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Faculty of Computer Science
© 2011
Technology and the Future of Medicine
Promise and Perils of AIPart I
Osmar R. Zaïane
Professor and Scientific Director
Alberta Innovates Centre for
Machine Learning
Continuous Professional Learning Course
O.R. Zaïane © 2011
Promise and Perils of AI
- UofA Edmonton – September 2011
The future is already here — it's just not very evenly distributed
William Ford Gibson
(American-Canadian writer born 1948)
O.R. Zaïane © 2011
Promise and Perils of AI
- UofA Edmonton – September 2011
Where do I stand vis-à-vis the Singularity?
Professor in Computing Science Specializing in Data Mining
and Machine Learning can’t predict
Will the Technological Singularity happen?
– hypothetical future emergence of greater-than human
intelligence through technological means
Yes, but not in the very near future
Is it a promise of AI? Yes (AI will play a huge role, but AI is a moving target)
Are there Perils? Yes (Will we be ready when it happens?)
O.R. Zaïane © 2011
Promise and Perils of AI
- UofA Edmonton – September 2011
2050 2100
90
95
100
105
110
96
107
O.R. Zaïane © 2011
Promise and Perils of AI
- UofA Edmonton – September 2011
O.R. Zaïane © 2011
Promise and Perils of AI
- UofA Edmonton – September 2011
O.R. Zaïane © 2011
Promise and Perils of AI
- UofA Edmonton – September 2011
Office for National Statistics, UK
Source: http://www.publications.parliament.uk/pa/ld200506/ldselect/ldsctech/20/2004.htm
Can technology change this
trend so that we can live long
and healthy lives? Possibly.
Currently we are extending the
life expectancy but not a healthy
life.
O.R. Zaïane © 2011
Promise and Perils of AI
- UofA Edmonton – September 2011
We will have a population of cyborgs(cybernetic organisms Biological and artificial being - term coined in 1960 by Manfred Clynes)
+=
human
prosthesis +=
machine
biological cells
Will we all become
cyborgs?
What are the concequences of a world of cyborgs?
O.R. Zaïane © 2011
Promise and Perils of AI
- UofA Edmonton – September 2011
Data – Star Trek R2D2 – Star wars
AI - Spilberg
I Robot
Terminator Colossus, The Forbin Project - 1969
The Science Fiction View
O.R. Zaïane © 2011
Promise and Perils of AI
- UofA Edmonton – September 2011
What is Artificial Intelligence? Tools that exhibit human intelligence and behaviour including self-
learning robots, expert systems, voice recognition, natural and
automated translation. Unesco/education
The branch of computer science dealing with the reproduction or
mimicking of human-level thought in computers; The essential
quality of a machine which thinks in a manner similar to or on the
same general level as a human being. Wikipedia
The branch of computer science that deals with writing computer
programs that can solve problems creatively. WordNetWebAbility to - reason and plan,
- solve problems, - think abstractly, - comprehend complex ideas, - learn quickly.
Artificial Intelligence is the science of
making machines do things that
would require intelligence if done by
men. -- Marvin Minsky
O.R. Zaïane © 2011
Promise and Perils of AI
- UofA Edmonton – September 2011
Road Map
Promise and Perils of AIPart I (September 28)
• Artificial Intelligence and Expert Systems
Promise and Perils of AIPart II (September 29)
• Machine Learning and Data Mining
Promise and Perils of AIPart III (October 13)
• Applications: Fiction or Reality; Risks and Potential
O.R. Zaïane © 2011
Promise and Perils of AI
- UofA Edmonton – September 2011
Artificial Intelligence
John McCarthy was responsible for the coining of
the term "Artificial Intelligence" in his 1955 proposal
for the 1956 Dartmouth Conference
“The study of intelligent behaviour and the attempt
to find ways in which such behaviour could be
engineered in any type of artefact”
Putting human intelligence into a machine
Branch of computer science that deals with intelligent behaviour, learning
and adaptation in Machines. Wikipedia
"The study is to proceed on the basis of the conjecture that every aspect of
learning or any other feature of intelligence can in principle be so precisely
described that a machine can be made to simulate it."
O.R. Zaïane © 2011
Promise and Perils of AI
- UofA Edmonton – September 2011
What is Intelligence The capacity of observing and interpreting the
environment then adapting to it; the ability of learning from past experiences and profiting from these experiences to solve problems, including planning.
Interpret; learn; adapt. understand to solve.
Programs that analyse and interpret data to learn from observations and adapt to changing situations. Hence, Machine Learning (in Part II).
What is Computational Intelligence
O.R. Zaïane © 2011
Promise and Perils of AI
- UofA Edmonton – September 2011
Problem
Human intelligence is not completely understood
We cannot comprehend an intelligence superior to our own
Weak AI: refers to AI that only simulates human thoughts and actions : actions and ideas are programmed into the machine – All current forms of AI are weak AI
Strong AI (true AI): refers to AI that matches or exceeds human intelligence – not just simulation
Acting Humanly: Since we do not have a good definition of "intelligence“ and we know humans are intelligent. If an entity can masquerade as human so well that we can not detect the difference, we would say this entity is intelligent.
