Transcript What is AI? - BYU Computer Science Students Homepage Index

Introduction to AI
CS470 – Fall 2003
Outline
What is AI?
A Brief History
State of the art
Course Outline
Administrivia
What is AI?
Other textbook definitions…
AI is an effort to make computers think . . . machines with
Intelligent
minds
AI behavior
is automation of activities we associate with human thinking,
such as decision-making, problem solving, learning
AI is the art of creating machines that perform
functions that
Computer
require intelligence when performed by people
AI is the study of how to make computers do things at which
people are, so far, better
AI study of mental faculties through use of computational
models
AI is the study of computations that make it possible to
perceive, reason, and act
AI is the design of intelligent
agents
Humans
AI is concerned with intelligent behavior in artifacts
AI Characterizations
Discipline that systematizes and automates
intellectual tasks to create machines that:
Think like humans
Think rationally
Act like humans
Act rationally
Act Like Humans
AI is the art of creating machines that
perform functions that require
intelligence when performed by humans
Methodology: Take an intellectual task
at which people are better and make a
computer do it •Prove a theorem
•Play chess
Turing test
•Plan a surgical operation
•Diagnose a disease
•Navigate in a building
Turing Test
Human imitation test
Loebner contest: Modern version of Turing Test,
held annually, with a $100,000 prize.
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…)
Problem: Turing test is not reproducible, constructive, or
amenable to mathematical analysis

Eliza
ELIZA: A program that simulated a psychotherapist
interacting with a patient
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 an associated reply which might include bits of
the input (after simple transformations (my  your)
Weizenbaum was shocked at reactions:



Psychiatrists thought it had potential.
People unequivocally anthropomorphized.
Many thought it solved the NL problem.
Try typing Meta-x doctor in Emacs.
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?
...
Parry
meets
Eliza…
P:
E:
P:
E:
P:
E:
P:
E:
P:
I don't understand your motives.
Why don't you understand my motives?
Shouldn't I be?
Why do you say that just now?
That's just the way it is.
Suppose that's the way it were not.
So?
I am not sure I understand you fully.
You harp on the same questions too
much.
E: What does that similarity suggest to
you?
P: I don't get you.
E: Do you wish that you did get I?
Chess
Name: Gary Kasparov
Title: World Chess
Champion
Crime: Valued greed
over common sense
Humans are still better at making up excuses.
© Jonathan Schaeffer
Perspective on Chess: Pro
“Saying Deep Blue doesn’t really think
about chess is like saying an airplane
doesn't really fly because it doesn't flap
its wings”
Drew McDermott
© Jonathan Schaeffer
Perspective on Chess: Con
“Chess is the Drosophila of artificial
intelligence. However, computer chess has
developed much as genetics might have if the
geneticists had concentrated their efforts
starting in 1910 on breeding racing Drosophila.
We would have some science, but mainly we
would have very fast fruit flies.”
John McCarthy
© Jonathan Schaeffer
Think Like Humans
•Connection with Psychology
How the computer performs functions
•General Problem Solver (Newell and Simon)
does matter
•Neural networks
Comparison of
the traces of the
•Reinforcement
learning
reasoning steps
Cognitive science  testable theories of
But:
the workings of the human mind
• Role of physical body, senses, and evolution
in human intelligence?
• Do we want to duplicate human imperfections?
Think/Act Rationally
Always make the best decision given
what is available
(knowledge,
•Connection
to economics,
operationaltime,
research,
resources)
and
control theory
•But
ignoresknowledge,
role of consciousness,
Perfect
unlimitedemotions,
resources
fear of dying on intelligence
 logical reasoning
Imperfect knowledge, limited resources
 (limited) rationality
Quiz
Does a plane fly?
Does a boat swim?
Does a computer think?
AI Prehistory
Philosophy
Mathematics
Psychology
Economics
Linguistics
Neuroscience
Control theory
logic, methods of reasoning
mind as physical system
foundations of learning, language, rationality
formal representation and proof algorithms,
computation, (un)decidability, (in)tractability
probability
adaptation
phenomena of perception and motor control
experimental techniques (psychophysics, etc.)
formal theory of rational decisions
knowledge representation
grammar
plastic physical substrate for mental activity
homeostatic systems, stability
simple optimal agent designs
Bits of History
1956: The name “Artificial Intelligence”
was coined by John McCarthy. (Would
“computational rationality” have been
better?)
Early period (50’s to late 60’s):
Basic principles and generality




