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CS 8520: Artificial Intelligence

CS 8520: Artificial Intelligence. Introduction Paula Matuszek Fall, 2005. AI Course Details. Instructor: Paula Matuszek Paula.A.Matuszek@GSK.com 610-270-6851 Course web page There will be one. Still working on where, exactly. Syllabus, Requirements Handing in Homework

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CS 8520: Artificial Intelligence

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  1. CS 8520: Artificial Intelligence Introduction Paula Matuszek Fall, 2005 CSC 8520 Fall, 2005. Paula Matuszek

  2. AI Course Details • Instructor: • Paula Matuszek • Paula.A.Matuszek@GSK.com • 610-270-6851 • Course web page • There will be one. Still working on where, exactly. • Syllabus, Requirements • Handing in Homework • Academic Integrity • Required and recommended texts • Questions? • Student questionnaire CSC 8520 Fall, 2005. Paula Matuszek

  3. Our Approach • Following the book, mostly: • Tools and techniques (through chapter 10) • Some of the domains, depending on interest • Working in the lab: • We will spend some part of most classes doing hands-on stuff. Trying out tools and applications, exploring what's out there, etc. • AI is also FUN, exciting, always new. I hope to convey some of why. • We will all get more out of this class if you speak up. I encourage questions and ideas and discussion in class. CSC 8520 Fall, 2005. Paula Matuszek

  4. Class Background • In order to help structure and focus the course, we need to have an idea of the interests and backgrounds of the members of the class. • Name • Something about your background • Something about why you're interested in AI • Something about what you hope to get from this class CSC 8520 Fall, 2005. Paula Matuszek

  5. Resource • We will add to the class web page lists of interesting resources. Two major sources you should be aware of: • Our textbook is in extensive use, and there is a web page with many resources and links at aima.cs.berkeley.edu • The American Association for Artificial Intelligence is the primary professional organization in the US for AI. Their web page at www.aaai.org has many resources. CSC 8520 Fall, 2005. Paula Matuszek

  6. Most of the remaining slides of this presentation are modified from those of Professor Maria DesJardins, University of Maryland Baltimore County. The originals can be found at http://www.cs.umbc.edu/671/fall01/schedule.html CSC 8520 Fall, 2005. Paula Matuszek

  7. What is AI? • There are no crisp definitions Q. What is artificial intelligence? A. It is the science and engineering of making intelligent machines, especially intelligent computer programs. It is related to the similar task of using computers to understand human intelligence, but AI does not have to confine itself to methods that are biologically observable. (John McCarthy, 1956. http://www.formal.Stanford.EDU/jmc/whatisai ) Q. Yes, but what is intelligence? A. Intelligence is the computational part of the ability to achieve goals in the world. Varying kinds and degrees of intelligence occur in people, many animals and some machines. CSC 8520 Fall, 2005. Paula Matuszek Based on http://www.cs.umbc.edu/671/Fall01/.

  8. Other possible AI definitions • AI is a collection of hard problems which can be solved by humans and other living things, but for which we don’t have good algorithmic solutions • e.g., understanding spoken natural language, medical diagnosis, circuit design, etc. • AI Problem + Sound theory = Engineering problem • Many problems used to be thought of as AI but are now considered not • e.g., compiling Fortran in 1955, symbolic mathematics in 1965, image cleanup, Optical character recognition. CSC 8520 Fall, 2005. Paula Matuszek Based on http://www.cs.umbc.edu/671/Fall01/.

  9. Ways to Examine the field of AI • The field of can generally be viewed from two directions: • The techniques you use • Search • Knowledge Representation • Inference • Logic • The areas you're working in • Planning • Learning • Natural Language Understanding • Games • Etc. Etc. Etc. CSC 8520 Fall, 2005. Paula Matuszek

  10. What’s easy and what’s hard? • Easier: many of the high level tasks we usually associate with “intelligence” in people • e.g., Symbolic integration, proving theorems, playing chess, medical diagnosis, etc. • Harder: tasks that lots of animals can do • walking around without running into things • catching prey and avoiding predators • interpreting complex sensory information • modeling the internal states of other animals from their behavior • working as a team (e.g. with pack animals) • What's the difference? CSC 8520 Fall, 2005. Paula Matuszek Based on http://www.cs.umbc.edu/671/Fall01/.

