 11 reality simulators 234 reality–based concepts 258 recapitulate evolution 218 Recognition 367 recursive changes to identity 385 recursively modify conscious behavior 389 reduce the units of information 175 Reduction to Practice 455 relational concept 39, 407, 429 relationship to reality 364 Relationships 40 relationships as calculated ranges of measurements 406 repetoire of automatic actions 133 reproduction 353 reversal of the process of simulated consciousness 355 reverse engineer 218 RNA 269 Robotic vs. Goal–Directed Causality 268 robotics researchers 312 Robots 254, 453 robots 400 S Say class 141 Say methods 142 scientific method 16 selected by choice 374 self 201 self as a virtual entity 199 self–awareness 198, 206 self–caused 228


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Index

self–conscious 86 Self–Consciousness 194 Self–consciousness 199 self–consciousness 201, 223, 224, 342 self–defining 236, 374, 383 self–defining system 216 self–evident axiom 86 self-evident facts 199 self–generate 306 self–generated 256, 286, 307 self–generated, self–sustaining action 50 self–generating 312 self–generation 267 self–goal–directed object 266 Self–Powered Objects 50 self–programming 388, 437 self–regulating 228, 374 self–regulation 222, 451 self–sustaining 286, 312 Sensation 99 sensations 229, 276, 315 sensing event 101 sensory input 198 sensory modalities 323 sentence as a complete thought 446 sentence object 441 sentence objects 440 shared properties 179 Short Term Memory (STM) 125, 136 similar objects as units 186 similarities and differences between objects 183 similarities in measurements 394 simulate consciousness 217, 234, 275, 315, 358 simulated act of will 85


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simulated concept 390 Simulated concepts 410 Simulated concepts as timelessness 416 simulated consciousness 326, 446 simulated desires 147 simulated effort 182 simulated environment 318 simulated feeling 339 simulated feelings 361, 407 simulated happiness 114, 347 simulated hunger 111 simulated life 269 simulated life–form 217 simulated life–forms 236 Simulated Natural Language 438 simulated natural language 429 simulated objects’ identities 95 simulated perception 436 Simulated percepts 410 simulated percepts 343 simulated perceptual consciousness 333, 439 simulated reality 89 simulated sensations 330 simulated sensors 99 simulated values 337 simulated volition 213, 398 simulated world 89 simulating consciousness 152 Simulating Evaluation (Feelings) 107 Simulating hunger 112 Simulating Life and Death 76 Simulating Perception 91 Simulating Sensation 89 simulation of consciousness 272


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Index

Simulations 12 software 364, 376, 450 software agents 453 spacial relationships 405 Spiral Theory of Learning 207 standard of value 340 State of the Art Concepts vs. Objective Concepts 245 sub–classifications 403, 414 subconscious 183, 186, 208, 230 subconsciously 316 subjective 396 Subjective concepts 247 subjectivism 22 subject–verb–object 440 Subject–Verb–Object (SVO) encoding 204 subsystem layer 357, 382, 454 subsystem layers 260, 273 survival advantage 377, 413 survival advantages of concepts 388 survival behaviors 372 survival strategies 361 survival strategy 258 survival tools 262 survival value 32, 174, 182, 183, 268 Survival Value of Concepts 173 symbol manipulation 429 symbolic form 169 symbolic representation of reality 202 symbolic representations 315 Symbols 164 symbols 204 T teleological 263, 283, 333, 343, 347, 353 teleological actions 218


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teleological agent 288, 457 teleological agents 271, 303 teleological behaviors 365 teleological causal process 294 teleological causality 256, 269, 285, 373 teleological design 303 teleological entity 264 teleological layer 262 teleological process 260 teleological processes 262, 445 teleological program 275 teleological simulation 14 teleological simulation system 309 teleological software 311 teleological systems 265, 288 teleologically necessitated 378 teleologically optional 378 teleology 255, 365 tests for a life–form 79 thermostat 313 thought 428 three ways of forming concepts 248 timelessness of concepts 198 trace the calculation chains 434 transduced 322 transduction of the energy 99 transform by calculation 408 transformations 321 Transition to Volitional Consciousness 147 two forms of causality 256 U ultimate genera 196, 410 unbroken chain of the identity information 362 unconscious machines 438


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Index

Unintelligent Robots 254 unit economy 223, 320, 331, 353, 388, 400, 413, 451 unit processing economy 436 units 179, 317 units to process 174 V validity of sense perception 238 valuable actions 311 value 336 value as in number 338 value/disvalue pair 338 values 287 value–significance 256, 267, 268, 288, 290, 292, 293, 307, 336, 366 vegetative functions 293 virtual entities 75 virtual form 233, 314 virtual life–forms 292 virtual mind 314 virtual object 291, 426 virtual reality 234 virtual world 90 Volition 194 volition 217, 269, 277, 381, 446, 451 Volition (free–will) 222 Volition and Concepts 63 Volition as an action capacity 380 volition as caused by previous instances of its own use 172 Volitional (free will) action 60 volitional behavior 147, 275 volitional control 86, 188, 196 volitionally self–defining 375 W word 395 word as a symbol 397


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Words 431 words connect via their chains of calculations 428 World as Objects 91 X X,Y 174 X,Y coordinate lists 397 X,Y coordinate pairs 330, 390, 408 X,Y coordinate systems 431 X,Y coordinates 321, 329, 333, 353, 400, 410, 453

How to Simulate Consciousness Using a Computer System

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