CS 8520: Artificial Intelligence

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CS 8520 Artificial Intelligence

• Introduction
• Paula Matuszek
• Fall, 2005

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AI Course Details

• Instructor
• Paula Matuszek
• Paula.A.Matuszek_at_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

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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.

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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

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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.

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• 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

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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.

Based on http//www.cs.umbc.edu/671/Fall01/.
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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 dont 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.

Based on http//www.cs.umbc.edu/671/Fall01/.
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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.

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Whats easy and whats 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?

Based on http//www.cs.umbc.edu/671/Fall01/.
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History
Based on http//www.cs.umbc.edu/671/Fall01/.
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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.

Based on http//www.cs.umbc.edu/671/Fall01/.
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Foundations of AI
Computer Science Engineering
Mathematics
Philosophy
AI
Biology
Economics
Psychology
Linguistics
Cognitive Science
Based on http//www.cs.umbc.edu/671/Fall01/.
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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.

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Possible Approaches
AI tends to work mostly in this area
Based on http//www.cs.umbc.edu/671/Fall01/.
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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.

Based on http//www.cs.umbc.edu/671/Fall01/.
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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

Based on http//www.cs.umbc.edu/671/Fall01/.
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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.

Based on http//www.cs.umbc.edu/671/Fall01/.
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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).

Based on http//www.cs.umbc.edu/671/Fall01/.
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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.

Based on http//www.cs.umbc.edu/671/Fall01/.
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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.

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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 -gt
  your)
• Weizenbaum was shocked at reactions
• Psychiatrists thought it had potential.
• People unequivocally anthropomorphised.
• Many thought it solved the NL problem.

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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.

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Colbys PARRY
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? ...

• 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 PARRYs ramblings from
  those of real paranoids.

Based on http//www.cs.umbc.edu/671/Fall01/.
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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/

Based on http//www.cs.umbc.edu/671/Fall01/.
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So when WILL we decide that computers are
intelligent?
Based on http//www.cs.umbc.edu/671/Fall01/.
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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

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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.

Based on http//www.cs.umbc.edu/671/Fall01/.
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What cant 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

Based on http//www.cs.umbc.edu/671/Fall01/.
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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

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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/t
  urpap/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_at_villanova.edu
• Academic Integrity revisited.