Introduction to AIIntroduction to AICS470 – Fall 2003CS470 – Fall 2003

Outline

What is AI?A Brief HistoryState of the artCourse OutlineAdministrivia

What is AI?What is AI?

Other textbook Other textbook definitions…definitions…

AI is an effort to make computers think . . . machines with mindsAI is automation of activities we associate with human thinking, such as decision-making, problem solving, learningAI is the art of creating machines that perform functions that require intelligence when performed by peopleAI is the study of how to make computers do things at which people are, so far, betterAI study of mental faculties through use of computational modelsAI is the study of computations that make it possible to perceive, reason, and actAI is the design of intelligent agentsAI is concerned with intelligent behavior in artifacts

Intelligent behavior

Humans

Computer

AI CharacterizationsAI Characterizations

Discipline that systematizes and automates intellectual tasks to create machines that:

Think like humans

Think rationally

Act like humans Act rationally

Act Like HumansAct Like Humans

AI is the art of creating machines that perform functions that require intelligence when performed by humansMethodology: Take an intellectual task at which people are better and make a computer do itTuring test

•Prove a theorem•Play chess•Plan a surgical operation•Diagnose a disease•Navigate in a building

Turing TestTuring Test

Human imitation testLoebner 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

ElizaELIZA: 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 PARRYKenneth 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? ...

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: I don't understand your motives. E: Why don't you understand my

motives? P: Shouldn't I be? E: Why do you say that just now? P: That's just the way it is. E: Suppose that's the way it were not. P: So? E: I am not sure I understand you fully. P: 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?

P: I don't understand your motives. E: Why don't you understand my

motives? P: Shouldn't I be? E: Why do you say that just now? P: That's just the way it is. E: Suppose that's the way it were not. P: So? E: I am not sure I understand you fully. P: 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?

ChessChess

Name: Gary KasparovTitle: World Chess ChampionCrime: Valued greed over common sense

Humans are still better at making up excuses.

© Jonathan Schaeffer

Perspective on Chess: ProPerspective 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: Perspective on Chess: ConCon

“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 HumansThink Like Humans

How the computer performs functions does matterComparison of the traces of the reasoning stepsCognitive science testable theories of the workings of the human mind

•Connection with Psychology•General Problem Solver (Newell and Simon)•Neural networks•Reinforcement learning

But:• Role of physical body, senses, and evolution in human intelligence?• Do we want to duplicate human imperfections?

Think/Act RationallyThink/Act Rationally

Always make the best decision given what is available (knowledge, time, resources)Perfect knowledge, unlimited resources logical reasoningImperfect knowledge, limited resources (limited) rationality

•Connection to economics, operational research, and control theory•But ignores role of consciousness, emotions, fear of dying on intelligence

QuizQuiz

Does a plane fly?Does a boat swim?Does a computer think?

AI PrehistoryPhilosophy logic, methods of reasoning

mind as physical systemfoundations of learning, language,

rationalityMathematics formal representation and proof algorithms,

computation, (un)decidability, (in)tractability probability

Psychology adaptation phenomena of perception and motor controlexperimental techniques (psychophysics,

etc.)Economics formal theory of rational decisionsLinguistics knowledge representation

grammarNeuroscience plastic physical substrate for mental activityControl theory homeostatic systems, stability

simple optimal agent designs

Bits of HistoryBits of History1956: 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 HistoryBits of History1969-1971: Shakey the robot (Fikes, Hart, Nilsson) Logic-based planning (STRIPS)Motion planning (visibility graph)Inductive learning (PLANEX)Computer vision

Bits of HistoryBits 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 and4-wheel drive andhigh ground clearance

Then: the car can traverse difficult terrain (0.8) Knowledge engineering 5th generation computer project CYC system (Lenat)

Bits of HistoryBits 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 lawTranslate spoken English into spoken Swedish in real timePerform a complex surgical operation

Predictions and Reality … Predictions and Reality … (1/3)(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 scenesBut computer systems routinely perform road traffic monitoring, facial recognition, some medical image analysis, part inspection, etc…

Predictions and Reality … Predictions and Reality … (2/3)(2/3)

In 1958, Herbert Simon (CMU) predicted that within 10 years a computer would be Chess championThis prediction became true in 1998Today, 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 … Predictions and Reality … (3/3)(3/3)

In the 70’s, many believed that computer-controlled robots would soon be everywhere from manufacturing plants to homeToday, some industries (automobile, electronics) are highly robotized, but home robots are still a thing of the futureBut 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 470CS 470

We will focus on the rational agents (“engineering”) paradigmMake 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 actsAbstractly, an agent is a function from percept histories to actions :P*→AFor any given class of environments and tasks, we seek the agent (or class of agents) with the best performanceCaveat: computational limitations make perfect rationality unachievable; so: design best program for given machine resources

SyllabusSyllabus

Representing knowledge

Reasoning or using knowledge

Learning or Acquiring knowledge

Problem solving: Search Constraint satisfaction

Logic and InferencePlanningDealing with Uncertainty

Adversarial search Deciding under

probabilistic uncertainty Belief networks

Supervised learning