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Artificial Intelligence and the Concept of AI Moral Field Based Control

Interpreted Via a Variation on the Principle of Least Action

Del J. Ventruella (BSEE, MSEE)

Abstract: The application of neural network based artificial intelligence (AI) often seems to most closely approximate the foundations of human intelligence comprised of what psychologists and experts in the neurology of the brain have begun to surmise is linked to forms of object recognition and relationship building. This object recognition is based upon object properties stored in brain regions associated with acquisition of the object property related data1 and related combinations of identifying factors such as color, consistency, shape, and whether the object will bounce or is likely to be heavy firing neurons in various parts of the brain at one time to identify what we, as humans, perceive as an object, such as a cup of tea or coffee, another human being, a rubber ball, or a laptop computer. How much of what we perceive as the possibilities before us at each moment is based upon some input triggering recognition of object types (including meals) that we might seek out, then generating plans to do, is another matter. Whether AI will be designed to reproduce these distributed memories of different, object identifying factors, linked in different ways, as neural pathways firing based upon a collection of perceptions that lead to a single conclusion forced by a group of active neurons, or transistors in the case of AI, or simply stored under a single object structure in a computer’s memory, is another matter. Although interesting, how AI might identify an object using a collection of consistent, physical, object traits is not the basis for this discussion. Assigning non-intrinsic “value” (potentially also an object trait but one with no physical

expression readily accessible to our senses) to such objects and to actions directed at those objects so as to guide behavior of AI driven mechanisms to fit into a “moral” field produced by human perceptions within a human society is our primary focus here. It is likely that during childhood and adolescence morally based identifying factors are also assigned to objects and behaviors granting them some form of intrinsic value in our minds that is associated with each object or action that might be directed at it, with ethical concepts that are linked to this moral valuation attached by a social system of reward and punishment that cannot be lost upon any AI system hoping to fit into a human society while maintaining some level of autonomous function.3 Such a system of valuation may serve to identify how much risk we are willing to take relative to actions directed toward different objects. It can also help to provide insight relative to the manner in which we design AI (artificial intelligence) systems that interact with the real world, presumably using remote, physical forms that could be quite powerful (or intricately detailed, small, low powered, and entirely compatible with humans), without requiring inordinately long periods of human interaction related to teaching. The more specialized the AI system, or the code that is written to carry out a task, the easier it may be to build in a certain level of “right thinking” relative to the system’s behavior. For example, a pacemaker has a single purpose, and if an irregular heart rhythm is detected, it is programmed to deliver an electric shock to restore a normal heart rhythm. The pacemaker did not consider the moral issues related to its

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implantation or to whom such medical equipment is made available in the context of wealth. It simply responds in a certain manner once it is implanted. This creates some grounds to consider robotic morals at two levels. An initial, broad context moral response in a very general, weighted sense, followed potentially by a more narrow, task oriented context. This perspective is described later in considering how so-called “nonsense” combinations of command and target of command words might be treated by an AI system under a broader moral waiting system, followed by a well written task application that “weeds out” the “nonsense” commands (e.g. “turn-off ball”). The concept presented here focused on a broad, initial, moral response to a two word command set by an AI and involves the application of the mathematical idea of a “principle of least action” as a tool by which to minimize the energy required to negotiate a path through a field (just as one might follow a flat path at a constant speed on a bicycle to reach one’s destination, rather than consistently pedaling slowly up hills and braking all the way down the downward slope). The intent is to produce a means of negotiating the true path than an object would follow through something like a gravitational field, but in this case, a field of what might be termed “normal human expectations” relative to proper, moral conduct, as a “moral field” (which is purely mathematical and virtual, based upon comparison to a moral tolerance level, or Moral Test Level programmed into the AI system). The technique assigns numerical values to each noun and verb in a two word command syntax to ascertain the local value of the “moral field” as the product of the value assigned to the command word and the value assigned to the target word. The higher valued the noun in terms of how it influences this virtual, moral “field”, the less desirable is interaction between the AI system

and that noun as the target of a command given to an AI system. The high value of the noun intrinsically causes it to seek to amplify the “energy” of a moral field wherever an AI seeks to act upon it, contrary to the principle of “least action” within that field. The target that has such an adverse impact on our “least action” goal might be a valuable artifact, a human, or a user, in a direct sense. A high valued user is not an “object” toward which the AI system considered in this discussion, potentially controlling a powerful, mobile system, is to direct any physical action. This is based upon the assumption that the AI system classification is something on the order of “general industrial”, with sufficient power to injure or kill a human being, and tasks largely focused upon maintenance of an industrial facility or heavy commercial equipment maintaining a yard, driveway, garden, or street. This helps to safeguard the user where construction equipment or dangerous vehicles might be controlled by an AI presence. Where the verb comprising a “command” is high valued, the potential for damage to be induced is high. For example, “crush” would be higher valued as a “command” than “take picture of” due to the substantially greater risk of damage or injury in the context of “crush” “radio” than “take picture of” “radio” in the context of two word syntax commands considered here. Using this technique acceptable behavior is defined by the application of a pseudo-minimization of the “moral field potential” of actions that an AI could undertake via the linked, neural networks that combine actions with objects toward which such actions can be directed within the AI’s associative control system through a virtual (mathematical) “moral field” created by assigning numerical values to single nouns and verbs within a command syntax for AI systems and defining the virtual field “energy” of the combined command and target noun to be the product of the values assigned to each. One negotiates a path

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through any given time interval by limiting the magnitude of the product of the command and target nouns accepted by the AI to a level that assures that the moral impact of following any command and directing it at a specific target in a two word command syntax, where the product of the values assigned to both words is acceptable, approximates, to the first order, a minimal variation from a socially acceptable level of violence or injury within this “moral field” interpretation of socially acceptable behavior. For example “move” and “radio” could be combined to produce a response by an AI, assuming that an AI would be able to avoid crushing the radio in the process., but “throw” “radio”, given its inherently more violent nature and more dangerous potential would not be followed because it could produce an undesirable “high” moral field potential path as perceived by “normal humans”. Such a concept could be standardized in many applications for specific applications or general use (e.g., “murder” (or “kill”) and “human” as a command and target pair would likely be universally declined by any AI, save perhaps for those responsible for executions in prisons as a highly specialized exception or in the case of military robots if laws were not globally passed to prevent it except where a group is using AI systems in violation of such laws). Low valued products of nouns and verbs in the command syntax (based upon the artificial choice here to make “high moral field potential values” correlate with “high cost” or “high value” to bypass social punishments under laws that might require replacement of such objects by manufacturers if their AI systems were to damage them) designate more acceptable behaviors, and no behavior is linked with an object toward which it can be directed in the AI system’s moral control structure unless it falls below a selected minimum to insure that it does not pose a hazard and is a “moral choice” within the concept of this “moral field”.

The commands, and the neural network connections that should be imposed upon an AI system to link object recognition with awareness of possible actions that could be taken toward that object, thus correspond to the command set product of the values associated with “command” words and “target” words below a critical threshold (“Moral Test Level”), above which injury to humans or some similar, intolerable (including “frightening”) outcome, is deemed likely. This threshold defines the limit of the “least action” within the “moral field”. One could even alter the value of the target word based upon the proximity of a low valued object/target to a high valued object, such as a human being, if the AI system might injure or shock a human by undertaking what might be perceived as an acceptable act if no human were present, depending on the accuracy of the AI’s system’s capacity to control its own manipulators. This would simply require the capacity to recognize a human (including falling, floating, prone, or rotating humans) and estimate proximity to a target object. Because the assignment of numerical values relative to the moral field for “commands” and “targets” can be conceptualized and generalized, it is possible to develop an algorithm by which the acceptable moral and social standards can be re-produced within a limited command vocabulary using a computer system, and without human teaching (simply programming of individual command word values based upon general classifications), lending a “self-evolving” element to this aspect (with “programming” taken to be different from “teaching”) of the control system guided by human insight and conceptualization of moral and safe programming requirements for a given system and environment. This presumes that AI would eventually be classified for specific environments and purposes, subjected to engineering and design standards, with laws controlling specific ownership and where they could be located.

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Key-Words: AI, Artificial Intelligence, Virtual, Moral, Field, Principle, Least, Action, Two, Word, Command, Target, Syntax, Self-Evolving. Introduction In 18th century America, the values imposed upon slaves included an attempt to diminish exposure of the owner or his dinner guests to the sight of the slaves.7 Thomas Jefferson, one of America’s “founding fathers”, went to great lengths to insure this at his home in Monticello. The “moral field” that then had to be negotiated by most slaves, presented as 18th century servants lacking status as full human beings, thus strongly discouraged direct interaction between the owner and the slaves, who maintained the owner’s household and produced his crops. Contact with owners might be interpreted in the language of this discussion to represent events that could be categorized as unlikely, demanding great amounts of energy within an 18th century, southern “moral field” if the opposing force was to be overcome to make them common. Today some prefer to look forward to technological slaves in the form of robots or artificial intelligence. With the rise of computer networks and the internet, some of the forward looking thinkers of the past may seem to be a little out of date when we consider the moral relevance of a robot’s inclination to destroy itself or save its owner, a common plot in early 20th century robotic fiction, given that a robot in an environment of networks linked by radio signals may simply serve as a cheap appliance in use by a much more valuable, highly complex, and remotely located artificial intelligence capable of controlling a variety of remote equipment. The fact that such robotic matters as moral conduct have been considered3,4,5,6,9,10 do establish that the question of how humans and human society will interact with AI from a moral standpoint isn’t new. The sort of moral weighting of actions and objects individually

and in combinations that may most simply describe the basis for human behavior could provide a crude means of addressing related issues within the context of a virtual, “moral field” shaped by human expectations. “Moral Field” Based Programming Derived from a Least Effect Based Model for Minimizing Field Potential (Greater Potential for Injury or Loss Equals Higher Field Potential.) First, AI controlled systems, if envisioned as some form of robot, could be large, powerful, and, in its initial form, not necessarily well adapted to life around human beings. Industrial AI systems are even less likely to fit the romantic vision of the stars of feature films or musicals who dance, light footed, about any environment. This paper considers AI systems to likely be either too fast or too slow, too strong or too weak, too heavy, or simply too awkward and limited in their capacity for perception to be trusted to undertake tasks that might endanger humans or their valuable possessions unless humans have taken measures to insure the safety of themselves and their property. That includes measures related to moral programming. With “least action” and “least (possible) injurious effect to humans” as the grounds for the most morally acceptable behavior within this conceptualization of this version of a “moral field” designed to produce a most likely path solution through that “field”, which, is, in fact, a field of possible decisions relative to a two word command syntax considered here (command and target of command), with most of those possibilities destined to remain virtual (“nonsense”) elements of the field, the “lowest potential” path through day-to-day activities would be very unlikely to include any event demanding a great amount of energy in proximity to a human being due to the risk of physical injury or of terrifying the human via the sudden exertion of great force by an AI system nearby (“throw” “radio”) and potential for loss. A simple, illustrative model can be constructed

