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CSC 450 Artificial Intelligence

This textbook covers the essential techniques of AI, including symbolic programming, knowledge representation, search, learning, and planning. Explore the nature of human intelligence and AI definitions. Learn the different systems and milestones in AI development.

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CSC 450 Artificial Intelligence

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  1. 1 CSC 450 Artificial Intelligence

  2. Textbook 2 Artificial Intelligence: A Modern Approach Stuart Russell and Peter Norvig Prentice Hall 2nd Edition 2003

  3. Purpose To learn fundamental techniques of artificial intelligence, including symbolic programming, knowledge representation, search, inference, learning, and planning. 3

  4. Student Responsibilities Attendance is required for this class. Participation of in-class discussions and activities is also required. All submitted assignments must be done by the student. It is a violation of the UT regulations to submit other’s work and the instructor of this course takes the violations very seriously. 4

  5. What is the AI • هو ذلك العلم الذي يحاول محاكاة الذكاء البشري • Science that study the Simulation of the human intelligence! perceiving, thinking, learning and acting. • The attempt to make computers “more intelligent”. • To understand the nature of human intelligence.

  6. What is Artificial Intelligence? A more difficult question is: What is intelligence? The attempt to make computers “more intelligent”. To understand the nature of human intelligence. There are four categories for the definitions of AI based on whether an agent thinks/acts humanly/rationally. 6

  7. Definitions of Artificial Intelligence A general classification of AI systems, due to Russell and Norvig 7

  8. Systems that think like humans AI systems of this type would try to recreate the human mind and its innate cognition mechanisms. This is very difficult, because it requires a thorough understanding of psychology, neurophysiology, and philosophy. Such systems would belong to Cognitive Sciencerather than Artificial Intelligence. 8

  9. Systems that think like humans: Cognitive Modelling 9 • Not content to have a program correctly solving a problem. More concerned with comparing its reasoning steps to traces of human solving the same problem. • Requires testable theories of the workings of the human mind: cognitive science.

  10. Systems that act like humans E. Rich & K. Knight (1991) AI is the study of how to make computers do things which, at the moment, people do better: 10 • A computer can do some things better than a human can

  11. Systems that act like humans 11 • Name some things that a human can do better than a computer. • The ability to: • acquire and learn faster • Make right Decisions • Discover mistakes and correction • Experience transmission • Distinguish between the different kinds of knowledge • The innovations

  12. Systems that act like humans • A computer can do some things better than a human can • Speed in computation • Do repetitions • Information Storage and retrieval • Low cost

  13. Systems that act like humans: The Turing Test • Alan Turing (1912-1954) • “Computing Machinery and Intelligence” (1950) Imitation Game Human Human Interrogator AI System

  14. Systems that act like humans: The Turing Test • 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.

  15. Systems that think rationally E. Charniak & D. McDermott (1985) AI is the study of mental faculties through the use of computational models. Mental faculties (reasoning, learning, perception) are studied more or less as in psychology, except that working with programs is easier and more objective, more measurable. On the other hand, programs require full and explicitly stated knowledge. 15

  16. P. H. Winston (1992) AI is the study of the computations that make it possible to perceive, reason, and act. These are the hallmarks of intelligence, and they can be measured more or less objectively. Systems that think rationally 16

  17. G. F. Luger & W. F. Stubblefield (1993),G. F. Luger (2005) AI is the branch of computer science concerned with the automation of intelligent behavior. This means seeing AI as part of computer science that grows out of the same basic principles. Systems that act rationally 17

  18. Summary 18 • As a summary a simple definition might be as follows: • AI is the Science that study the Simulation of the human intelligence! • الذكاء الإصطناعي هو ذلك العلم الذي يحاول محاكاة الذكاء البشري

  19. Milestones in AI 1923Karel Capek's play "R.U.R." (Rossum's Universal Robots) opens in London (1923). First use of the word 'robot' in English. 1943 Warren McCulloch and Walter Pitts publish "A Logical Calculus of the Ideas Immanent in Nervous Activity" (1943), laying foundations for neural networks. First work generally recognized as AI. Proposed a model of connected artificial neurons, capable of computing any computable function, and capable of learning. Used 3000 vacuum tubes to simulate a network of 40 neurons. See http://en.wikipedia.org/wiki/Warren_McCulloch http://www.csulb.edu/~cwallis/artificialn/warren_mcculloch.html 19

  20. History of AI (2) 1950A. M. Turing published "Computing Machinery and Intelligence" - Introduction of Turing's Test as a way of operationalizing a test of intelligent behavior. Claude Shannon published detailed analysis of chess playing as search. Isaac Asimov published his three laws of robotics 20

  21. History of AI (3) 1956Dartmouth workshop 1956, organized by Alan Turing, John McCarthy, Marvin Minsky, Allen Newell.Considered the birth of AI. John McCarthy proposed the name ARTIFICIAL INTELLIGENCE. Demonstration of Logic Theorist - considered to be the first AI program: a reasoning program to prove theorems. (Newell and Simon) 1958 LISP (LISt Processing language) created by John McCarthy 1963 J. Alan Robinson (now Emeritus Professor at Syracuse University) invented a mechanical proof procedure, the Resolution Method, which allowed programs to work efficiently with formal logic as a representation language. 21 John McCarthy

