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CS 8520: Artificial Intelligence

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The American Association for Artificial Intelligence is the primary professional ... a topic for interesting philosophical debate, but it's not of any practical help. ... – PowerPoint PPT presentation

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Title: CS 8520: Artificial Intelligence


1
CS 8520 Artificial Intelligence
  • Introduction
  • Paula Matuszek
  • Fall, 2005

2
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

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.

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

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.

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

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.

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 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/.
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.

10
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/.
11
History
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.

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
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.

15
Possible Approaches
AI tends to work mostly in this area
Based on http//www.cs.umbc.edu/671/Fall01/.
16
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/.
17
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/.
18
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/.
19
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/.
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.

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.

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

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.

24
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/.
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/

Based on http//www.cs.umbc.edu/671/Fall01/.
26
So when WILL we decide that computers are
intelligent?
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

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.

Based on http//www.cs.umbc.edu/671/Fall01/.
29
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/.
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

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/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.
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