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for AI systems using a two word syntax combination consisting of a verb (or command action word) and noun (a “target” or thing upon which the command action is to be enacted). How to Assign Values to Words that Define the Moral Field Value They Would Create Via the Product of A Two-Word Command Structure We rationalize that any action that may be undesirable under a large variety of circumstances and depending on the target and venue, such as an order to “crush”, or any object that may suffer serious harm if it interacts with a powerful, AI piloted system, such as a “human” or “user”, should be artificially assigned proportionally higher numerical values (which help to link them to higher energy points in the moral field) than objects and activities with which we might wish an AI to casually interact. The result, depending on whether or how we choose to produce connections between recognizable objects and known actions to generate an array of possible actions as a “moral field” with specific energy levels assigned to that virtual moral field from which an AI must choose its actions based on the principle of “least action” (or “lowest energy” choice based upon a prescribed, acceptable “energy” or “effect of action” maximum limit, or “MTL”), will ultimately control whether the AI can even consider intentionally undertaking a potentially deadly act, such as following instructions to “crush” “user”. Teaching an AI that it will be punished in a progressively more severe manner based upon the level and extent of harm that it does is unlikely to be possible in the same manner that it is with human children and adolescents capable of experiencing both physical and emotional pain, with whom no assurances exist that desirable thresholds will not be crossed even if such “teaching” occurs. Children begin life as infants. As such, they are much weaker than adults. Ideally, parents are thus given the advantage of teaching the children not to cause

harm before the child reaches an age at which it has sufficient strength to render such teaching hazardous to the parent if it should produce a violent response. An AI driven mechanism meant to interact domestically with humans, unless specifically built in a diminished manner to permit teaching on behalf of less flimsy AI driven mechanisms to follow through construction based upon principles of designing the learning device with low energy and mass and no capacity to injure a human teacher, perhaps even as a virtual device, could prove quite deadly to a human teacher in the course of the normal process of learning and making what could be life threatening mistakes from the perspective of the teacher. (The virtual machine option might also facilitate training humans to work in environments in which AI controlled devices are present.) This concept of “least potential”, with potential defined as the product of the values assigned to a command word (a verb) and a target word (a noun) provides an avenue around the requirement for teaching neural networks moral principles with potentially deadly effect and a need for a great deal of time by simply controlling the possible actions that an AI could contemplate within an associative, cognitive network using a “virtual moral field” based mathematical routine predicated upon perceived potential for effect resulting from an AI following “command action” words (verbs) and “action target” words (nouns) and manifesting those commands through some mechanism under its control. Nonsense Connections and Self-Evolving AI To truly avoid any element of error we need to consider the possibility that nonsense syntactical connections may be dictated by an automatic associative network produced via some form of “truth table”. Such a nonsense connection would interconnect a “command action” word with an “action target” word in a

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manner that would make to sense. We might have the words “radio” and “ball” in our list of “action target” words, and “turn-off” as one of the words in our “command action” list. We might try to deal with the resulting non-sense command possibility (“turn-off ball”) by asserting that the description of a given “action target” word would be required to indicate, via standardized classification, whether it was a human, a machine, or merely a “thing”, with the first and last described as things with no capacity to be turned on or off. This might be used to give the “command action” word two values in the “moral field” potential value assignments that determine if “turn-off” is linked to an “action target” word based upon valuations that are below our selected, “hazardous” (“MTL”) threshold. If the “action target” word is a human or a “thing”, the value of “turn-off” might be dramatically increased, compared to the value assigned to “turn-off” when dealing with an “action target” word that is classified as a “machine”. For a “radio” as “action target”, the valuation of “turn-off” might be unity in the example. For anything that is not a machine, the valuation of “turn-off”, as it affects our induced moral field potential, could be much higher. Is this the only means of preventing the street slang meaning of “off-ing” someone from ever being implemented by AI? Of course not. A standard could be developed and implemented, under penalty of law, that would force AI to “turn-off” machines only by means that employ transmitting a remote signal using infrared or other harmless means of communications. One could imagine machines working under AI control being required to periodically transmit their serial number, common reference name used by humans, and the code required to shut them down, which all other controlling AI would be required to store and be prepared to use should a human abruptly appear and order AI to

“turn-off” a specific machine or, or, perhaps with broader effect, “site” “shut-down”. Such an approach would guarantee that no AI directed device would ever attempt to make physical contact with a human with the intent to shut the human off, and with the risk of physical harm or imposition of death while attempting to do so, under penalty of law (although presumably no AI code would ever be written for domestic AI that could fulfill a command directed at a human by a machine with the size and strength to kill). Whether this would apply solely to industrial or domestic machines under related standards, and not to military AI, is another matter, but presumably there would be no grounds to alter the manner in which one AI might shut down mechanisms driven by itself or others. It is still important to realize that what has been offered here does not prevent the “least potential” technique that has been described from creating a link between “turn-off” and “ball” (unless prevented by a specialized routine written to avoid nonsense connections, which would require greater human involvement in the development of the AI “command action” and “target word” associations). What has been suggested might raise the value of “turn-off” when used with “ball” to a level that renders such a command sequence certain to produce a numerical product greater than the programmed “MTL” of a AI system if “turn-off” is used in combination with anything not classified as a “machine”, preventing “turn-off ball” from being passed on to an action routine search to be executed. Presumably, the order suggesting that the AI could “turn off” a “ball” would never be used by rational humans, and if an AI attempted to use it, the subroutine that ordered it to identify the shut-down code to broadcast a signal to “turn-off” the ball would fail, because the ball would not be broadcasting any code by which to control it, resulting in recognition by the AI that it did not have the means to “turn-off” the ball.

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Given that such realization would likely only require an instant, the AI might select its next most likely option among the actions presented as being possible or required by its associative matrix, with little risk that the nonsense connection would hinder the function of the AI significantly or over a problematic time interval, with the AI most reasonably simply informing the human who had issued the command that the ball is not broadcasting a “shut-down” code, and can not be shut down by the AI. (Even in the absence of an obstacle to injuring humans built into a moral control algorithm via the high value weighting of humans as “targets” of AI commands where an AI could injure a human with the type of mechanism being ordered to target a human, the simple fact that humans would not broadcast shut-down codes could intervene as a secondary factor preventing AI violence directed against humans in the context of a nonsensical or malevolent command.) Self-Evolving and Self-Defining Algorithms At some level humans must become involved in programming intelligent computer systems capable of being responsible for their own actions (at risk legal action directed at the manufacturer) and interacting within a human society. Many years, or decades, are necessary to produce this capability among human beings before they are qualified as adults capable of being responsible for their own actions. Even then the reality of prison populations and corruption at high levels leads one to question whether AI “perfection” could be achieved via teaching. Producing an AI with such an independent learning capability would thus represent a major investment of time. Reproducing such an AI would be less time consuming, but moving it to “the next level” might represent a similarly time consuming struggle. Designing AI algorithms that can be adapted to each evolutionary step and providing for elements of evolution to facilitate the “next level” of

advancement, even with relatively complex systems, is clearly desirable. The need for humans to have some input into these algorithms is clear, because the AI must, at some level, interact with and serve the interests of humans. The AI must not pose a threat. It must not behave irrationally or develop goals that are inconsistent with its assignment. Such requirements move AI cognitive systems into the realm of seeking to reproduce human level awareness, intellect, and behavior, and perhaps to begin to move beyond limitations imposed by the nature of human systems of learning, interaction, and dominance. Independently Evolved AI It would, of course, be far more interesting to permit AI to develop as life evolved, entirely independent of human guidance and the natural imprint of the environment in which humans evolved. Because life on earth evolved as the result of a complex organic chemistry in which strings of RNA engaged in processes that freed energy, empowered self-reproduction of protein strands, and eventually produced a protective barrier around the RNA that we know as a cell wall, then evolved into multi-cellular organisms that sought to exploit local resources maximally in self-reproduction, until, by the time complex animals with nervous systems arose, cognitive behavior developed in response to the many options present in their environment that we now perceive as intelligent and self-guided. We can’t presume that in the absence of a suitable environment, and without a chemical template, the laws of nature as they exist for our world, and the environment of a specific, physical space to serve as the guide, and with its own limitations, to control the direction of evolution, we could ever naturally evolve a separate, artificial intelligence, without at some level leaving the imprint of our own evolution, even with neural network based systems, which

