Intelligent Systems
Lection 1
Introduction to AI
What is AI?
• Artificial intelligence ("AI") can mean many
things to many people.
Much confusion arises because the word
'intelligence' is ill-defined.
The phrase is so broad that people have
found it useful to divide AI into two
classes:
strong AI and
weak AI.
What's the difference between
strong AI and weak AI?
• Strong AI makes the bold claim that
computers can be made to think on a level
(at least) equal to humans and possibly
even be conscious of themselves. Weak
AI simply states that some "thinking-like"
features can be added to computers to
make them more useful tools... and this
has already started to happen (witness
expert systems, drive-by-wire cars and
speech recognition software). What does
'think' and 'thinking-like' mean? That's a
matter of much debate.
AI
May be viewed:
• As product
• As branch of science
• As branch of technologies
• As dream
Approaches to tasks of development of AI:
• Utilitarian: goal is creating of practically useful
systems
• “Ideal” or Scientific: goal is creating of perfect
model of human mind
Why human-like AI is needed
• It is needed to create helper who will
understand us as human
• Information technologies grow too fast. So
education is behind. So AI systems must
to able to give birth and teach self-similar
systems, i.e. it is needed evolution of AI
systems
What is AI?
• ``The automation of activities that we associate with human thinking,
activities such as decision-making, problem solving, learning ...'' (Bellman,
1978)
• ``The exciting new effort to make computers think ... machines with minds,
in the full and literal sense'' (Haugeland, 1985)
• ``The study of mental faculties through the use of computational models''
(Charniak and McDermott, 1985)
• ``The art of creating machines that perform functions that require
intelligence when performed by people'' (Kurzweil, 1990)
• ``A field of study that seeks to explain and emulate intelligent behavior in
terms of computational processes'' (Schalkoff, 1990)
• ``The study of how to make computers do things at which, at the moment,
people are better'' (Rich and Knight, 1991)
• ``The study of the computations that make it possible to perceive, reason,
and act'' (Winston, 1992)
• ``The branch of computer science that is concerned with the automation of
intelligent behavior'' (Luger and Stubblefield, 1993)
Main approaches to development
of AI systems
• Logical
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Knowledge engineering
Methods of knowledge representation
Hierarchies
Logical engines
Symbols processing
and so on
• Neural
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Connectionist systems
Adaptive Behavior
Image recognition
Signal processing
and so on
Possible goals to pursue in artificial
intelligence
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Systems that think like humans
Systems that act like humans
Systems that think rationally
Systems that act rationally
AI prehistory
• Philosophy
• Mathematics
• Economics
• Neuroscience
• Psychology
• Computer
engineering
• Control theory
• Linguistics
Logic, methods of reasoning, mind as physical
system foundations of learning, language,
rationality
Formal representation and proof algorithms,
computation, (un)decidability, (in)tractability,
probability
utility, decision theory
physical substrate for mental activity
phenomena of perception and motor control,
experimental techniques
building fast computers
design systems that maximize an objective
function over time
knowledge representation, grammar
Role of information and AI in history
of mankind
Information revolutions:
1. Appearance of speech
2. Invention of writing
3. Invention of book-printing
4. Invention of telecommunication (radio, TV,
phone)Invention of computer
5. Invention of Internet
6. Invention of AI
1. At first a knowledge can directly to supervise of
manufacture (without man)
2. At first a possibility of automatic creating of new
knowledge is appearing
History of AI
40—50th years of XX century:
1943 – McCulloch & Pitts, Boolean circuit model of brain
1948 – N. Viner, “Cybernetics”
1950 - A. Turing, "Computing Machinery and Intelligence", Turing test
≈ 1950 – an idea of K. Shannon to use chess as intellectual task for AI
50th - Samuel's checkers program, Newell & Simon's Logic Theorist,
Gelernter's Geometry Engine, the program for symbol integration of
Slagle
1957 – first learning program of Samuel fir playing in checkers
≈ 1957 – first popular language for AI LISP of McCarthy
50th – programs for proof in logic and geometrics, programs for playing
in different games (chess, checkers, ouths and crosses and so on)
50th – investigations of Rosenblatt in using of perceptrons for image
recognition
Features: solving of task – search in state space, decision trees,
development of heuristics for solving of symbol intelligent tasks
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
• Anticipated all major arguments against AI in following 50 years
• Suggested major components of AI: knowledge, reasoning,
language understanding, learning
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History of AI
60th years of XX century:
Using of first of industrial robots
Investigations of Intelligent robots (cooperation of hand and eye, eye
and moving) – Edinburg University, Stanford University, MIT,
Carnegie Mellon University, Japan
An idea of frames – M. Minsky
1965 - Method of proof in first-order logic – resolution of Robinson
An idea of knowledge representation, in particular, by rules
1965 – book “Principles of neurodynamics” of Rosenblatt, first use of
neural networks (hardware)
1968-1969 – first Expert Systems DENDRAL and MYCIN
1969 – book “Perceptrons” of Minsky, Papert with critics of results of
Rosenblatt – begin of decrease of interest to neural networks
Target setting of understanding of natural language, semantic nets
Fuzzy sets and logic of L. Zadeh
Use of AI in program Apollo (NASA)
Features: separation between knowledge (description ‘how to solve
concrete task’) and algorithms of its processing
First industrial robots
Robot “Shaky”
Thinking rationally: "laws of thought"
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Aristotle: what are correct arguments/thought
processes?