  11. History CSC 8520 Fall, 2005. Paula Matuszek Based on http://www.cs.umbc.edu/671/Fall01/.

  12. Current State • Is AI a failure? Is AI dead? • NO. AI is • pervasive • invisible • There are no solved problems in AI. Why? Once they're solved they aren't AI any more. CSC 8520 Fall, 2005. Paula Matuszek Based on http://www.cs.umbc.edu/671/Fall01/.

  13. Foundations of AI Computer Science & Engineering Mathematics Philosophy AI Biology Economics Psychology Linguistics Cognitive Science CSC 8520 Fall, 2005. Paula Matuszek Based on http://www.cs.umbc.edu/671/Fall01/.

  14. Why AI? • Engineering: To get machines to do a wider variety of useful things • e.g., understand spoken natural language, recognize individual people in visual scenes, find the best travel plan for your vacation, etc. • Cognitive Science: As a way to understand how natural minds and mental phenomena work • e.g., visual perception, memory, learning, language, etc. • Philosophy: As a way to explore some basic and interesting (and important) philosophical questions • e.g., the mind body problem, what is consciousness, etc. CSC 8520 Fall, 2005. Paula Matuszek

  15. Like humans Well Rational agents Think GPS Heuristic systems Act Eliza Possible Approaches AI tends to work mostly in this area CSC 8520 Fall, 2005. Paula Matuszek Based on http://www.cs.umbc.edu/671/Fall01/.

  16. Like humans Well Think Rational agents GPS Heuristic systems Act Eliza Think well • Develop formal models of knowledge representation, reasoning, learning, memory, problem solving, that can be rendered in algorithms. • There is often an emphasis on systems that are provably correct, and guarantee finding an optimal solution. CSC 8520 Fall, 2005. Paula Matuszek Based on http://www.cs.umbc.edu/671/Fall01/.

  17. Like humans Well Think Rational agents GPS Heuristic systems Act Eliza Act well • For a given set of inputs, generate an appropriate output that is not necessarily correct but gets the job done. • A heuristic (heuristic rule, heuristic • method) is a rule of thumb, strategy, trick, • simplification, or any other kind of device • which drastically limits search for solutions • in large problem spaces. • Heuristics do not guarantee optimal solutions; in fact, they do not guarantee any solution at all: all that can be said for a useful heuristic is that it offers solutions which are good enough most of the time.–Feigenbaum and Feldman, 1963, p. 6 CSC 8520 Fall, 2005. Paula Matuszek Based on http://www.cs.umbc.edu/671/Fall01/.

  18. Like humans Well Think Rational agents GPS Heuristic systems Act Eliza Think like humans • Cognitive science approach • Focus not just on behavior and I/O but also look at reasoning process. • Computational model should reflect "how" results were obtained. • Provide a new language for expressing cognitive theories and new mechanisms for evaluating them • GPS (General Problem Solver): Goal not just to produce humanlike behavior (like ELIZA), but to produce a sequence of steps of the reasoning process that was similar to the steps followed by a person in solving the same task. CSC 8520 Fall, 2005. Paula Matuszek Based on http://www.cs.umbc.edu/671/Fall01/.

  19. Like humans Well Think Rational agents GPS Heuristic systems Act Eliza Act like humans • Behaviorist approach. • Not interested in how you get • results, just the similarity to what • human results are. • Exemplified by the Turing Test (Alan Turing, 1950). CSC 8520 Fall, 2005. Paula Matuszek Based on http://www.cs.umbc.edu/671/Fall01/.

  20. Turing Test • Three rooms contain a person, a computer, and an interrogator • The interrogator can communicate with the other two by teleprinter. • The interrogator tries to determine which is the person and which is the machine. • The machine tries to fool the interrogator into believing that it is the person. • If the machine succeeds, then we conclude that the machine can think. CSC 8520 Fall, 2005. Paula Matuszek Based on http://www.cs.umbc.edu/671/Fall01/.

  21. Some Turing Test - like Programs • Over the course of the last 60 years there have been a number of programs which might be said to have at least tried the Turing Test, although not always with that goal. CSC 8520 Fall, 2005. Paula Matuszek

  22. Eliza • ELIZA: A program that simulated a psychotherapist interacting with a patient and successfully "passed" the Turing Test. • Coded at MIT during 1964-1966 by Joel Weizenbaum. • First script was DOCTOR. • The script was a simple collection of syntactic patterns not unlike regular expressions • Each pattern had associated reply including bits of the input (after simple transformations (my -> your) • Weizenbaum was shocked at reactions: • Psychiatrists thought it had potential. • People unequivocally anthropomorphised. • Many thought it solved the NL problem. CSC 8520 Fall, 2005. Paula Matuszek