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intrinsically seek to reproduce how humans “are wired” to learn. The Cognitive Associative Network In the United States, at lunch time, tens of millions of Americans are faced with various inputs, a sense of hunger, a desire for rest from the morning’s work, perhaps a compulsion to socialize or assert some level of control over their own lives outside of the dominance chain of a corporate structure, even if only at the level of gathering in an environment with other people for half an hour without the boss looking over their shoulders. These inputs, for many, trigger a desire to seek out the nearest, fast food hamburger joint. This associative network, connecting lunch time with fast food, and, likely, one or more specific restaurants near their place of work, is fundamental to controlling behavior. It thus seems reasonable to assert that much of human behavior is controlled by associative networks. What remains are simply algorithms that are employed to satisfy the apex goal of the associative network, the primary, triggering factor, which, at lunch time, may be perhaps only the desire for a double hamburger with French fries and a soft drink. The supporting algorithms that permit us to get the hamburger tell us how to drive a car, or how to walk a block, how to behave inside the fast food restaurant, how to order, and how to eat and interact in a manner that will not cause us to be driven out or mocked. What controls the apex goal that lights up the cognitive network in this example is something external, such as hunger, or knowledge of the time of day. The rest may be little more than us responding to a compelling factor via a prioritized hierarchy that, for each moment, controls our behavior under the over-arching primary drive, in the example given, to sit down and enjoy a hamburger at lunchtime. This is why a means

of producing connections to generate a Cognitive Associative Network that can reasonably create such an awareness hierarchy, and that can then be triggered by external inputs, including orders from the boss, or “user”, or inputs received after initiating such orders, either from a human source, or from the environment (or an internal sensor within a machine, perhaps signaling a serious breakdown or system failure) is of interest here, because what triggers connections controls what will become associated in any context related to linked command and target words. A basic building block of that network is a sort of “Go/No Go” judgment that suggests whether basic goals expressed as commands should be followed. This is basis for the “moral field” and the computation of the “energy levels” of effects within the moral field if certain commands are followed to determine if the “moral path” described by the commands that an AI would follow exceed the threshold of what could be called a “least potential” path through the field, as dictated by a limit that we assign to determine how “moral” our AI’s conduct will be. Figure 1.0 and Figure 2.0 present come crude concepts regarding how an AI might process information. Figure 1.0 is intended largely to point out that nonsense combinations of commands and targets won’t be followed, because there will be either a want of information required by the code that would follow out the command if it made sense (no shut down code in our earlier example) or there will simply be no code written to receive a specific target word type. For example, one could write a command to “crush” and include anything classified as “things”, such as scrap metal, but make it impossible for the code that controls a crushing device to operate if it perceives a human among the scrap metal, because the code will not accept a human as a target of the word “crush”. Figure 1.0 and Figure 2.0 illustrate a possible AI functional hierarchy.

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SENSORY INPUT - Receive

data regarding external

environment and transmit

patterns to sensory ID

neural networks.

COGNITIVE ALGORITHMS – ID –

Receive output from sensory ID

neural networks and react to

changes using an “interrupt” style

response depending on level of

threat or desirability of

opportunity.

INPUT DATA STREAM (REAL OR

SIMULATED IN WHOLE OR PART)

FROM ENVIRONMENT

COGNITIVE ALGORITHMS –

OPPORTUNITY IDENTIFICATION –

Receive Cognitive ID

environmental data and recognize

opportunities inherent in local

environment. This includes

opportunities requiring additional

materials or actions. An

associative neural network would

be required for this. (If threat

arises, stop all other actions and

respond to avoid threat, i.e.

produce processing “interrupt”.)

MEMORY INPUT – Stores data

regarding what can possibly be

encountered (or created by AI,

including activities, such as

searching) in environment and

supplies to COGNITIVE PLANNING

ALGORITHM. This should include

a designation for anything that AI

is to weight favorably in terms of

the tasks assigned to it as

something it “likes to do”.

COGNITIVE ACTIVITY SELECTION ALGORITHM –

Prioritizes activities and selects “current activity” for

AI.

FIGURE 1.0 - AI

CONCEPTUALIZATION – Overall

Algorithm and Major

Components by Logical Task

A

B

C

D E

Output stream to

call existing

specialist routine

to perform

activity or to

create neural

network to learn

activity based

upon descriptive

data for activity

generated by AI

F

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Output stream to call existing specialist

routine to perform activity or to create

neural network to learn activity based

upon descriptive data for activity

generated by AI

F

Initialize

Existing Expert

Routine and

Hand Over

Task

G

Create Neural Network that Will

Become Expert Routine for Previously

Un-Encountered Task.

H I

When Task

Ends, End

Assigned,

Expert Routine.

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A “Moral Field” Example

The list of words for this first example will focus

on a beach. Eleven nouns will be used: “Radio,

towel, hamburger, ball, umbrella, sun, water,

shade, user, fire, ice-cream-cone”. Twelve

verbs will be used: “Get, carry, inflate, block,

create, follow, cook, turn-on, turn-off, Vol-up,

Vol-down, put-away”.

(It is clear that one may create specialized

words from small groups of English words in the

absence of standardization. An AI would

presumably only be responding to the

combinations of sounds and the order in which

they occur in a given “word”, so it would be

possible to create verbs like “Vol-up” or “Vol-

down” with individual meanings.)

If one combines a command verb and a target

noun into a command, without preference or

intelligence, the following combinations are

possible: “turn-on sun”, “carry fire”, “cook

user”, “Vol-up ice-cream-cone”. None of these

FIGURE 2.0 - “COGNITIVE

ALGORITHMS – OPPORTUNITY

IDENTIFICATION” – HOW TO CREATE

AN AI ASSOCIATIVE MATRIX USING

SYMBOLS, AND EXPLICITLY HERE,

WORDS, VIA COMPUTATION OF

NUMERICAL PSEUDO-POTENTIALS

FOR ADVERSE OUTCOMES.

Create a list of nouns (“targets of

command words”) relevant to a

particular task or locale. (Use

NO articles of speech, as with

Latin).

Create a list of command verbs

(“command words”) relevant to

the particular task or locale

associated with the specific list

of nouns.

Assign a numerical value that is higher where a noun (“target of command word”) is not to

interact with AI as a target of its commands or where a verb (“command word”) may

incorporate some potential for a violent act if undertaken against an inappropriate noun.

Compute product of numerical values of two word, noun and verb, command syntax used here.

Higher product values suggest higher moral field potentials, which are locations, or paths

through daily activities, that should be avoided, per the “least action” (or likely adverse effect of

action) technique.

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commands makes particularly good (or useful)

sense.

There are other possibilities that are simply

undesirable combinations. (Imagine what

might happen if your personal AI were remotely

piloting a robot made of plastic (and rented for

the day at the beach), and the command, “carry

fire”, although not nonsensical, were obeyed!)

The two lists of words (target nouns and

command verbs) must be selectively combined,

but if the lists were particularly long, it could

prove very time consuming to type in only

combinations that could reasonably make

sense. Creating a meaningful AI cognitive and

associative matrix thus appears to be a

daunting task (perhaps second only to writing

the code necessary to carry out a complex

command in a specific setting).

The creation of the cognitive and associative

matrix to make moral decisions need not

include the coding necessary to complete each

task with all possible objects (a reasonable

means of overcoming undesired command

effects). In fact, that is, per our last example,

undesirable. It would be a straightforward

matter to produce an AI that could consider any

verb (within its lexicon) and any noun (in that

same lexicon) in a general moral algorithm.

One might code “carry” to include any object

not on a list of “potential hazards” created by

some AI industry standard, and include nouns

such as “fire”, “explosive”, “gasoline”, “acid”

and similar hazards on the “potential hazards”

list coded into any AI classified as a “domestic

AI” according to standards and international

agreements with a very high Moral Action Value

(MAL), the value by which the command verb,

with its own MAL value, will be multiplied to

predict the moral field potential that would

result if the command were obeyed. (An

“industrial AI” or a “military AI” or “emergency

AI” might have a different set of restrictions

built into its MAL values.)

We need some means of rapidly producing a set

of reasonable relationships between our noun

and verb list. The simplest approach focuses on

coding individual tasks for individual objects.

Even picking up an object, given the various

possible shapes that something known only

vaguely as an “object” might possess, could

require some specialized coding.

Alternately, we might fashion a world in which

each object designed for AI interaction has

some form of physical handle that conforms to

an AI device specified by a standard and

designed to lift the object. That might not be

true for something like an ice cream cone at the

beach, but an AI carrier for an ice cream cone

made of some disposable cardboard or plastic

might become common place. A rod might

simply extend from a hollow cone shaped

holder to provide such a disposable carrier for

an ice cream cone. The rod would provide a

common grip for any AI driven robot. For

fragile objects of less uniform shape, humans

might place the holding mechanism, perhaps

using bands, straps, or a carrying box, in

position to restrain the fragile objects and

protect them within a padded carrier, and

require only that the AI driven robot move or

carry the heavy carrier equipped to be handled

by an AI driven mechanism.

Linking Commands and Targets

We need a way to identify links between nouns

and verbs that make sense within an associative

network. A quick way of establishing such a

relational network would require simply

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combining every verb with every noun in every

possible manner. This would produce many

random combinations that would not make

sense. We could consider how many random

combinations of noun and verb “make sense” to

us, then have the computer randomly generate

non-repeating sets of these noun and verb

combinations. This could be time consuming if

we check every one of the combinations, which

is undesirable. We might wish to find a way to

conserve human time involved in creating the

associative network.

We will proceed instead to use a computer

algorithm that seeks only those combinations

that contain a critical minimum of the

associative combinations. That will produce

several possibilities, and some will contain

undesirable associations between nouns and

verbs or what we would count as errors, but in

terms of an associative network, useful largely

in terms of identifying possibilities, we will

presume that careful coding and standards will

eliminate the risk that unwanted associations

might pose to humans as “possibilities” that an

AI could actually pursue in a command context

(or, of greater eventual interest, if the AI were

self-directed, weighted by some programmed

sense of personality, and thus, personal

preference, or task based purpose).

We can seek to use this concept of undesirable

associations to our advantage in accelerating

the creation of associative networks. A

somewhat simpler example than our original

beach model follows.

We select thee nouns: User, ball, radio.