  22. History of AI (4) 1965Joseph Weizenbaum (MIT) built ELIZA, an interactive program that carries on a dialogue in English on any topic. It was a popular toy at AI centers on the ARPA-net when a version that "simulated" the dialogue of a psychotherapist was programmed. See the paper ELIZA--A Computer Program For the Study of Natural Language Communication Between Man and Machine" ELIZA sites:http://www.parnasse.com/drwww.shtmlhttp://www-ai.ijs.si/eliza/eliza.html 22

  23. History of AI (5) 1966Ross Quillian (PhD dissertation, Carnegie Inst. of Technology; now CMU) demonstrated semantic nets. Negative report on machine translation kills much work in Natural Language Processing (NLP) for many years. Difficulties:- Lack of knowledge about world- The scale problem - programs worked only for "toy" problems. 23

  24. History of AI (7) 1969-1979Rapid development of methods for knowledge representation and knowledge based systems - Alain Colmerauer developed Prolog - a logic programming language- Marvin Minsky invented frames for knowledge representation.- Tom Mitchell, at Stanford, invented the concept of Version Spaces for machine learning.- Drew McDermott and Jon Doyle at MIT, and John McCarthy at Stanford began publishing work on non-monotonic logics and formal aspects of truth maintenance. Benchmark systems: - SHRDLU by Terry Winograd - understanding natural language in the block world - DENDRAL - inferring molecular structure from the information provided by a mass spectrometer. Developed by Edward Feigenbaum MYCIN: Ted Shortliffe's PhD dissertation on MYCIN (Stanford) demonstrated the power of rule-based systems for knowledge representation and inference in the domain of medical diagnosis and therapy. Sometimes called the first expert system. 24

  25. History of AI (8) Early 1980’s R1 - first commercial ES - McDermott 1982, configures orders for new computer systems.First National AAAI conference held (at Stanford).Fifth generation project in Japan - a ten year plan to build intelligent computers running Prolog. Mid 80's Neural Networks become widely used with the Backpropagation algorithm (first described by Werbos in 1974). 25

  26. History of AI (9) 1990's Major advances in all areas of AI, with significant demonstrations in machine learning, intelligent tutoring, case-based reasoning, multi-agent planning, scheduling, uncertain reasoning, data mining, natural language understanding and translation, vision, virtual reality, games, and other topics. Rod Brooks' COG Project at MIT, with numerous collaborators, makes significant progress in building a humanoid robot.First official Robo-Cup soccer match featuring table-top matches with 40 teams of interacting robots and over 5000 spectators. Late 90's Web crawlers and other AI-based information extraction programs become essential in widespread use of the world-wide-web. 26

  27. History of AI (10) 2000 Interactive robot pets (a.k.a. "smart toys") become commercially available, realizing the vision of the 18th cen. novelty toy makers. Cynthia Breazeal at MIT publishes her dissertation on Sociable Machines, describing KISMET, a robot with a face that expresses emotions. The Nomad robot explores remote regions of Antarctica looking for meteorite samples. Today: See AI in the News 27

  28. Major branches of AI Weak AI. The study and design of machines that perform intelligent tasks. Not concerned with how tasks are performed, mostly concerned with performance and efficiency, such as solutions that are reasonable for NP-Complete problems. E.g., to make a flying machine, use logic and physics, don’t mimic a bird. 28

  29. Major branches of AI Strong AI. The study and design of machines that simulate the human mind to perform intelligent tasks. Borrow many ideas from psychology, neuroscience. Goal is to perform tasks the way a human might do them – which makes sense, since we do have models of human thought and problem solving. 29

  30. Important AI Research and Application Areas 30 • Game Playing • Automated Reasoning and Theorem Proving • Expert Systems • Natural Language Understanding and Semantic Modeling • Vision • Planning and Robotics • Languages and Environments for AI • Machine Learning • Alternative Representations: Neural Nets and Genetic Algorithms

  31. Game Playing

  32. Vision • Computer vision:

  33. Robotics • In robotics:

  34. NLP

  35. Face Recognition

  36. Image Processing

  37. Image Processing

  38. Branches of AI 38

  39. AI Programming Languages IPL RITA ROSIE PROLOG LISP 39

  40. Top AI Schools and Companies Top AI Schools Stanford University MIT Carnegie Mellon University (CMU) Berkeley Also Toronto, Washington, Illinois, Texas, Maryland, Edinburgh, UCLA, Karlsruhe, and many others.… Top research labs Microsoft Research (MSR) IBM Research AT&T Labs Xerox PARC, SRI, ATR (Japan), …

  41. Questions What are the goals of the Strong AI and the goals of the Weak AI? Name some specific features of AI problems. Name the basic AI methods. Which year is considered to be the birth-date of AI and why? Open the links to the web pages of the AI scientists and take a look at their work. You should be able to name at least three contemporary scientists and know their major contributions to AI. 41

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