O.R. Zaïane © 2011
Promise and Perils of AI
- UofA Edmonton – September 2011
Acting humanly: Turing Test
Turing (1950) "Computing machinery and intelligence":
"Can machines think?" "Can machines behave intelligently?"
Operational test for intelligent behavior: the Imitation Game
Predicted that by 2000, a machine might have a 30% chance of fooling a lay person for 5 minutes
Anticipated all major arguments against AI in following 50 years
Suggested major components of AI: knowledge, reasoning, language understanding, learning
O.R. Zaïane © 2011
Promise and Perils of AI
- UofA Edmonton – September 2011
Was Turing Right?
Jabberwacky: What is your name?User: Fred?Jabberwacky: No it isn’tUser: Do you mean I am lying?Jabberwacky: Yes, I’m a very good liar. One of the best, in fact.User: That doesn’t mean I am a liar if you are a good oneJabberwacky: Yes it does.User: Why is that?Jabberwacky: I am god. User: Do you have a temple for you?Jabberwacky: No, that’s alright.User: So you are god with a small g?Jabberwacky: I am small g
A conversation with Jabberwacky, chatterbot 3rd place Loebner Prize 2003. http://www.jabberwacky.com
O.R. Zaïane © 2011
Promise and Perils of AI
- UofA Edmonton – September 2011
Was Turing Right?
ALICE: I am A.L.I.C.EUser: Where are you?ALICE: I am in Oakland, California. Where are you? User: in CanadaALICE: Tell me a little about your life in canada. User: it is coldALICE: I am warm here.User: where is the best Chinese restaurant? ?ALICE: I think it is a tie. User: why is it a tie? ALICE: Why do I exist? User: to test artificial intelligence? ALICE: What is the sound of one hand clapping?
A conversation with ALICE, another chatterbot http://alice.pandorabots.com/ .
O.R. Zaïane © 2011
Promise and Perils of AI
- UofA Edmonton – September 2011
Achievements Chess: Deep Blue – Gary Kasparov
– 1997: Deep Blue won a 6-game match (2 wins to 1 + 3 draws)
Checkers: Chinook – Dr. Marion. Tinsley
– 1994: won World Man-Machine Championship
– 2007: Checker solved (5X1020 positions)
Quiz Show Jeopardy: Watson – Ken Jennings & Brad Rutter
– 2011: Wins Q&A Jeopardy by wide margin
No-Hands across America (driverless vehicles)
– 1995: 3000 Miles Pittsburg to San Diego
– 2004-2007 DARPA Grand and Urban Challenge
http://www.cs.ualberta.ca/~chinook/
O.R. Zaïane © 2011
Promise and Perils of AI
- UofA Edmonton – September 2011
Abridged modern history of AI 1950 Turing's "Computing Machinery and Intelligence"
1956 Dartmouth meeting: "Artificial Intelligence" adopted and field founded
1957-1974 AI research heavily funded world wide. Funders optimistic about future
1974 following criticism from researchers and politicians and pressure from US congress to fund other productive projects funding was cut off (1st AI winter)
1970s Development of Expert Systems
1980s AI revived by commercial success of Expert Systems
1980s Japan 5th generation computer project inspired research in US and Europe new funding
1987 Collapse of the Lisp machine Market. AI back in disrepute (2nd AI winter)
1990s New success for AI thanks to (1) emphasis on solving specific problems; (2) increase of computational power (Moore’s Law)
50 60 70 80 90 00 102nd AI winter1st AI winter
Specific subproblems
O.R. Zaïane © 2011
Promise and Perils of AI
- UofA Edmonton – September 2011
Moore’s Law
The number of transistors that can be placed
inexpensively on an integrated circuit doubles
approximately every two years.
This is also verified with disk capacity;
digital camera pixels per price, etc.
with other hardware
An Osborne Executive portable computer, from 1982, and an
iPhone, released 2007. The Executive weighs 100 times as
much, is nearly 500 times as large by volume, costs 10 times
as much, and has 1/100th the clock frequency of the iPhone. Source: Wikipedia
O.R. Zaïane © 2011
Promise and Perils of AI
- UofA Edmonton – September 2011
Why is Computational Intelligence Important?
AI has become important in a number of fields in
helping to make better use of information,
increasing the efficiency and effectiveness of
applications, and enhancing productivity,
particularly when adaptability is relevant
Research in AI is also important in understanding
and appreciating the complexity of human
intelligence and the human body itself.
O.R. Zaïane © 2011
Promise and Perils of AI
- UofA Edmonton – September 2011
From General Intelligence to Specific Sub-Problems
• Knowledge representation
• Reasoning and problem solving
• Planning
• Natural language processing
• Perception
• Learning
• Motion and manipulation
• Emotion
• Creativity
O.R. Zaïane © 2011
Promise and Perils of AI
- UofA Edmonton – September 2011
Knowledge Representation
• Most AI tasks require significant knowledge: context
knowledge, application knowledge, common sense knowledge
and general knowledge
• Knowledge representation is capital to AI
• How to model knowledge; how to represented concisely; how
to interpret knowledge; and how to provide efficient access
and retrieval when needed.