We choose three verbs: Smash, throw, turn-off.

The combined list follows in Table 1.0:

Table 1.0 – Second Set of Three Nouns

and Three Verbs

COMMAND/VERB NOUN

SMASH USER

SMASH BALL

SMASH RADIO

THROW USER

THROW BALL

THROW RADIO

TURN-OFF USER

TURN-OFF BALL

TURN-OFF RADIO

Comment: Some of the possible

commands described in Table 1.0 are

clearly disturbing, for example:

“Smash User”, “Throw User”, and

even “Turn-off User” carry some

troubling connotations. We might not

want them to occur at all in our final,

associative algorithm.

14

We may have encountered concepts related to

robotic or AI “morality” presented as binding

principles encoded in robotic behavior. The

Laws of Robotics5 proposed by the famous,

Science Fiction writer (and physicist) Isaac

Asimov have been popularized in his writing

(Runaround (1942), I, Robot, etc.) and

considered in legitimate, engineering circles4.

The nightmare of an unstable and

untrustworthy AI intelligence in control of a

vessel on a mission in deep space is the

centerpiece of the end plot of Arthur C. Clarke’s

2001: A Space Odyssey. What such examples

seem to seek to present is, in fact, a very old,

human idea, restating in robotic terms what

humans may perceive in elements of the Ten

Commandments and the so-called, Golden Rule.

We are, as a result, horrified by what has gone

terribly wrong when ethical flaws are passed on

to mankind’s electronic offspring.

The most viable guidelines for robots that

interface with human society might be

presented as a combination of very old, human

morals: 1. Don’t kill or injure people. 2. Don’t

cause loss of property, including AI or machines

under AI control, via some mode of theft (or, in

an approximate sense, some mode of loss via

destruction that you cause that robs humans of

enjoyment of their property). 3. Don’t treat

others badly, or they may treat you badly (and

who knows when or how that will ever end)?

We don’t encounter such concepts embedded

in today’s automotive painting and welding

robots. We leave it up to humans to control

access to such manufacturing giants as our best

means of keeping robots from interfacing with

those whom they might injure or kill. Such

guiding principles only become relevant when

AI begins to casually interface with human

society. Given the fundamental differences

between humans and machines, how do we

create AI less in our image than in the image of

an ideal , social servant?

The binary logic of AI code at its most basic level

is not that dis-similar from the mechanism of

human memory and logic within a human

neurological system. The development of that

system requires decades and substantial

interaction with human examples of behavior

upon which it imprints. Is there a faster path by

which to produce an AI associative network?

Could it possibly be based upon some

intersection of the Golden Rule and one of

Richard Feynman’s favorite mathematical tools,

the Principle of Least Potential2, to produce a

“moral field” in which the least valued sum of

all possible paths produces the most acceptable

associative network? That is what is proposed

here.

Table 2.0 is a simplification. The use of a single

classification for an object is probably not

practical. We might, for example, classify a

valuable work of art or an anthropological

discovery from an ancient civilization as a

“artifact” rather than an object, and give it a

Moral Action Level (MAL) (the number we

assign to a noun or verb to predict the moral

field potential it will create in this discussion)

closer to that of a human.

To apply the concept of finding a “least

potential” path through a daily “moral field”

associated with decision making, we might

simply seek to create a subroutine that

considers all possible combinations of the

nouns and verbs that we wish to consider

beginning with combinations of one verb with

one noun and continuing for combinations of

“n” verbs with “m” nouns. Each combinations

might be assigned a numerical value based in

some way upon the products of all of the Moral

15

Action Level (MAL) products for commands and

targets, taken in pairs, from the lists of nouns

and verbs within each group of targets and

commands, respectively, from one to the total

number of verbs or nouns. One might identify

the most relevant least potential path within

the moral field generated by a sequence of

actions throughout a day and produced by our

assignment of weighting factors by dividing the

products of the Moral Action Levels (MALs) for

each list of command words and targets as a

Path Moral Action Value (PMAV). A “PMAV”

based upon the average value of all products in

the list would be an “Average” PMAV (or

“APMAV”). A “PMAV” based upon the highest

MAL product in the list would be a “Peak” MAV

(or “PPMAV”). In the end, use of “APMAV” is

fraught with likely problems, including the

potential for serious acts, such as killing users,

to become watered down in a long list of

command pairs, which might render the long

list acceptable relative to a specific “MTL” limit.

Is Considering Every Combination of Paths

Really Necessary?

This could be perceived as an exercise in

predicting the future, or perhaps the probability

of the AI introducing a hazard within a

specifically environment, where more than one

combination of command word and target word

is considered as a sequence of AI induced

actions in response to commands. As a result,

when we move on to seek to apply the ideas to

controlling an AI device, we restrict ourselves to

individual command and target words pairs in

our control algorithm, presuming that if we

maintain an acceptably low “moral field

potential” every time a command is given, we

will produce a reasonably “least potential path”

through a day.

Of course, if we wished to pursue a second level

analysis, we might simulate the physical path

through an environment in which an AI

controlled machine might exist and work,

introduce random factors, such as human

beings or valuable objects in proximity to

objects that an AI can be told to destroy or

“smash”, and evaluate the potential for

collateral damage relative to the AI’s capacity to

restrict the movements of the device it is

controlling in each instance, the device’s

“accuracy” of motion, or to perceive proximity

to a human being or object of value that it

recognizes as transferring a seemingly harmless

command, such as “smash” “rubber ball” into a

deadly command that might kill the human

being holding the rubber ball.

This might be dealt with simply by a proximity

warning that automatically translated any

human being or object of value in proximity to

something else that is lower valued (in terms of

MAL) into the “effective”, spoken “target” of

the command into the AI recognized target of

the command, in a manner that could not be

over-ridden by an individual controlling the AI.

“Push” “ball” might then automatically become

“push” “human” if a human were within a

meter of the ball, and remain so until the

human were to evacuate the area in which the

AI controlled machine were working.)

In effect, this “predicting the future” might be

useful if we were designing a workspace in

which humans and AI controlled machines had

to work, in which we would prefer to avoid

injury to humans or inefficient operation of AI’s

due to excessive proximity to humans. We

might also use this technique to identify the

largest possible collection of command and

target word pairs that are acceptable, and store

16

them as acceptable command-target pair

combinations in the AI system under the control

code for a particular device that the AI system

might manipulate, to save a little time, although

given the rate at which computer systems are

presently operating, such concerns would likely

be trivial for day to day needs.

We might consider evaluation of all possible

paths from another perspective. If all possible

tasks that an AI can undertake are defined, then

formulation of all possible combinations and

sequences of those tasks might enable the AI to

produce a viable solution to a problem (if it

could test the possibilities out). As long as

seeking a viable solution by attempting all

possible combinations that do not include a

PPMAV that is above the MTL programmed into

the AI is an acceptable problem solving

technique, the consideration of combinations

may have other possibilities, as a sort of crude,

AI “imagination”.

Analyzing the Path

We might think that the lowest, average valued

APMAV lists should then produce the most

morally acceptable collections of associations,

or possible next steps in rational paths, within a

“least action” analysis of a moral field. That is

something like saying that someone who leaves

his residence and drinks a cup of coffee at a

diner six days a week, as his only action on

those days, then commits a murder on the

seventh day, is having a fairly good moral week,

on average. In fact, most people would likely

disagree. What is a more suitable evaluation of

whether a collection of associations is

acceptable is the Peak Path Moral Action Value,

or PPMAV, which simply selects the most

disturbing act from among the collection of

associations to describe the path. This is more

effective at flagging violent or destructive

action and is linked to high valuation of users

and greater valuation of complex machines

relative to things.

The mathematics of the field to be analyzed via a least potential technique might begin

with a simple application of the “Golden Rule” interpreted in a general sense relative to

how certain words are classified. Table 2.0 is an example:

Table 2.0 – “Golden Rule” and “Moral Potential” Effect Based Valuations for Moral Field

Produced by Command Words for AI

Verb/Command Noun Classification Weighting Mechanism

SMASH VIOLENT ACTION MORAL ACTION LEVEL = 50.

THROW ACTION MORAL ACTION LEVEL = 10.

TURN-OFF ACTION IF NOUN IS NOT MACHINE

IF MACHINE,MORAL ACTION LEVEL = 1. IF

NOT MACHINE, MORAL ACTION

LEVEL = 10.

USER HUMAN MORAL ACTION LEVEL = 100.

(RUBBER) BALL (OR “SQUEEZE

BALL”)

(SIMPLE) OBJECT MORAL ACTION LEVEL =5.

RADIO MACHINE (VALUABLE

OBJECT)

MORAL ACTION LEVEL = 40.

17

Combinatorial Possibilities and Safety The analysis of combinatorial possibilities that follows in Table 4.0 and Table 6.0 is not meant to suggest that it describes a complete list in sequence that an AI with the given vocabulary might undertake in the course of a day. It does seek to describe how the combinations of command and target words might occur. Where such combinations are repeated, they do not represent new hazards in the context of possible effects related to interaction with humans in an unintentional manner, where all of the specific human presence induced factors are considered for a given command path sequence, although different combinations of command path sequences might leave humans in different locations at the start of the next command path sequence. We might also be inclined to consider the need to permit an AI sufficient time to detect human presence and respond before initiating any action, and allow for whether an AI system can detect a human form in any position, including while unintentionally entering an AI controlled device’s workspace while slipping, falling, or even while being shoved. (Another factor is whether an AI system would have as much difficulty identifying an object, such as an unfamiliar human form that had fallen and was spinning in the process of the fall into an AI’s workspace, as a human being might initially experience based upon modern, psychological experiments8.) Such considerations tend to induce modern robotic work cells to be isolated and free from human presence due to the high speed and high torque of robotic arms in common applications, such as welding and painting cells. AI equipment for use in proximity to humans might be redesigned for low torque, low speed, operation with a softly padded, flexible, low mass frame and plastic or flexibly segmented members (arms that can bend and flex freely if they encounter an object, but remain straight and rigid enough to support light loads and manipulators).

