Transcript CSC 8520: Artificial Intelligence Course Details

CSC 8520: Artificial Intelligence
Course Details
• Paula Matuszek, Robin McEntire
• Student Questionnaire
• Snow
– Contact sheet
• Course web page
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Home page, www.csc.vill.edu/~matuszek/spring2004
Syllabus
Handing in Homework
Academic Integrity
• Questions?
Intelligent Systems Lab
• Mendel 156
• We will meet in here, and use the lab during class
• Your badge should also open the door, and you
can work in here. (It may take a while to get this
all working)
– No other classes
– Some other students
– Files and settings will NOT remain
• PCs have Windows XP, Lisp. Will have Prolog
and some other tools we will use
Musings on AI
• AI to me is a lot of things.
• The field can generally be viewed from two
directions:
– The areas you're working in
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Planning
Learning
Natural Language Understanding
Games
– The techniques you use
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Search
Knowledge Representation
Inference
Logic
Our Approach
• Following the book:
– Tools and techniques
– Some of the domains, depending on interest
• Working in the lab:
– We will spend some part of most classes doing handson 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.
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.
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.
• 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/.
Introduction to
Artificial
Intelligence
Chapter 1
Big Questions
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Can machines think?
And if so, how?
And if not, why not?
And what does this say about
human beings?
• And what does this say about
the mind?
What is AI?
• There are no crisp definitions
• Here’s one from John McCarthy, (He coined the phrase AI in
1956) - see http://www.formal.Stanford.EDU/jmc/whatisai/)
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.
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.
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
don’t 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
What’s easy and what’s 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
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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?
History
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.
Foundations of AI
Mathematics
Economics
Psychology
Computer
Science &
Engineering
AI
Cognitive
Science
Philosophy
Biology
Linguistics
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.
Possible Approaches
Like
humans
Think
GPS
Act
Eliza
Well
Rational
agents
Heuristic
systems
AI tends to
work mostly
in this area
Like
humans
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
a systems that are provably
correct, and guarantee finding
an optimal solution.
Think
Act
Well
GPS
Rational
agents
Eliza
Heuristic
systems
Like
humans
Act well
Think
GPS
Well
Rational
agents
Heuristic
Eliza
• For a given set of inputs, generate an
Act
systems
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
Like
humans
Think like humans
Think
GPS
Well
Rational
agents
• Cognitive science approach
• Focus not just on behavior and I/O Act Eliza Heuristic
systems
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.
Like
humans
Act like humans
Think
Act
Well
GPS
Rational
agents
Eliza
Heuristic
systems
• 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).
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.
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 -> 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 Elizalike program
found in
Emacs (Gnu
20.4.1)
• Try typing
Meta-x
doctor
in your favorite
Emacs.
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I am the psychotherapist. Please, describe your problems.
Each time you are finished talking, type RET twice.
i'm having a hard time with my students
Why do you say that?
well, they keep asking me for money
Maybe your plans have something to do with this.
what plans?
Is it because of your life that you say what plans?
No, it's because I didn't know what you were talking about
Is it because you didn't know what I was talking about that you
came to me?
no, it's because i wanted to see if you could pass the turing test
Does it bother you that you wanted to see if I could really pass
the turing test?
…..
How do you reconcile problems at school?
This is a waste of time. Good bye
My secretary will send you a bill.
Colby’s PARRY
• 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
PARRY’s ramblings
from those of real
paranoids.
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?
...
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.
– If better than a human, win $100,000. (Nobody
yet…)
So when WILL we decide that
computers are intelligent?
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
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.
What can AI systems do
Here are some example applications
• 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.
What can’t 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
What's Happening Now in AI?
• Exercise for the next part of the class:
• In teams of two:
– Log in to one of the workstations
– Go to the AAAI news web page:
http://www.aaai.org/AITopics/newstopi
cs/main.html
– Explore some of the news articles on topics that interest
you.
– Pick two articles to tell us about at the end of the class.
Why do those two interest you?
First Homework Assignment
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Assignment (link on the syllabus page).
1.
2.
Read chapters 1 and 2. (Chapter 2 is what we will cover next week)
From the textbook: Answer questions 1.2 and any three of 1.7a-j. 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 fund a copy of Turing's paper at
http://www.abelard.org/turpap/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.
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Due: 5PM, Jan 22.
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Academic Integrity revisited.