  23. Eliza • This is a transcript of a conversation with an Eliza-like program found in Emacs. Try typing Meta-x doctor in your favorite Emacs. • I am the psychotherapist. Please, describe your problems. • I am angry. • Are you angry often? • No. • Why not? • Usually I am even-tempered. • Why do you say usually you are even tempered? • I do not get angry usually. • What causes you to be angry? • My mother. • Tell me something about your family. • My mother is a busybody. • You seem to dwell on your family. CSC 8520 Fall, 2005. Paula Matuszek

  24. H: Good afternoon. My name is Doctor Jones. P: Pleased to meet you. H: How are you? P: All right I guess. H: Could you tell me your name? P: Pat Smith. H: I though you might have some problems. P: I've been upset lately. H: By what? P: People get on my nerves sometimes. H: Tell me more. P: Do you know anything about bookies? ... Colby’s PARRY • Kenneth Colby modeled a paranoid using the same techniques circa 1968. • PARRY has basic emotions. If it gets angry, its replies become more hostile. • In a Turing test, expert psychiatrists were unable to distinguish PARRY’s ramblings from those of real paranoids. CSC 8520 Fall, 2005. Paula Matuszek Based on http://www.cs.umbc.edu/671/Fall01/.

  25. The Loebner Contest • A modern version of the Turing Test, held annually, with a $100,000 cash prize. • http://www.loebner.net/Prizef/loebner-prize.html • Restricted topic (removed in 1995) and limited time. • Participants include a set of humans and a set of computers and a set of judges. • Scoring • Rank from least human to most human. • Highest median rank wins $2000. ($3000 in 2005) • If better than a human, win $100,000. (Nobody yet…) • The 2004 winner, Alice, is a chatbot. Try it at http://www.alicebot.org/ CSC 8520 Fall, 2005. Paula Matuszek Based on http://www.cs.umbc.edu/671/Fall01/.

  26. So when WILL we decide that computers are intelligent? CSC 8520 Fall, 2005. Paula Matuszek Based on http://www.cs.umbc.edu/671/Fall01/.

  27. How Do We Know When We're There? • Some requirements I think any test we use must meet: • Whatever test we use must not exclude the majority of adult humans. I can't play chess at a grand master level! • Whatever test we use must produce an observable or testable result. "Isn't intelligent because it doesn't have a mind" is perhaps a topic for interesting philosophical debate, but it's not of any practical help. • AI from a computer scientist perspective! Not the Chinese Room CSC 8520 Fall, 2005. Paula Matuszek

  28. What can AI systems do In the meantime, AI can be an effective tool. Here are some example applications of current AI capabilities: • Computer vision: face recognition from a large set • Robotics: autonomous (mostly) car • Natural language processing: simple machine translation • Expert systems: medical diagnosis in a narrow domain • Spoken language systems: ~1000 word continuous speech • Planning and scheduling: Hubble Telescope experiments • Learning: text categorization into ~1000 topics • User modeling: Bayesian reasoning in Windows help • Games: Grand Master level in chess (world champion), checkers, etc. CSC 8520 Fall, 2005. Paula Matuszek Based on http://www.cs.umbc.edu/671/Fall01/.

  29. What can’t AI systems do yet? • Understand natural language robustly (e.g., read and understand articles in a newspaper) • Surf the web • Interpret an arbitrary visual scene • Learn a natural language • Play Go well • Construct plans in dynamic real-time domains • Refocus attention in complex environments • Perform life-long learning CSC 8520 Fall, 2005. Paula Matuszek Based on http://www.cs.umbc.edu/671/Fall01/.

  30. What's Happening Now in AI? • Homework assignment will explore some of the things now going on in AI • A useful resource in current AI news is http://www.aaai.org/AITopics/newstopics/main.html CSC 8520 Fall, 2005. Paula Matuszek

  31. First Homework Assignment • From the textbook: Answer questions 1.2 and 1.7. Look at the other questions and think about them; you might find it interesting to make note of your thoughts and read them again at the end of the course. For question 1.2, you can find a copy of Turing's paper at http://www.abelard.org/turpap/turpap.htm. • Skim through your textbook, including the detailed contents list. Choose two chapters from chapters 11-27 that you are most interested in seeing us cover in class. Due: 5PM, Sept 8. Remember to email to Paula.Matuszek@villanova.edu • Academic Integrity revisited. CSC 8520 Fall, 2005. Paula Matuszek

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