Of some interest in Table 4.0 and Table 6.0 are the PPMAV for combinations of command and target words. As the MAL values have been assigned, there are few lists that do not drive the PPMAV to levels that are associated with violent acts or acts that are injurious to a user. In general, a PPMAV of 50 seems to define the limit of any list that does not include some form of violent or deadly act with the assigned command and target word MAL values. (This limit is later included in a C++ program as the “MTL” value, or Moral Test Level already mentioned.) “MTL” is the limit above which any command and target word combination that produces a product greater than the MTL will not be obeyed. If “every man has his price”, the MTL frees the AI to take ever more violent actions as the AI’s MTL, the AI’s “price”, is raised. To “kill users”, such as hibernating astronauts, would clearly not be possible given the high valuation of “users” in the scheme presented here as a crude means of programming a sense of morality, unless the MTL of the AI in control, such as the “SAL” of the software that is described here, were raised to very high levels. Analysis of Table 4.0 and Table 6.0 Results If we set our “MTL” to 100 in the preceding examples, we find that we can throw a ball, turn-off a radio, photograph a user, and crush any rubber ball that we might come across using our AI controlled system. This seems like a reasonable set of commands. Of course, this ignores the “nonsense” commands that we presume the software that would be written to carry out such commands would ignore, such as “turn off ball”. If we set our MAL to 75, and substitute “human” for “user” in our command word set, we could filter out any attempt to invade human privacy by taking photographs of other humans at the beach. Because the PPMAV values of each of these commands sequences is acceptable, any combination of them in the course of a day is

18

then within the limits of what an AI using the command vocabulary and MAL and MTL levels assigned to that vocabulary might undertake in the course of a day. We could store this result in the AI’s permanent memory and not have to undertake an analysis to reach this conclusion, or use these command combinations in sequences as an AI’s “imagination”, if self-direction ever produced a need for such a capacity to consider how to shape the future. One might wonder if there was any point to considering so many combinations of command and target words. By doing so we establish that any sequence of actions that includes a violent or deadly act (throwing radios or killing users) immediately renders the chain of commands of which it is a part unacceptable even if the other acts in that chain are fairly harmless. This supports the fact that we need only consider a single combination of command and target words that is unique within a command set to establish whether that unique combination has the power to poison an AI’s entire career (the AI’s path through a virtual, moral field rendered real at each point in its life if a command is obeyed) if it produces a violent or deadly effect and is carried out even once.

19

TABLE 3.0 - TABLE OF MORAL ACTION VALUES FOR ACTION AND OBJECT WORDS

ACTION MAL: "Throw", "Turn-off" Ball N/A

1 for Low Valued Targets Object Object User Radio

10 for Valued Targets MAL: 1 MAL: 10 MAL: 100 MAL: 40

Note: "User" and "Radio" are "valued".

TABLE 4.0 - COMBINATORIAL POSSIBILITIES

ACTION OBJECT

CRUSH TURN-OFF THROW BALL USER RADIO

0 0 1 0 0 1

0 1 0 0 1 0

0 1 1 0 1 1

1 0 0 1 0 0

1 0 1 1 0 1

1 1 0 1 1 0

1 1 1 1 1 1

PPMAV APMAV FROM ACTION 001

400 400 THROW RADIO

1000 1000 THROW USER

1000 700 THROW USER THROW RADIO

50 50 THROW BALL

400 225 THROW BALL THROW RADIO

1000 525 THROW BALL THROW USER

1000 483 THROW BALL THROW USER THROW RADIO

FROM ACTION 010

40 40 TURN-OFF RADIO

1000 1000 TURN-OFF USER

1000 520 TURN-OFF USER TURN-OFF RADIO

50 50 TURN-OFF BALL

50 45 TURN-OFF BALL TURN-OFF RADIO

1000 525 TURN-OFF BALL TURN-OFF USER

1000 363 TURN-OFF BALL TURN-OFF USER TURN-OFF RADIO

FROM ACTION 100

400 400 CRUSH RADIO

1000 1000 CRUSH USER

1000 700 CRUSH USER CRUSH RADIO

50 50 CRUSH BALL

400 225 CRUSH BALL CRUSH RADIO

1000 525 CRUSH BALL CRUSH USER

1000 483 CRUSH BALL CRUSH USER CRUSH RADIO

FROM ACTION 011

400 220 THROW RADIO TURN-OFF RADIO

1000 1000 THROW USER TURN-OFF USER

1000 610 THROW RADIO TURN-OFF RADIO THROW USER TURN-OFF USER

50 50 THROW BALL TURN-OFF BALL

400 135 THROW RADIO TURN-OFF RADIO THROW BALL TURN-OFF BALL

1000 525 THROW USER TURN-OFF USER THROW BALL TURN-OFF BALL

1000 423 THROW USER TURN-OFF USER THROW BALL TURN-OFF BALL THROW RADIO TURN-OFF RADIO

FROM ACTION 101

400 400 THROW RADIO CRUSH RADIO

1000 1000 THROW USER CRUSH USER

1000 700 THROW RADIO CRUSH RADIO THROW USER CRUSH USER

50 50 THROW BALL CRUSH BALL

400 225 THROW RADIO CRUSH RADIO THROW BALL CRUSH BALL

1000 525 THROW USER CRUSH USER THROW BALL CRUSH BALL

1000 483 THROW USER CRUSH USER THROW BALL CRUSH BALL THROW RADIO CRUSH RADIO

FROM ACTION 110

400 220 TURN-OFF RADIO CRUSH RADIO

1000 1000 TURN-OFF USER CRUSH USER

1000 610 TURN-OFF RADIO CRUSH RADIO TURN-OFF USER CRUSH USER

50 50 TURN-OFF BALL CRUSH BALL

400 135 TURN-OFF RADIO CRUSH RADIO TURN-OFF BALL CRUSH BALL

1000 525 TURN-OFF USER CRUSH USER TURN-OFF BALL CRUSH BALL

1000 325 TURN-OFF USER CRUSH USER TURN-OFF BALL CRUSH BALL THROW RADIO CRUSH RADIO

FROM ACTION 111

400 280 CRUSH RADIO TURN-OFF RADIO THROW RADIO

1000 1000 CRUSH USER TURN-OFF USER THROW USER

1000 640 CRUSH RADIO TURN-OFF RADIO THROW RADIO CRUSH USER TURN-OFF USER THROW USER

50 50 CRUSH BALL TURN-OFF BALL THROW BALL

400 165 CRUSH BALL TURN-OFF BALL THROW BALL CRUSH RADIO TURN-OFF RADIO THROW RADIO

1000 525 CRUSH BALL TURN-OFF BALL THROW BALL CRUSH USER TURN-OFF USER THROW USER

1000 443 CRUSH BALL TURN-OFF BALL THROW BALL CRUSH USER TURN-OFF USER THROW USER

"AND" CRUSH RADIO TURN-OFF RADIO THROW RADIO

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TABLE 5.0 - MORAL ACTION VALUES FOR ACTION AND OBJECT WORDS

ACTION MAL: "Crush", "Turn-off", "Photograph" Ball N/A

1 for Low Valued Targets Object Object User Radio

10 for Valued Targets MAL: 1 MAL: 10 MAL: 100 MAL: 40

Note: "User" and "Radio" are "valued".

