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Random Administrivia

This lecture introduces the concept of artificial intelligence (AI), including different approaches to AI, examples of intelligent behavior, and a brief history of AI development. Topics covered include systems that act like humans, systems that think like humans, systems that think rationally, and systems that act rationally. The lecture also provides an overview of the LISP programming language and its importance in AI research.

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Random Administrivia

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  1. Random Administrivia • In CMC 306 on Monday for LISP lab

  2. Artificial Intelligence: Introduction • What IS artificial intelligence? • Examples of intelligent behavior:

  3. Definitions of AI • There are as many definitions as there are practitioners. • How would you define it? What is important for a system to be intelligent?

  4. Four main approaches to AI • Systems that act like humans • Systems that think like humans • Systems that thinkrationally • Systems that act rationally

  5. Approach #1: Acting Humanly • AI is: “The art of creating machines that perform functions that require intelligence when performed by people” (Kurzweil) • Ultimately to be tested by the Turing Test

  6. The Turing Test • Demonstrations of software • Eliza: http://ds.dial.pipex.com/town/avenue/wi83/eliza/ (1965) • Alice: http://www.alicebot.org/ (Loebner Prize 2000 & 01 winner) • Transcripts: http://www.loebner.net/Prizef/hutchens1996.txt

  7. In practice • Needs: • Natural language processing • Knowledge representation • Automated reasoning • Machine learning • Too general a problem – unsolved in the general case • Intelligence takes many forms, which are not necessarily best tested this way • Is it actually intelligent? (Chinese room)

  8. Approach #2: Thinking Humanly • AI is: “[The automation of] activities that we associate with human thinking, activities such as decision-making, problem solving, learning…” (Bellman) • Goal is to build systems that function internally in some way similar to human mind

  9. Workings of the human mind • Computer game players typically work much differently than human players • Cognitive science tries to model human mind based on experimentation • Cognitive modeling approach to AI: act intelligently while internally mimicking to human mind

  10. Approach #3: Thinking rationally • AI is: using logic to make complex decisions • I.e., how can knowledge be represented logically, and how can a system draw deductions? • Uncertain knowledge? Informal knowledge? • “I think I love you.”

  11. Approach #4: Acting rationally • AI is: “...concerned with the automation of intelligent behavior” (Luger and Stubblefield) • The intelligent agent approach • An agent is something that perceives and acts • Emphasis is on behavior

  12. Acting rationally: emphasis of this class (and most AI today) • Why? • In solving actual problems, it’s what really matters • Behavior is more scientifically testable than thought • More general: rather than imitating humans trying to solve hard problems, just try to solve hard problems

  13. Recap on the difference in approaches • Thought vs. behavior • Human vs. rational

  14. Early AI History • Birth: McCulloch and Pitts, simulated neurons, 1943 • “AI”: Dartmouuth workshop, 1956 • Early successes: General Problem Solver (1957), Lisp (1958) • Predictions that AI would eventually do almost anything

  15. The Dark Ages • Mid 60s – Mid 70s • AI failed to deliver • Minsky and Papert’s Perceptrons

  16. The Crawl Back • 1970s: knowledge based AI • 1980s: some commercial systems • Rumelhart and McClelland’s Parallel Distributed Processing

  17. Modern Success Story • Machine learning / data mining • Intelligent agents (‘bots) • Game playing (Deep Blue / Fritz) • Robotics • Natural language processing (Babelfish)

  18. More History of AI • It’s in text and very cool, read it • Sections 1.2-1.3

  19. What we’ll be doing • LISP Programming • Intelligent agents • Search methods, and how they relate to game playing (e.g. chess) • Logic and reasoning • Propositional logic

  20. What we’ll be doing • Uncertain knowledge and reasoning • Probability, Bayes rule • Machine learning • Neural networks, decision trees, computationally learning theory, reinforcement learning

  21. What we won’t be doing in class (but you can for project) • HAL • Natural language processing (Jeff’s class) • Computer vision (Jack’s class in image processing) • Building Quake-bots

  22. The Lisp Programming Language • Developed by John McCarthy at MIT • Second oldest high level language still in use (next to FORTRAN) • LISP = LISt Processing • Common Lisp is today’s standard • Most popular language for AI

  23. Why use Lisp? • Everything's a list • Interactive • Symbolic • Dynamic • Garbage collection

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