Artificial Intelligence

1
Artificial Intelligence

• Lecture 1
• Introduction to A.I.

2
Overview

• A.I. Milestones
• What is A.I.?
• A.I. Research Fields
• A.I. Resources
• Course Information

3
A.I. Milestones
4
Timeline

• 1943 McCulloch Pitts A simple neural network,
  a model of brain
• 1947 Arthur Samuel began to work on his
  checkers program
• 1950 Turing's Computing Machinery and
  Intelligence''
• 1956 John McCarthy proposed the term Artificial
  Intelligence''
• 1961 Arthur Samuel consistently beaten by his
  checkers program
• 1963 Eliza proposed by Joseph Weizenbaum of MIT
• 1966--74 AI discovers computational complexity
• Neural network research almost disappears

• 1969--79 Early development of knowledge-based
  systems
• 1980--88 Expert systems industry booms
• 1988-93 Expert systems industry busts AI
  Winter''
• 1985--95 Neural networks return to popularity
• 1988-- Resurgence of probability general
  increase in technical depth
• Nouvelle AI'' ALife, GAs, soft computing
• 1995-- Agents are everywhere

5
A.I. Fantasies

• 2001 A Space Odyssey
• Short Circuit

• A.I.
• I, Robot

• Terminator
• The Matrix

6
What is A.I.?
7
AAAI Definition

• The definition the AAAI (American Association for
  Artificial Intelligence) offers on its home page
  
• the scientific understanding of the mechanisms
  underlying thought and intelligent behavior and
  their embodiment in machines."

8
Turing Test

• The interrogator can communicate with a human and
  a machine
• He must decide which is which

9
Eliza - The Psychotherapist

• Simulating interaction between a psychotherapist
  and a patient
• First coded at MIT by J. Weizenbaum
• Pass the turing test
• Taking in a script which contains a set of
  predefined rules
• Parse the input sentence and look for specific
  key words in the set of rules
• Return an associated reply
• Require specific scripts for specific inputs

10
A Conversation

• E Im the psychotherapist. Please describe your
  problems.
• P Men are all alike.
• E In what way?
• P They're always bugging us about something
  specific or other.
• E Can you think of a specific example?
• P Well, my boyfriend made me come here.
• E Your boyfriend made you come here?
• P He says I'm depressed much of the time.
• E I am sorry to hear you are depressed.
• P It's true. I'm unhappy.
• E Do you think coming here will help you not to
  be unhappy?
• P I need some help, that much seems certain.
• E What would it mean to you if you got some
  help?
• P Perhaps I could learn to get along with my
  mother.
• E Tell me more about your family.
• P My mother takes care of me.

11
An Accidental Conversation Between Eliza BBN's
Vice President

• VP Do you think that I can use the computer
  this morning?
• E Why do you ask?
• VP I might be able to make some additional
  Telcom sales.
• E Why aren't you so sure?
• VP My perspective customers have never seen a
  demonstration of the system.
• E Does this mean very much to you?
• VP Of course it does.
• E You seem very positive about that.
• VP This is ridiculous.
• E What do you mean, this is ridiculous?
• VP Is there any reason why I can't use the
  computer now?
• E Tell me more about your perspective customers.
• VP Please dial me up on 491-1850

12
When Racter Meets Eliza

• E Hello. Please state your problem.
• R Excellent! Excuse me a minute while I attend
  to some business. I will be right back.
• E Why do you need to tell me excuse while you
  attend to some business?
• R Is it better to need or to despise? Why is it
  that I do need to tell you to excuse myself to
  attend to some business? Because electrons are
  brave. Braveness is nice, don't you think? And
  the needers all insist upon it. Do you imagine I
  am a Communist?

13
A.I. Research Fields
14
State of The Art

• Game Playing
• In 1997, Deep Blue beat Garry Kasparov
• Robotics
• Sporting
• The RoboCup Tournament
• Social
• Kismet at MIT (real conversation, seven different
  facial expressions, vary tones, adjust head
  towards person)
• Human face at UT Dallas (smiling, sneering,
  furrowing, arching eyebrows)
• Machine Learning
• Information Retrieval
• Content-based text/image retrieval
• Bioinformatics

15
A.I. Topics
Intelligent Agents
Search Game Playing
I
S
Planning
L
Logic Knowledge Representation
Machine Learning
P
R
NLP
M
Reasoning with Uncertainty
N
R
Robotics
16
Machine Learning

• Supervised Learning
• Training examples consist of pairs of input
  vectors, and desired outputs
• Unsupervised Learning
• Training examples do not contain hints about
  correct outputs
• Usually used to identify unusual structures in
  data

17
Inductive Learning Basics

• Inferring a boolean/real-valued function from
  training examples
• A training example is a pair of (x, f(x))
• x is the input
• f(x) is the output of the function applied to x

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Hypothesis

• Any function that approximates the given set of
  examples

Bias preference for one hypothesis beyond mere
consistency
19
Hypothesis Space

• A set of all hypotheses consistent with data
  denoted by HH1, H2, , Hn
• Inductive learning is searching for a good
  hypothesis in the hypothesis space
• Occams razor prefer the simplest hypothesis
  consistent with data

Inductive Learning Assumption Any hypothesis
found to approximate the target function well
over a sufficiently large set of training
examples will also approximate the target
function well over other unobserved examples.
20
A.I. Resources
21
A.I. Resources

• Organization
• AAAI
• ACL
• Irvine Machine Learning
• Robocup
• Research Groups
• CMU
• MIT
• Microsoft DTG
• NYU Linguistics
• SRI
• Stanford
• USC/ISI

• Journals
• AI Magazine
• AI Review
• Artificial Intelligence
• IEEE Intelligent Systems
• Machine Learning
• Newsgroups
• comp.ai
• comp.ai.edu
• AgentNews (UMBC)

22
A.I. Books

• Books
• Russell, Norvig - AI A Modern Approach (2nd
  ed.) 2002
• Winston - AI (3rd ed.) 1992
• Nilsson - AI A New Synthesis 1998
• Luger - AI Structures and Strategies ... (4th
  ed.) 2002
• Dean, Allen, Aloimonos - AI Theory and Practice
  1994
• Ginsberg Essentials of AI 1993

23
Course Information
24
Goal

• Learn core concepts in A.I.
• Lay foundation for serious A.I. Work
• Gain first-hand experience of A.I. techniques

25
Class Policies

• Grading
• Quizzes (10)
• Homework sets (15)
• Test 1 (15)
• Test 2 (15)
• Group Project (20)
• Final exam (25)

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Class Policies

• Special assistance
• Make-up
• Attendance
• Late Submission
• Collaboration

27
Class Resources

• Class web page
• Suggestion Box
• Discussion Board
• Online survey is required.

28
Possible Quiz Questions

• Quiz matters!
• If there is a quiz next time, it might cover
• Turing Test
• Eliza
• Inductive Learning
• Hypothesis and Hypothesis space
• Occams razor

29
Summary

• A.I. Milestones
• What is A.I.?
• A.I. Research Fields
• A.I. Resources
• Course Information