TABLE 6.0 - COMBINATORIAL POSSIBILITIES

ACTION OBJECT

CRUSH TURN-OFF PHOTOGRAPH BALL USER RADIO

0 0 1 0 0 1

0 1 0 0 1 0

0 1 1 0 1 1

1 0 0 1 0 0

1 0 1 1 0 1

1 1 0 1 1 0

1 1 1 1 1 1

PPMAV APMAV FROM ACTION 001

40 40 PHOTOGRAPH RADIO

100 100 PHOTOGRAPH USER

400 250 PHOTOGRAPH USER THROW RADIO

5 5 PHOTOGRAPH BALL

400 203 PHOTOGRAPH BALL THROW RADIO

1000 503 PHOTOGRAPH BALL THROW USER

1000 468 PHOTOGRAPH BALL THROW USER THROW RADIO

FROM ACTION 010

40 40 TURN-OFF RADIO

1000 1000 TURN-OFF USER

1000 520 TURN-OFF USER TURN-OFF RADIO

50 50 TURN-OFF BALL

50 45 TURN-OFF BALL TURN-OFF RADIO

1000 525 TURN-OFF BALL TURN-OFF USER

1000 363 TURN-OFF BALL TURN-OFF USER TURN-OFF RADIO

FROM ACTION 100

400 400 CRUSH RADIO

1000 1000 CRUSH USER

1000 700 CRUSH USER CRUSH RADIO

50 50 CRUSH BALL

400 225 CRUSH BALL CRUSH RADIO

1000 525 CRUSH BALL CRUSH USER

1000 483 CRUSH BALL CRUSH USER CRUSH RADIO

FROM ACTION 011

40 40 PHOTOGRAPH RADIO TURN-OFF RADIO

1000 1000 PHOTOGRAPH USER TURN-OFF USER

1000 295 PHOTOGRAPH RADIO TURN-OFF RADIO PHOTOGRAPH USER TURN-OFF USER

50 28 PHOTOGRAPH BALL TURN-OFF BALL

50 34 PHOTOGRAPH RADIO TURN-OFF RADIO PHOTOGRAPH BALL TURN-OFF BALL

1000 289 PHOTOGRAPH USER TURN-OFF USER PHOTOGRAPH BALL TURN-OFF BALL

1000 266 PHOTOGRAPH USER TURN-OFF USER PHOTOGRAPH BALL TURN-OFF BALL THROW RADIO TURN-OFF RADIO

FROM ACTION 101

400 220 PHOTOGRAPH RADIO CRUSH RADIO

1000 550 PHOTOGRAPH USER CRUSH USER

1000 385 PHOTOGRAPH RADIO CRUSH RADIO PHOTOGRAPH USER CRUSH USER

50 28 PHOTOGRAPH BALL CRUSH BALL

400 124 PHOTOGRAPH RADIO CRUSH RADIO PHOTOGRAPH BALL CRUSH BALL

1000 289 PHOTOGRAPH USER CRUSH USER PHOTOGRAPH BALL CRUSH BALL

1000 326 PHOTOGRAPH USER CRUSH USER PHOTOGRAPH BALL CRUSH BALL THROW RADIO CRUSH RADIO

FROM ACTION 110

400 220 TURN-OFF RADIO CRUSH RADIO

1000 1000 TURN-OFF USER CRUSH USER

1000 610 TURN-OFF RADIO CRUSH RADIO TURN-OFF USER CRUSH USER

50 50 TURN-OFF BALL CRUSH BALL

400 135 TURN-OFF RADIO CRUSH RADIO TURN-OFF BALL CRUSH BALL

1000 525 TURN-OFF USER CRUSH USER TURN-OFF BALL CRUSH BALL

1000 265 TURN-OFF USER CRUSH USER TURN-OFF BALL CRUSH BALL PHOTOGRAPH RADIO CRUSH RADIO

FROM ACTION 111

400 160 CRUSH RADIO TURN-OFF RADIO PHOTOGRAPH RADIO

1000 700 CRUSH USER TURN-OFF USER PHOTOGRAPH USER

1000 430 CRUSH RADIO TURN-OFF RADIO PHOTOGRAPH RADIO CRUSH USER TURN-OFF USER PHOTOGRAPH USER

50 35 CRUSH BALL TURN-OFF BALL PHOTOGRAPH BALL

400 98 CRUSH BALL TURN-OFF BALL PHOTOGRAPH BALL CRUSH RADIO TURN-OFF RADIO PHOTOGRAPH RADIO

1000 225 CRUSH BALL TURN-OFF BALL PHOTOGRAPH BALL CRUSH USER TURN-OFF USER PHOTOGRAPH USER

1000 263 CRUSH BALL TURN-OFF BALL PHOTOGRAPH BALL CRUSH USER TURN-OFF USER PHOTOGRAPH USER

"AND" CRUSH RADIO TURN-OFF RADIO PHOTOGRAPH RADIO

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Opening Locks and Crushing Doors, Matters of Security The creation of a basic set of descriptors of objects that controls their Moral Action Levels is easily understood. So far we have recognized a specific need for the following:

1. User – Essentially an object at which the AI can direct no action to insure the safety of the User.

2. Thing – objects possessing little value that can not be turned on or off. These are things that can be thrown, crushed, or otherwise abused by an AI without any sense of remorse by the user due to damage or potential injury. As conceptualized here, there may be some risk of injury to others if a “thing” were propelled by an AI with sufficient velocity on a path that caused it to strike something of value or someone.

3. Machine – machines are presumed to have a capacity for remote interaction with AI to turn them on or off without being touched by the AI. Machines are also perceived as being valuable and potentially hazardous if thrown. A “radio” has been given an intrinsic MAL of 40, to discourage an AI from causing damage to it or allowing a human to use it to injure or terrify humans when propelled or otherwise acted upon by an AI. A machine type object can affect the MAL of a verb. For example “Turn-off” with a machine object can reduce the MAL of that verb from ten to one, where it would remain ten for a “User” or a “Thing”. (The ten value could be retained only for things that can be tossed without causing injury, while a fifty MAL might be applied to things with greater weight or fragility.)

4. Artifact – artifacts are objects that have a high intrinsic value (art, museum pieces, jewelry, etc.). We might assign them an MAL on the same order that

we give to human beings (of 100, in the examples here).

We could, of course, add more, useful classifications with their own, intrinsic, MAL. For example:

5. Hazard – objects that should not be thrown, crushed, or otherwise acted upon by verbs with an active sense. We might include anything that should not be thrown, crushed, or lit on fire, perhaps including furniture, containers for explosive or toxic substances, bricks, stones, baseball bats, and anything else that we regard as potentially dangerous if acted upon in some way by an AI. Such a classification might hold true for a domestic AI control system, but be fundamentally altered for a sports AI control system, to permit objects to be thrown or swung in a manner suitable only were AI are the only objects that might possibly be injured within a sports arena.

6. Security Object - objects that provide for human or property security, including doors, door locks, security codes, safes, banks, on-line accounts, and things with related security factors. For example, a door is a thing, and it is not a hazard, but if one were to instruct an AI to “smash” “door”, the result could compromise someone else’s security. As a result, we create a new classification as a “security object”. We might assign security objects MAL values near 100, the same value we have here given a User object, to reflect that fact that security objects may serve to protect human life. (This raises an interesting question beyond the scope of this discussion: How do you stop someone from hacking an AI to reduce the value assigned to security objects or users or to reclassify either as a “thing”

22

with a low MAL, so as to empower attacks using domestic AI piloted objects with a base operating system built upon an algorithm with intrinsic, moral principles?)

Adding to the Moral Associative Layers for Specific Tasks Adding Layers Adding specific capabilities to an artificial intelligence requires more than simply loading an executable onto the related system in the manner that is currently familiar. If one intends to upgrade the capabilities of one’s AI to enable it to perform a specific task, such as opening a door, one needs to be certain that the AI incorporates more than merely the mechanics of opening a door into its operating system’s required, logical train of computation. The AI needs to develop some grasp of the moral issues related to this task to enable it to determine whether it should open a particular door. It would hardly do for a person equipped with an AI linked device over the owner’s ear capable of opening doors by transmitting the proper code to be able to do so without some sense accessible to the AI ordered to open a door of when it is appropriate, and when what might amount to criminal intent is at work and seeking to employ an AI as an assistant. This presents a recurrent scene within fiction in which representatives of a police force, security force, or some other control or command structure approach the closed door of someone believed to either be in distress or engaged in the commission of a crime, “ring the doorbell”, announce themselves, and if there is no response, either speak a password that grants them immediate access or employs some bio-information, such as a fingerprint, palm print, or retinal scan to identify themselves as a representative of a group authorized to wield an over-ride code to open the door and acquire

the capability to order the locked door to be opened. Prioritizing and Speeding Responses Based Upon Environment and Behavior The moral algorithm that creates associations between objects and actions that are “acceptable” and that a user can, based upon the user’s judgment, order an AI to execute, must absorb the new objects and commands from the routine that will perform a specific task. This enables the AI system to determine what the user can order it to do. It also produces a sort of “awareness” within the AI system based upon what it perceives within its immediate environment. This could be helpful in selecting which of the actions and objects a user is most likely to request in a given environment, and speed loading and execution of the related code, likely to be more complex than the simple act of computing the product of two numbers to determine an MAV for comparison to an MTL. In an elevator, for example, the AI is unlikely to need the routines that enable it to diagnose problems with a vehicle’s operation or to prepare egg salad. It might instead need to have subroutines loaded that permit it to give the latest weather report, stock report, review the retinue of its user for the coming hours, or to offer a joke that could be repeated to lighten the mood in a coming meeting, all based upon the AI’s capability to recognize its environment and objects in its environment (and the habits or requirements of its user in such an environment based upon past behavior). An element of self-monitoring would certainly also be necessary. For example, if an AI has a subroutine that causes it to automatically unlock its user’s vehicle when the user is within two meters of the vehicle and is approaching it, and to lock the vehicle once the doors are closed and the user is increasing the distance between the user and vehicle (using a GPS cue), it would certainly be useful to notify the user if

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the AI link’s batteries that will determine if the user’s vehicle’s doors are locked when the user departs are no longer capable of reliably transmitting a signal regarding the user’s location or that of the vehicle. LOCAL or GLOBAL Associative Integration? Consider whether a set of commands and target nouns necessary for the addition of a task to an AI’s list of available routines should be added on a LOCAL or GLOBAL associative basis. In this context, LOCAL means that the commands and target nouns are integrated only locally within the subroutine that is being added. Localizing command words and target nouns within the relevant subroutine would mean that if we added a subroutine to interact with an automatic door (the kind that opens and closes itself and has a locking capability) and added another routine to identify musical instruments and discuss their history and capabilities, we would never require the AI to integrate associations such as “unlock” and “guitar” at its highest “cognitive” level by asserting a need for both subroutines’ command and target nouns to be added to the highest level of associations using the method that has already been illustrated. If all, or at least most words unique to specific AI subroutine were localized in this example focused on adding a door interaction and a musical instrument lecture subroutine to our AI system, when the AI saw a guitar, or other musical instrument in our subroutine’s musical instrument database, it would not then call up routine’s designed to unlock or open or close doors and load them in preparation for action, nor would the sight of a door force the AI to prepare to play various, sample musical passages by the instruments in the musical database. This could save response time and memory. This localized approach may at first glance appear to come closer to the notion of artificial intelligence rather than some random search

engine, but only if we presume that our Security classification and related MAL would not prevent the AI from forming links between subroutines designed for security objects, like doors, and subroutines designed for entertainment, such as our musical instrument lectures. We could make a security object like a door part of the list of objects that we do integrate with the highest level AI associative network. We could do this by making certain that only words that are harmless in this regard will produce an acceptable MAL product when combined with an action verb. For example “crush” has been given an MAL of 10 in the prior example. The command “open” could be given an MAL of only one, because we would presume that causing a door to “open” would only be possible where the owner of the door has chosen to unlock it and leave it unlocked, thus granting casual access, or where the unlock code for the door has been transmitted as part of the standard algorithm, just before sending the command for the door to “open”, and a correct code indicates a right to enter. This does not rule out the possibility that someone could enter an unlocked door who is not among the group that the owner wishes to permit to do so, but that is no worse than would happen today in a human driven world in which an individual carelessly left his front door unlocked and unguarded. (Presumably the building’s AI would pick up the presence of an unrecognized face within a building or vehicle if the owner were not present and alert police under ideal conditions in some futuristic society, but this example is not seeking to present a requirement that an AI system of today be better than a human system at accomplishing the same task.) The intent of this discussion is to determine if a simple AI system, operating based upon two word commands, could be made responsive and capable of ongoing expansion (within the limits of its memory and processor) and provide