General problem solving
Theorem proving
Games
Formal calculus
Bits of History
1969-1971: Shakey the
robot (Fikes, Hart, Nilsson)
Logic-based planning
(STRIPS)
Motion planning (visibility
graph)
Inductive learning (PLANEX)
Computer vision
Bits of History
Knowledge-is-Power period (late 60’s to
mid 80’s):


Focus on narrow tasks require expertise
Encoding of expertise in rule form:
If:
the car has off-highway tires and
4-wheel drive and
high ground clearance
Then: the car can traverse difficult terrain (0.8)



Knowledge engineering
5th generation computer project
CYC system (Lenat)
Bits of History
AI becomes an industry (80’s – present):






Expert systems: Digital Equipment,
Teknowledge, Intellicorp, Du Pont, oil
industry, …
Lisp machines: LMI, Symbolics, …
Constraint programming: ILOG
Robotics: Machine Intelligence Corporation,
Adept, GMF (Fanuc), ABB, …
Speech understanding
Information Retrieval – Google, …
State of the Art
Which of the following can be done at present?
Play a decent game of table tennis
Drive along a curving mountain road
Drive in the center of Cairo
Buy a week's worth of groceries at Berkeley Bowl
Buy a week's worth of groceries on the web
Play a decent game of bridge
Discover and prove a new mathematical theorem
Write an intentionally funny story
Give competent legal advice in a specialized area of law
Translate spoken English into spoken Swedish in real time
Perform a complex surgical operation
Predictions and Reality … (1/3)
In the 60’s, a famous AI professor from MIT
said: “At the end of the summer, we will have
developed an electronic eye”
As of 2002, there is still no general computer
vision system capable of understanding
complex dynamic scenes
But computer systems routinely perform road
traffic monitoring, facial recognition, some
medical image analysis, part inspection, etc…
Predictions and Reality … (2/3)
In 1958, Herbert Simon (CMU) predicted
that within 10 years a computer would
be Chess champion
This prediction became true in 1998
Today, computers have won over world
champions in several games, including
Checkers, Othello, and Chess, but still do
not do well in Go
Predictions and Reality … (3/3)
In the 70’s, many believed that computer-controlled
robots would soon be everywhere from manufacturing
plants to home
Today, some industries (automobile, electronics) are
highly robotized, but home robots are still a thing of
the future
But robots have rolled on Mars, others are performing
brain and heart surgery, and humanoid robots are
operational and available for rent (see:
http://world.honda.com/news/2001/c011112.html)
Why is AI Hard?
Simple syntactic manipulation is not
enough
•Machine Translation
•Big project in 1957 following Sputnik launch
•Translation of Russian documents
•‘The spirit is willing but the flesh is weak’
•‘The vodka is strong but the meat is rotten’
Why is AI Hard?
Computational intractibility
•AI goal defined before notion of NP-completeness
•people thought to solve larger problems we
simply need larger/faster computers
•didn’t understand the notion of exponential
growth
Why AI today?
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.
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.
CS 470
We will focus on the rational agents
(“engineering”) paradigm
Make computers act more intelligently


techniques: search, supervised learning,
constraint satisfaction, decision theory
tasks: perception, commonsense
reasoning, planning
Rational Agents
An agent is an entity that perceives and acts
Abstractly, an agent is a function from
percept histories to actions :P*→A
For any given class of environments and
tasks, we seek the agent (or class of agents)
with the best performance
Caveat: computational limitations make perfect
rationality unachievable; so: design best program
for given machine resources
Syllabus
Representing
knowledge
Problem solving:


Reasoning or using
knowledge
Logic and Inference
Planning
Dealing with Uncertainty


Learning or Acquiring
knowledge
Search
Constraint satisfaction

Adversarial search
Deciding under probabilistic
uncertainty
Belief networks
Supervised learning