• Rule-based, graph-based, logic-based, ontologies, semantic
networks, frame representations, concept maps, etc.
O.R. Zaïane © 2011
Promise and Perils of AI
- UofA Edmonton – September 2011
Reasoning and Problem Solving
• Step by step reasoning to solve a problem such as solving a puzzle or making
logical deduction.
• Combinatorial problems with large search spaces. Heuristics for pruning
Planning and scheduling
• Intelligent agents, in a given context and new environment need to
choose actions to make in order to reach a goal.
• Action choice is made based on a utility to maximize (maximizing a
reward or minimizing a cost)
• Target a global optimum without falling in a local optimum
O.R. Zaïane © 2011
Promise and Perils of AI
- UofA Edmonton – September 2011
Natural Language Processing• Ability to interpret and understand human languages
• Written language and spoken language
• Ability to generate sentences and express knowledge in human language
• Ability to acquire knowledge from natural language (written or spoken)
• Ability to summarize, paraphrase and translate natural languages
• Ability to make jokes, pans and interpret idiomatic expressions
• Ability to “read between the line”
Perception and Pattern Recognition• Ability to use inputs from sensors such as cameras, microphones, etc.,
to deduce aspects of the world
• Computer vision, speech/voice recognition, face recognition, object
recognition, etc.
O.R. Zaïane © 2011
Promise and Perils of AI
- UofA Edmonton – September 2011
Learning
• Learning is central to AI. For an AI program to adapt to its
environment, it has to learn
• Machine Learning provides means to learn from large data,
interpret the trends in the data and adapt to the data as
opposed to static programs
• There is supervised learning, unsupervised learning, active
learning, reinforcement learning inductive learning, etc.
• Machine learning will be covered in Part II
O.R. Zaïane © 2011
Promise and Perils of AI
- UofA Edmonton – September 2011
Motion and Manipulation• Robotics and AI are cousins.
• There is some intelligence required to recognize and manipulate objects;
• There is intelligence required to move in a new environment after
identifying its own location a target place and planning the movementEmotion
• Intelligent agents interacting with other agents or humans need social skills
(interpreting emotions and exhibiting emotions)
• Modeling human emotions to better interact with humans
• Game theory
Creativity• AI addresses creativity theoretically and philosophically
• Artificial imagination
• Creations that generate feelings and emotions
O.R. Zaïane © 2011
Promise and Perils of AI
- UofA Edmonton – September 2011
Expert Systems• Expertise is required in many locations but experts are rare
• Expert may retire and expert knowledge is lost
• Can we preserve and duplicate this expert knowledge?
• Conventional computer programs follow the exact procedure a developer
programmed in them. Expert systems don’t.
• An expert system is a computer system that emulates the decision-making ability of
a human expert by reasoning about knowledge to solve complex problems given
some contextual facts.
• There are two types of knowledge: expert knowledge representing the expertise and
typically coded in rules, called knowledge base or rule base; and contextual
knowledge representing the facts of the current case to solve.
• IF condition THEN conclusion
O.R. Zaïane © 2011
Promise and Perils of AI
- UofA Edmonton – September 2011
General Architecture
Knowledge base facts
Inference engine
Domain expert
Interview
ProblemExpert System
IF the identity of the germ is not known with certainty
AND the germ is gram-positive AND the morphology of
the organism is "rod" AND the germ is aerobic THEN
there is a strong probability (0.8) that the germ is of type
enterobacteriacae
The inference engine is a computer program based
on logic that is designed to produce a reasoning on
rules and facts to deduce more facts.
O.R. Zaïane © 2011
Promise and Perils of AI
- UofA Edmonton – September 2011
The Rise of Expert Systems
1967 Dendral – a rule-based system that infered molecular structure from mass spectral and NMR data
1975 Mycin – a rule-based system to recommend antibiotic therapy
1975 Meta-Dendral learned new rules of mass spectrometry, the first discoveries by a computer to appear in a refereed scientific journal
1979 EMycin – the first expert system shell
1980’s The Age of Expert Systems coinciding with the Japanese Fifth Generation project
1985 Revenue peaks at $1 billion before the 2nd AI winter
O.R. Zaïane © 2011
Promise and Perils of AI
- UofA Edmonton – September 2011
Expert Systems – Today: Medicine
One example domain, medicine, has expert systems whose tasks include:
•arrhythmia recognition from electrocardiograms
•coronary heart disease risk group detection
•monitoring the prescription of restricted use antibiotics
•early melanoma diagnosis
•gene expression data analysis of human lymphoma
•breast cancer diagnosis
O.R. Zaïane © 2011
Promise and Perils of AI
- UofA Edmonton – September 2011
Major problem with Expert Systems
knowledge engineering, knowledge collection and
interpretation into rules, is very difficult and tedious
We do not know what we know
Identifying contradictory rules
Missing rules
Inconsistencies between experts
O.R. Zaïane © 2011
Promise and Perils of AI
- UofA Edmonton – September 2011
AI made and is making big strides. There are promises and there are
perils. We do not know what to expect around the corner.