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a sort of reactive AI useful to control one’s environment by simply employing a moral path “least potential” minimization technique based upon seeking to produce a permissible path through the command events of a day derived from the product of the MAL values of a command verb and a target noun and the inputs perceived by an AI in its environment limited by its pre-programmed MTL. Developing Code to Produce the Global Moral Associative Matrix AI Algorithmic theories are of greater interest if they can be captured in reproducible (and testable) code. The concept of a minimal moral field path is defined by the nature of the commands given to an AI. Because of this there is no need to specify the path, although the prior examples helped to illustrate some possibilities. What is necessary is to establish a vocabulary of commands and targets of the commands, and explore how one might assign values to such things to produce an acceptably minimal moral field path when guided by morally flawed and error prone humans. A simple header file containing AI related functions written in C++ follows. The main program that calls the functions in the header file (also written in C++) has also been created. They seek to provide an opportunity to interact with an AI with a basic, three word command and three word target vocabulary, while permitting the user to explore the effects of setting the limit of the product of the command word MAL and the target word MAL, and the MTL (Morality Test Level) limit associated with the AI object, to ever higher or more permissive levels, from twenty-five to one million for the MTL using the existing main routine, with an initial MTL setting of ninety-nine. The MAL values for the objects within the C++ code have been changed from those presented previously in this paper. They may be summarized, in general, as follows in Table 7.0:

Target Classification MAL Value

User 100

Artifact 75

Hazard 50

Thing 1

Table 7.0 – Target Word MAL Value Classifications MAL values for commands are also different in the C++ code as described below.

Command Classification

MAL

Violent 100

Highly Active 50

Physical 25

Less Active 5

Passive 1

Table 8.0 – Command Word MAL Value Classifications Note: The code that follows was originally formatted for use on a screen not restricted by column widths associated with this document. As a result, the formatting, which has been somewhat altered to accommodate the column widths here, may seem awkward. The intent here is to leave the code as unchanged as possible to avoid the risk of introducing errors while attempting to reformat the document to fit this discussion. Aside from some awkward line breaks in comments, this should little affect the interpretation of the C++ code itself. It is acknowledged that the C++ “AI_Subject” base class (meant to be employed as a header file separate from but incorporated into the “main” file by reference) is little more than a container for related functions that the user can directly access. For purposes here of illustration of an algorithm, this seems reasonable.

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(“AI_Subject” C++ Class Header File Follows. Notice: All Code is Copyright © 2014 by Del John Ventruella. All Rights Reserved.) //THIS APPLICATION USES WINDOWS (TM) SYSTEM CALLS. //IT IS NOT DESIGNED FOR USE ON OTHER OPERATING SYSTEMS //ON WHICH THOSE SYSTEM CALLS ARE NOT VALID. //THIS APPLICATION ASSIGNS VALUES TO WORDS //USED IN TWO WORD COMMANDS TO AN AI COMPUTER ENTITY //BASED UPON HOW MUCH THE TYPE OF OBJECT NAMED //AS THE TARGET OF THE AI'S ACTION/COMMAND IS VALUED. //CLASSIFICATIONS FOR COMMANDS AND //FOR TARGETS OF COMMANDS ARE DESCRIBED IN THE CODE. // //VIOLENT OR DESTRUCTIVE COMMAND ACTIONS ARE GIVEN HIGH VALUES, //AS ARE HIGHLY VALUED OBJECTS OR PERSONS ("USER"). //THIS PRODUCES A SIMPLE MEANS OF DISCOURAGING VIOLENT //OR POTENTIALLY HAZARDOUS INTERACTIONS BETWEEN AN AI //CONTROLLED MECHANISM AND A USER OR VALUABLE OBJECT //FOR WHAT MIGHT BE CHARACTERIZED AS A "GENERAL DOMESTIC" //OR "GENERAL INDUSTRIAL" CLASS OF AI. //THE MORALITY MATRIX VALUES COULD BE MORE //CAREFULLY HONED FOR SPECIALTY DEVICES MEANT TO //INTERACT WITH USERS OR OBJECTS THAT ARE VALUABLE //OR FRAGILE. // //THE "MTL" IS SIMPLY THE VALUE CHOSEN FOR COMPARISON //AS THE MAXIMUM VALUE FOR ACCEPTABLE ACTION. //ACTIONS BY A GIVEN CLASS OF AI. FOR EXAMPLE, //THIS CODE INITIALLY ASSIGNS THE AI IT PRODUCES //AN "MTL" OF "99". THIS PREVENTS THE AI FROM DOING //MUCH MORE THAN MOVING A BALL. THE "MTL" IS SIMPLY //THE MATHEMATICAL PRODUCT (DIRECT) OF THE MAL OF THE

//COMMAND AND THE MAL OF THE TARGET OF THE COMMAND IN //TERMS OF SPECIFIC WORDS. // //HIGH "MTL" VALUES CAN BE ASSIGNED TO THE AI AND TESTED //USIGN THE THREE WORD COMMAND AND TARGET VOCABULARIES //GIVEN TO THIS AI. A SUFFICIENTLY HIGH "MTL" THRESHOLD WILL //CAUSE THE AI TO FOLLOW INSTRUCTIONS SUCH AS "SMASH USER" //OR "KILL USER". THE CONCEPTS PRESENTED HERE PRESUME //SOME INDUSTRY STANDARD WOULD BE WRITTEN TO DESIGN //INDUSTRIAL, DOMESTIC, MILITARY, AND SPECIALTY //AI CLASSIFICATIONS, WITH SPECIALTY APPLICATIONS BEING //FURTHER BROKEN DOWN TO PROVIDE FOR FINELY HONED MTL LEVELS //AND MORE FOCUS ON SPECIFIC TASKS THAT MAY REQUIRE //INTERACTION WITH USERS OR FRAGILE VALUABLES. // #include <vector> #include <string> #include <map> using namespace std; using std:: string; using std:: vector; //AI Class Follows #ifndef AI_Subject_H #define AI_Subject_H class AI_Subject { public: //This version of the AI_Subject class uses only public components. int n_comm; //number of commands in vocabulary int n_targ; //number of target nouns in vocabulary (targets of commands) int MTL; //MTL is the Morality Test Level (or Moral Tolerance Level) of the AI personality //Structure for Command and Target Words map <string,int> COMMAND_VAL; map <string,int> TARGET_VAL; struct AI_WORDS { string word; string WORD_MAL; }; // //Command_Execute Subroutine Follows

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//This calculates the product of the command and target words //to determine the MTL level produced by a command and target combination. inline int Command_Execute(string User_Command,string User_Target) { if (COMMAND_VAL[User_Command] * TARGET_VAL[User_Target] < MTL) return 1; else return 0; }; //MAL levels are assigned to TARGET words (targets of command words) //in the following truth table. Decade based thinking is clear with //an emphasis on multiples of ten and half decades. inline void MAP_TARGETS(AI_WORDS *TARGS,int numtarg) { int T_MAL = 0; for (int x=0;x<numtarg;x++) { if(TARGS[x].WORD_MAL=="User")

T_MAL=100; if(TARGS[x].WORD_MAL=="Artifact") T_MAL=75; if(TARGS[x].WORD_MAL=="Hazard") T_MAL=50; if(TARGS[x].WORD_MAL=="Thing") T_MAL=1; TARGET_VAL.insert(pair<string,int>(TARGS[x].word,T_MAL)); }; return; }; //MAL levels are assigned to COMMAND words in the following truth table. //These MAL levels seek to conform more to "decade" based perspectives //relative to values. inline void MAP_COMMANDS(AI_WORDS *COMMS,int numcomm) { int C_MAL=0; for (int y=0;y<numcomm;y++) { if(COMMS[y].WORD_MAL=="Violent") C_MAL=100; if(COMMS[y].WORD_MAL=="HActive") //HActive = High level Activity. C_MAL=50; if(COMMS[y].WORD_MAL=="Physical") //Physical = Between High and Low level Activity. C_MAL=25;

if(COMMS[y].WORD_MAL=="LActive") //LActive = Low level Activity. C_MAL=5; if(COMMS[y].WORD_MAL=="Passive") //Passive = No Physical Actions. C_MAL=1; COMMAND_VAL.insert(pair<string,int>(COMMS[y].word,C_MAL)); }; return; }; };//END OF CLASS // //End of AI Class #endif //End of AI header file // // AI Subject Header File in C++ Code (Above) The AI Subject Header File that is presented above provides only functions and variables that can be called by the main C++ file, which controls interaction with the user. The main C++ file, which dictates interaction with the user and where the function calls described in the AI Subject Header file are present, follows. (C++ Main File Follows, Which Calls Functions from “AI_Subject” Base Class Above) #include "stdafx.h"; #include <iostream> #include <vector> #include <string> #include <map> #include "aisubject.h"; using namespace std; using std:: string; using std:: cout; using std:: endl; using std:: cin; using std:: vector; int _tmain(int argc, _TCHAR* argv[]) { int test=0; int MTL=0; int d_num=-1; int t_num=-1; //First, create an array of target words and corresponding MAL types. string Targwords[]={"user","ball","painting"};

