random administrivia in cmc 306 on monday for lisp lab
TRANSCRIPT
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Random Administrivia In CMC 306 on Monday for LISP lab
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Artificial Intelligence: Introduction What IS artificial intelligence? Examples of intelligent behavior:
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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?
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Four main approaches to AI Systems that act like humans Systems that think like humans Systems that think rationally Systems that act rationally
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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
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The Turing Test Demonstrations of software
Eliza: http://www-ai.ijs.si/eliza/eliza.html (1965)
Alice: http://www.alicebot.org/ (Loebner Prize 2000-2001 winner)
Transcript: http://www.nik.com.au/alice/
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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)
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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
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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
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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.”
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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
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Acting rationally: emphasis of 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
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Recap on the difference in approaches Thought vs. behavior Human vs. rational
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Early AI History Birth: McCulloch and Pitts,
simulated neurons, 1943 “AI”: Dartmouth workshop, 1956 Early successes: General Problem
Solver (1957), Lisp (1958) Predictions that AI would
eventually do almost anything
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The Dark Ages Mid 60s – Mid 70s AI failed to deliver Minsky and Papert’s Perceptrons
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The Crawl Back 1970s: knowledge based AI 1980s: some commercial systems Rumelhart and McClelland’s
Parallel Distributed Processing
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Modern Success Story Machine learning / data mining Intelligent agents (‘bots) Game playing (Deep Blue / Fritz) Robotics Natural language processing
(Babelfish)
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More History of AI It’s in text and very cool, read it Sections 1.2-1.3
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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
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What we’ll be doing Uncertain knowledge and
reasoning Probability, Bayes rule
Machine learning Neural networks, decision trees,
computationally learning theory, reinforcement learning
jjijiji aWin aW,
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What we won’t be doing in class (but you can for project) Natural language processing (Jeff’s
class) Computer vision (Jack’s image
processing class) Computers that will take over the
planet
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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 One of the most popular languages
for AI