CSC 8520: Artificial Intelligence Course Details • Paula Matuszek, Robin McEntire • Student Questionnaire • Snow – Contact sheet • Course web page – Home page, www.csc.vill.edu/~matuszek/spring2004 – Syllabus – Handing in Homework – Academic Integrity • Questions? Intelligent Systems Lab • Mendel 156 • We will meet in here, and use the lab during class • Your badge should also open the door, and you can work in here. (It may take a while to get this all working) – No other classes – Some other students – Files and settings will NOT remain • PCs have Windows XP, Lisp. Will have Prolog and some other tools we will use Musings on AI • AI to me is a lot of things. • The field can generally be viewed from two directions: – The areas you're working in • Planning • Learning • Natural Language Understanding • Games – The techniques you use • Search • Knowledge Representation • Inference • Logic Our Approach • Following the book: – Tools and techniques – 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. 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. 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. • 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/. Introduction to Artificial Intelligence Chapter 1 Big Questions • Can machines think? • And if so, how? • And if not, why not? • And what does this say about human beings? • And what does this say about the mind? What is AI? • There are no crisp definitions • Here’s one from John McCarthy, (He coined the phrase AI in 1956) - see http://www.formal.Stanford.EDU/jmc/whatisai/) 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. 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. 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 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? History 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. Foundations of AI Computer Science & Engineering Mathematics Philosophy Economics AI Biology Psychology Cognitive Linguistics Science 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. Possible Approaches Like humans Well GPS Rational Think agents AI tends to work mostly in this area Heuristic Act Eliza systems Like Think well humans Well • Develop formal models of Think Rational GPS agents knowledge representation, Eliza Heuristic Act systems reasoning, learning, memory, problem solving, that can be rendered in algorithms. • There is often an emphasis on a systems that are provably correct, and guarantee finding an optimal solution. Like Act well humans Well Think Rational GPS agents • For a given set of inputs, generate an Act Eliza Heuristic systems 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 Like Think like humans humans Well Think Rational GPS • Cognitive science approach agents • Focus not just on behavior and I/O Act Eliza Heuristic systems 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. Like Act like humans humans Well Think Rational GPS agents Heuristic Act Eliza systems • 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). 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. 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. • I am the psychotherapist. Please, describe your problems. Each time you are finished talking, type RET twice. Eliza • i'm having a hard time with my students • Why do you say that? • This is a • well, they keep asking me for money transcript of a • Maybe your plans have something to do with this. conversation • what plans? • Is it because of your life that you say what plans? with an Eliza- • No, it's because I didn't know what you were talking about like program • Is it because you didn't know what I was talking about that you found in came to me? Emacs (Gnu • no, it's because i wanted to see if you could pass the turing test 20.4.1) • Does it bother you that you wanted to see if I could really pass the turing test? • Try typing • ….. • How do you reconcile problems at school? Meta-x • This is a waste of time. Good bye doctor • My secretary will send you a bill. in your favorite Emacs. H: Good afternoon. My name is Colby’s PARRY Doctor Jones. P: Pleased to meet you. • Kenneth Colby modeled H: How are you? a paranoid using the P: All right I guess. same techniques circa H: Could you tell me your name? 1968. P: Pat Smith. • PARRY has basic H: I though you might have some emotions. If it gets problems. angry, its replies P: I've been upset lately. become more hostile. H: By what? • In a Turing test, expert P: People get on my nerves psychiatrists were sometimes. unable to distinguish H: Tell me more. PARRY’s ramblings P: Do you know anything about from those of real bookies? paranoids. ... 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. – If better than a human, win $100,000. (Nobody yet…) So when WILL we decide that computers are intelligent? 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 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. What can AI systems do Here are some example applications • 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. 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 What's Happening Now in AI? • Exercise for the next part of the class: • In teams of two: – Log in to one of the workstations – Go to the AAAI news web page: http://www.aaai.org/AITopics/newstopi cs/main.html – Explore some of the news articles on topics that interest you. – Pick two articles to tell us about at the end of the class. Why do those two interest you? First Homework Assignment • Assignment (link on the syllabus page). 1. Read chapters 1 and 2. (Chapter 2 is what we will cover next week) 2. From the textbook: Answer questions 1.2 and any three of 1.7a-j. 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 fund a copy of Turing's paper at http://www.abelard.org/turpap/turpap.htm. 3. 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, Jan 22. • Academic Integrity revisited.
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