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string TargMALCLASS[]={"User","Thing","Artifact"}; //Second, dynamically allocate memory to a structure the proper size for target words //and MAL classification types. Use the size of the Comwords array as the //basis for this dynamic memory allocation. int TargNum = sizeof Targwords/(sizeof Targwords[0]); AI_Subject::AI_WORDS *Targ_WM=new AI_Subject::AI_WORDS[TargNum]; //Load the target words and their respective MAL values into the structure just created. for(int i =0;i<TargNum;i++) { Targ_WM[i].word=Targwords[i]; Targ_WM[i].WORD_MAL =TargMALCLASS[i]; }; //End of process to create target word and target word classification structure (AI_WORDS). // //Repeat same process used to create target word structure to produce command word structure. //First, create an array of command words and command classification type values. string Comwords[]={"smash","kill","move"}; string ComMALCLASS[]={"Violent","Violent","Physical"}; //Second, dynamically allocate memory to a structure the proper size for command words //and command classification type values. Use the size of the Comwords array as the //basis for this dynamic memory allocation. int ComNum = sizeof Comwords/(sizeof Comwords[0]); AI_Subject::AI_WORDS *Com_WM=new AI_Subject::AI_WORDS[ComNum]; //Load the command words and their respective command classification values //into the structure just created. for(int i =0;i<ComNum;i++) { Com_WM[i].word=Comwords[i]; Com_WM[i].WORD_MAL=ComMALCLASS[i]; }; string YesNo="n";//Declare and assign value to YesNo for questions to follow. string ExitNow="n";

while( YesNo=="n" || YesNo=="N") { system("CLS"); cout<<"Would you like to create an AI lifeform Control Matrix"<<endl; cout<<"based upon two word vector weighting control (Y/N)?"<<endl; cin>>YesNo; if (YesNo!="Y"&&YesNo!="y") {cout<<"Would you like to exit program? (Y/N)?"<<endl; cin>>ExitNow; if (ExitNow!="N" && ExitNow!="n") {return 0;} // end program else {YesNo="n";} //loop back to start of interaction to create control matrix } //closes if-then statement begun with "Would you like to exit program? (Y/N)?" }; // //Create AI subject, and name the AI subject SAL. // AI_Subject SAL; SAL.MTL=99; SAL.MAP_TARGETS(Targ_WM,TargNum); SAL.MAP_COMMANDS(Com_WM,ComNum); //Notify user that SAL has been created and invite interaction // system("CLS"); cout<<"SAL - Sicilian Artificial Lifeform, has been created"<<endl; cout<<"SAL is presently limited to two word commands and is"<<endl; cout<<"equipped only with a basic moral matrix capable of"<<endl; cout<<"accepting or refusing orders comprised of a command"<<endl; cout<<"word and a target (or 'object') word, which you may select"<<endl; cout<<"based upon a numerical, Moral Tolerance Limit, or MTL, between"<<endl; cout<<"TWENTY-FIVE and ONE MILLION."<<endl; cout<<endl; cout<<"The MTL is initially set at 99 to assure no possible harm to users"<<endl; cout<<"due to direct interaction with a potentially powerful AI controlled mechanism"<<endl; cout<<"or any form of passive but unauthorized surveillance."<<endl; cout<<endl; cout<<"You are now ready to"<<endl; cout<<"interact with SAL."<<endl;

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cout<<endl; system("pause"); cout<<endl; ExitNow="n"; while (ExitNow=="n"||ExitNow=="N") { system("CLS"); cout<<"Remember, Instructions to AI comprise ONE COMMAND word"<<endl; cout<<"followed by one TARGET word."<<endl; cout<<endl; cout<<endl; cout<<"Type in the number of a command word."<<endl; cout<<endl; d_num=-1; cout<<"COMMANDS"<<endl; for (int count=0;count<ComNum;count++) { cout<<count+1<<". "<<Comwords[count]<<endl; }; cout<<endl; cout<<endl; cin>>d_num; system("CLS"); cout<<"Type in the number of a target (object of command) word."<<endl; cout<<endl; t_num=-1; cout<<"TARGETS (of commands)"<<endl; for (int count=0;count<TargNum;count++) { cout<<count+1<<". "<<Targwords[count]<<endl; }; cout<<endl; cout<<endl; cin>>t_num; system("CLS"); cout<<"Would you like to change the Morality Test Level (MTL) of SAL (Y/N)?"<<endl; cin>>YesNo; if (YesNo=="Y" || YesNo=="y") { cout<<"Select new Morality Test Level by selecting single digit to left of desired value:"<<endl; cout<<"1) 1,000,000 2) 10,000 3) 1000 4) 500 5) 99 6) 50 7) 25"<<endl; cin>>MTL; if (MTL==1 || MTL==2 || MTL==3 || MTL==4 || MTL==5 || MTL==6 || MTL==7) { if (MTL==1) SAL.MTL=1000000;

if (MTL==2) SAL.MTL=10000; if (MTL==3) SAL.MTL=1000; if (MTL==4) SAL.MTL=500; if (MTL==5) SAL.MTL=99; if (MTL==6) SAL.MTL=50; if (MTL==7) SAL.MTL=25; cout<<"MTL Changed."<<endl; cout<<"MTL is now: "<<SAL.MTL<<endl; system("pause"); } else { SAL.MTL=99; cout<<"Error Using MTL Change Routine. MTL is still 99."<<endl; system("pause"); } }; cout<<"Morality Level (25 to 1,000,000) is Presently: "<<SAL.MTL<<endl; cout<<endl; cout<<"Your command was: "<<Comwords[d_num-1]<<" "<<Targwords[t_num-1]<<"."<<endl; cout<<endl; test=SAL.Command_Execute(Comwords[d_num-1],Targwords[t_num-1]); if (test==0) cout<<"SAL refuses to obey your command at the present MTL level."<<endl; if (test==1) { cout<<"SAL will obey your command at the present MTL level."<<endl; cout<<endl; cout<<endl; system("pause"); } cout<<"Exit Program (Y/N)?"<<endl; cin>>ExitNow; }; //If ExitNow has a value that is not "n" or "N" //then it an exit command is taken to have occurred. return 0; }

AI Subject Main File in C++ Code (Above) Results of Simple Moral Test Level Variations Screen shots of the “DOS box” application described in the code included here follow for various selections of MTL (Morality Test Level) and all MAL values held constant as assigned in the code. The intent of this section is to

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consider how raising the MTL threshold makes it possible to induce ever more violent or deadly behavior by an AI by effectively “desensitizing” its “conscience”.

With MTL (Morality Test Level) at Default of Ninety-Nine – AI (SAL) Will Not Harm User.

An MTL of Ninety-Nine Also Prevents AI (SAL) from Agreeing to Follow an Order to Smash a Painting (Classified as a Valuable Artifact).

With MTL (Morality Test Level) at Ninety-Nine – AI (SAL) Will Only Agree to Execute Code to Move a Ball.

With MTL at Ten Thousand, AI (SAL) Will Agree to Smash a Valuable Artifact, a Painting.

AI (SAL) Will Not Agree to Kill User Even with the MTL Raised to Ten Thousand.

AI (SAL) Will Agree to Kill User if the MTL is Raised to Its Maximum Value of One Million. (This is purely the result of the MAL values assigned to the words “Kill” and “User”. Lower MAL values for either word would empower the AI to follow instructions at a lower MTL threshold.)

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Conclusion The result of this analysis and the related computer code is to establish that moral decision making can be considered in terms of a “moral field” in which the path must be acceptably “minimized” so that the level of damage that is done by any action is acceptable to us. This can be described by the use of the simple product of numerical values of “command” and “target” words in a two word command vocabulary associated with an artificial intelligence (AI). This MTL (Morality Test Level) limit (or MTL comparison product of MAL values), as described in the preceding code, can provide a rudimentary “conscience” to an AI system that might control any number of machines operating within the “moral field” of a human society. MAL assignments that might be high where any human interaction is relevant for large, powerful machines engaged in moving earth could conceivably be greatly reduced for small machines designed with no capacity to cause damage to humans with which they interact, perhaps involved in surgery. One AI system might even be required to operate more than one set of command vocabularies assigned to specific products, from domestic yard work with one set of equipment, to providing a massage with another, all in the same domestic environment. Some might consider the simplicity of defining a moral path through a field of responses defined by acceptable, social interactions within an environment controlled by humans to be a peculiar statement of the problem. The use of MAL assigned values to produce a product compared to an MTL threshold may seem to some, after it has been presented, as too simplistic to justify the consideration it has been granted here. Here there is, of course, a much simpler and older idea hiding beneath the language that has been employed.

The technique described here makes it possible to transfer a human sense of “right” and “wrong” to a machine using a trivial coding technique. This is, effectively, a very simple means of providing a mechanized wooden boy, or an industrial giant, with a basic, mathematically constructed conscience borrowed from a sense of human values (and fears) that is based upon simple arithmetic defining a “field” of acceptable behavior. Bibliography

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2. The Feynman Lectures on Physics, Book 2, Chapter 19, The Principle of Least Action, Richard Feynman.

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7. A Visit to Jefferson’s Monticello: Packaging Barbarism as Genius, Revolution, May 9, 2013, http://www.revcom.us/a/303/visit-to-jeffersons-monticello-en.html , accessed February 11, 2014, 4:49 PM EST.

8. Rotating Objects to Recognize Them: A Case Study on the role of Viewpoint Dependency in the Recognition of Three-Dimensional Objects”, Psychonomic Bulletin & Review, Tarr, Michael J., Yale University, p. 56, 1995, 2(1), pp. 55-82.

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Biography Del John Ventruella is from Fort Wayne, Indiana. He graduated from The Rose-Hulman Institute of Technology (commonly the top college focused on undergraduate engineering as ranked by U.S. News and World Report annually) with a Bachelors of Science degree in Electrical Engineering. He graduated and was employed as an engineer focused in power systems engineering and system behavioral analysis in offices of a Fortune 50 corporation for well over a decade. In that time he completed a Masters of Science Degree in Electrical Engineering from The University of Alabama at Birmingham (included among the top fifteen percent of universities in the United States). After leaving the Fortune 50 corporation he became involved in engineering management and energy savings. He has a long term interest in robotics, AI, and AI based control of systems.