Several Greek schools developed various forms of
logic: notation and rules of derivation for thoughts; may
or may not have proceeded to the idea of
mechanization
Direct line through mathematics and philosophy to
modern AI
Problems:
– Not all intelligent behavior is mediated by logical
deliberation
– What is the purpose of thinking? What thoughts
should I have?
– How to think about picture, sky, trees, loving and so
Thinking humanly: cognitive
modeling
• 1960s "cognitive revolution": information-processing
psychology
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• Requires scientific theories of internal activities of the
brain
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• -- How to validate? Requires
1) Predicting and testing behavior of human subjects (top-down)
or 2) Direct identification from neurological data (bottom-up)
• Both approaches (roughly, Cognitive Science and
Cognitive Neuroscience)
• are now sometimes distinct from AI
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History of AI
70th years of XX century:
Development of expert systems in different areas
1979 – an idea of language PROLOG for logical programming
First attempt of development AI - dialog program Alice
The investigations in semiotics, mathematical linguistics, modal logics
Model ART of Grossberg-Karpenter
An idea of program model of animal of Bongard (USSR)
Theory of functional systems of Anohin (USSR)
The first use of neural networks for mobile robot of N.Amosov (USSR)
Investigations of neural networks in Institute of Cybernetics (Kiev,
USSR)
Investigations of theory of automata, in particular, cooperation of
automatic machines by Ceitlin, Varshavsky (USSR)
Features: boom in logical AI, in particular, development of Expert
Systems, Neural network research almost disappears
Mobile Robot controlled by Neural Network
TAIR
The robot demonstrated purposive movement in natural environment, obstacle
avoidance and similar actions.
TAIR was a three-wheel power barrow equipped with a system of sensors
(rangefinder and tactile sensors).
It was controlled by a hardware-implemented neural network
(the network nodes - special transistor electronic circuits; links between nodes
- resistors).
History of AI
80th years of XX century:
1982 – first popular version of PROLOG of Edinburg University
1982 – publishing of Japan’s program of development of computers of
5th generation based on AI – challenge to scientific world
Begin of program of USA “Strategic Computer Initiative” (in main use of
AI in defense technologies)
Development of methodology and technologies of Expert Systems
First automatic factory and assembling robots
1982 – paper of Hopfield - New boom of development of use of neural
networks
An idea of development of AI (adaptive behavior) in mobile robots or
animates (Brooks, MIT)
Use of LISP as machine-level programming language in graphic
stations and in Autodesc’s AutoCAD
Features: development of different approaches to AI, in particular, begin
of Hybrid AI, AI becomes a industry
History of AI
90th years of XX century – current time:
Development of Hybrid AI
Boom in Intelligent Robotics (in defense technologies, pets and
humanoid robots) (USA, Japan)
Development of distributed AI (XML and Semantic WEB, multi-agent
systems)
Development of human-like AI (common sense, emotions, learning
similar to learning of baby and so on)
Boom of neural networks, in particular, use of its in financial analyzing
Use of natural language in information systems and OS
Features: Use of components of AI in different hardware and software,
investigations in human-like reasoning, learning, interactions and
behavior, concept of agent and multi-agent systems
History of AI (additional bibliography)
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The appendix to Ray Kurzweil's book "Intelligent Machines" (MIT Press, 1990,
ISBN 0-262-11121-7, $39.95) gives a timeline of the history of AI.
