Intelligent Systems Lection 1 Introduction to AI

1. Intelligent SystemsLection 1Introduction to AI

2. 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.

3. 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.

4. 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

5. 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

6. 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)

7. Main approaches to development of AI systems • Logical • Knowledge engineering • Methods of knowledge representation • Hierarchies • Logical engines • Symbols processing • and so on • Neural • Connectionist systems • Adaptive Behavior • Image recognition • Signal processing • and so on

8. Possible goals to pursue in artificial intelligence • Systems that think like humans • Systems that act like humans • Systems that think rationally • Systems that act rationally

9. AI prehistory • Philosophy Logic, methods of reasoning, mind as physical system foundations of learning, language, rationality • Mathematics Formal representation and proof algorithms, computation, (un)decidability, (in)tractability, probability • Economics utility, decision theory • Neuroscience physical substrate for mental activity • Psychology phenomena of perception and motor control, experimental techniques • Computer building fast computers engineering • Control theory design systems that maximize an objective function over time • Linguistics knowledge representation, grammar

10. Information revolutions: Appearance of speech Invention of writing Invention of book-printing Invention of telecommunication (radio, TV, phone)Invention of computer Invention of Internet Invention of AI At first a knowledge can directly to supervise of manufacture (without man) At first a possibility of automatic creating of new knowledge is appearing Role of information and AI in history of mankind

11. 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

12. 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

13. 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

14. First industrial robots

15. Robot “Shaky”

16. Thinking rationally: "laws of thought" • 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 on?

17. Thinking humanly: cognitive modeling • 1960s "cognitive revolution": information-processing psychology • Requires scientific theories of internal activities of the brain • -- 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

18. 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

19. 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).

20. 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

21. 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

22. History of AI (additional bibliography) • 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: • Charniak and McDermott's book "Introduction to Artificial Intelligence", Addison-Wesley, 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.

23. What has AI accomplished? Turing's claims, but quite a bit of progresshas been made, including Quite a bit, actually.  In 'Computing machinery and intelligence.',Alan Turing, one of the founders of computer science, made the claimthat by the year 2000, computers would be able to pass the Turing testat a reasonably sophisticated level, in particular, that the averageinterrogator would not be able to identify the computer correctly morethan 70 per cent of the time after a five minute conversation.  AIhasn'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

24. 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.

25. 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 seemingly-mindless 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'."

26. Most used programming languages for AI • LISP • PROLOG • C/C++ • Java • Python • Delphi