Pamela McCorduck, "Machines Who Think", Freeman, San Francisco, CA, 1979.
Allen Newell, "Intellectual Issues in the History of Artificial Intelligence",
Technical Report CMU-CS-82-142, Carnegie Mellon University Computer Science
Department, October 28, 1982.
See also:
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Charniak and McDermott's book "Introduction to Artificial Intelligence", AddisonWesley, 1985 contains a number of historical pointers.
Daniel Crevier, "AI: The Tumultuous History of the Search for Artificial
Intelligence", Basic Books, New York, 1993.
Henry C. Mishkoff, "Understanding Artificial Intelligence", 1st edition, Howard W.
Sams & Co., Indianapolis, IN, 1985, 258 pages, ISBN 0-67227-021-8 $14.95.
Margaret A. Boden, "Artificial Intelligence and Natural Man", 2nd edition, Basic
Books, New York, 1987, 576 pages.
The introductory material in:
Russell, S and Norvig, P, "Artificial Intelligence: A Modern Approach", Prentice
Hall, 1995
is also quite good.
What has AI accomplished?
Turing's claims, but quite a bit of progress
has been made, including Quite a bit, actually. In 'Computing machinery and
intelligence.',
Alan Turing, one of the founders of computer science, made the claim
that by the year 2000, computers would be able to pass the Turing test
at a reasonably sophisticated level, in particular, that the average
interrogator would not be able to identify the computer correctly more
than 70 per cent of the time after a five minute conversation. AI
hasn't quite lived upto:
• Deployed speech dialog systems by firms like IBM, Dragon and Lernout&Hauspie
• Financial software, which is used by banks to scan credit card transactions for
unusual patterns that might signal fraud. One piece of software is estimated to save
banks $500 million annually.
• Applications of expert systems/case-based reasoning: a computerized Leukemia
diagnosis system did a better job checking for blood disorders than human experts.
• Machine translation for Environment Canada: software developed in the 1970s
translated natural language weather forecasts between English and French.
Purportedly still in use.
• Deep Blue, the first computer to beat the human chess Grandmaster
• Physical design analysis programs, such as for buildings and highways.
• Fuzzy controllers in dishwashers, etc.
• Robots-cleaner
What are the branches of AI?
There are many, some are 'problems' and some are 'techniques‘
• Automatic Programming - The task of describing what a program should do and having the
AI system 'write' the program
• Bayesian Networks - A technique of structuring and inferencing with probabilistic
information. (Part of the "machine learning"
problem).
• Constraint Satisfaction - solving NP-complete problems, using variety of techniques.
• Knowledge Engineering/Representation - turning what we know about a particular domain
into a form in which a computer can understand it.
• Machine Learning - Programs that learn from experience or data.
• Natural Language Processing (NLP) - Processing and (perhaps) understanding human
("natural") language. Also known as computational linguistics.
• Neural Networks (NN) - The study of programs that function in a manner similar to how
animal brains do.
• Planning - given a set of actions, a goal state, and a present state, decide which actions must
be taken so that the present state is turned into the goal state
• Robotics - The intersection of AI and robotics, this field tries to get (usually mobile) robots to
act intelligently.
• Speech Recognition - Conversion of speech into text.
• Search - The finding of a path from a start state to a goal state. Similar to planning, yet
different...
• Visual Pattern Recognition - The ability to reproduce the human sense of sight on a machine.
What's an agent?
A very misused term. Today, an agent seems to mean a
stand-alone piece of AI-ish software that scours across
the internet doing something "intelligent." Russell and
Norvig define it as "anything that can be viewed a
perceiving its environment through sensors and acting
upon that environment through effectors." Several
papers I've read treat it as 'any program that operates on
behalf of a human,' similar to its use in the phrase 'travel
agent'. Marvin Minsky has yet another definition in the
book "Society of Mind."
Minsky's hypothesis is that a large number of seeminglymindless agents can work together in a society to create
an intelligent society of mind. Minsky theorizes that not
only will this be the basis of computer intelligence, but it
is also an explanation of how human intelligence
works. Andrew Moore at Carnegie Mellon University
once remarked that "The only proper use of the word
'agent' is when preceded by the words 'travel', 'secret', or
'double'."
Most used programming languages
for AI
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LISP
PROLOG
C/C++
Java
Python
Delphi