Artificial IntelligenceCS 165A

September 27, 2007

Prof. Matthew TurkComputer Science Department

Media Arts and Technology Program

What is Artificial Intelligence?

• AI in the media– Popular movies

2001: A Space Odyssey Star Trek Star Wars The Terminator The Matrix

– Popular press, novels

• Often portrayed as– A property of evil computers– Computers doing impossible things

• Public view– Books and movies have inspired many AI researchers– Books and movies have raised the public’s expectations

2001: A Space Odyssey

• HAL was “born” on January 12, 1997 in Urbana, Illinois– January 12, 1992 in the film

• What does HAL do?– Plays chess

– Talks, converses

– Has common sense

– Sees

– Appreciates art

– Lipreading

– Has feelings, emotions

What is Artificial Intelligence? (cont.)

“The science and engineering of making intelligent machines, especially intelligent computer programs.”

“The business of getting computers to do things they cannot already do, or things they can only do in movies and science fiction stories.”

“The study of how to make computers do things at which, at the moment, people are better.”

“The design of flexible programs that respond productively in situations that were not specifically anticipated by the designer.”

“The construction of computations that perceive, reason, and act effectively in uncertain environments.”

“The branch of CS concerned with enabling computers to simulate such aspects of human intelligence as speech recognition, deduction, inference, creative response, the ability to learn from experience, and the ability to make inferences given incomplete information.”

“Modeling aspects of human cognition on computers”

“What AI people do”

John McCarthy

Herb Simon

Allen NewellMarvin Minsky

Seminal event for AI as a field, in 1956:The Dartmouth Summer Research Conference on Artificial Intelligence

“We propose that a 2 month, 10 man study of artificial intelligence be carried out during the summer of 1956 at Dartmouth College in Hanover, New Hampshire. The study is to proceed on the basis of the conjecture that every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it. An attempt will be made to find how to make machines use language, form abstractions and concepts, solve kinds of problems now reserved for humans, and improve themselves. We think that a significant advance can be made in one or more of these problems if a carefully selected group of scientists work on it together for a summer.”

Goals of AI

• Scientific– To understand the principles and mechanisms that account for

intelligent action

• Engineering– To design intelligent systems that can survive and operate in the

real world and solve problems of considerable scientific difficulty at high levels of competence

To understand and build intelligent systems

To create models and mechanisms of intelligent action

Intelligent systems

• An intelligent system is characterized as one that can:

– Exhibit adaptive, goal-oriented behavior

– Learn from experience

– Use vast amounts of knowledge

– Exhibit self-awareness

– Interact with humans using language and speech

– Tolerate error and ambiguity in communication

– Respond in real-time

What AI people study

• Logic

• Knowledge representation

• Search

• Reasoning/inference

• Non-monotonic reasoning

• Planning

• Probabilistic reasoning

• Naïve physics

• Machine learning

• Speech recognition

• Natural language processing

• Computer vision

• Pattern recognition

• Intelligent agents

• Robotics

• Neural networks

• Data mining

• Expert systems

… and more…

What AI people (and programs) do

• Prove theorems

• Emulate/model human cognitive abilities

• (Attempt to) solve exponentially hard problems

• Build expert systems for diagnostic tasks (e.g, medical diagnosis, error analysis)

• Build robots

• Build machine vision systems for industrial tasks, surveillance, consumer apps, etc.

• Create speech recognition and understanding systems for various domains

• Process text to { understand, summarize, correct, respond, etc. }

• Create data mining systems to process very large amounts of information (e.g., bioinformatics)

• Build intelligent agents to look and act in socially useful ways

• Develop computer games

… and more…

Some notable AI systems

• IBM’s Deep Blue– Beat world chess champion Gary Kasparov in 1997

– Kasparov vs. (Israeli-built) Deep Junior, January 2003 (ended in a draw)

– Kasparov vs. X3D Fritz, November 2003

• Expert systems– Medical diagnosis

A computerized Leukemia diagnosis system did a better job checking for blood disorders than human experts

• Speech recognition– Commercial systems by Dragon, IBM, and others

– Phone-based systems (e.g., airline reservations)

• Automatic scheduling for manufacturing operations

• User interface– Grammar and spelling checkers, automated help

Some notable AI systems (cont.)

• Data mining– Fraud detection, credit scoring, customer profiles and preferences,

genome analysis

• Cyc– Doug Lenat’s 18-year old project to give computer common sense

• Computer vision– E.g., “Hands Across America” 1995

– Face recognition systems for biometrics

• Robotics– Mars Rover, robots for hazard environments, factory automation

– Sony, Honda, others: robot pets

– CMU Navlab drove across country (2797/2849 miles) 1980s – DARPA ALV Program

– DARPA Grand Challenge, Urban Challenge

DARPA Grand Challenge (2004, 2005)

“DARPA intends to conduct a challenge of autonomous ground vehicles between Los Angeles and Las Vegas in March of 2004. A cash award of $1 million will be granted to the team that fields the first vehicle to complete the designated route within a specified time limit.”

http://www.darpa.mil/grandchallenge

Terrain between LA and Las Vegas

DARPA Urban Challenge 2007

Perspectives on AI / Disciplines involved

• AI functions as a channel of ideas between computing and other fields, ideas that in fact have profoundly changed those fields– Logic

– Mathematics

– Statistics

– Philosophy

– Psychology

– Linguistics

– Neuroscience

– Computer science

– Cognitive science

AI

Foundations of AI

• Philosophy– Framed the ideas of AI

Dualism/materialism, logical/rational/empirical, causality, consciousness, mind/body…

• Mathematics– Formalized computation, logic, probability

– Possibilities and limitations of computation

• Psychology– Experimental: the brain as an information processing device

(Cognitive Science)

• Computer Science and Engineering– Algorithms

– Built real machines, Moore’s Law progress

AI and Computer Science

• AI is mostly about software (usually large and complex)– Important: Algorithms, tools, complexity, etc.

• Early advanced in CS due to AI researchers include:– Search algorithms– List structures, pointers– Virtual memory– Dynamic memory allocation– Garbage collection– Logical programming

• CS 165A will be taught primarily from a CS perspective– Not the only perspective, though

What is an AI Program?

• AI programs can generally be thought of as comprising three separated parts– Data / knowledge (“knowledge base”)

– Operations / rules (“production rules”)

– Control Which rules to apply when Selecting operations and keeping track of their effects Typically defined by the search strategy

• Data and Operations should be modular and easy to modify

An experiment...

Data/knowledgePerception

Operations/Rules

ControlEffector

The World

One at a time

Blind!

Can only report what’s there

Goals of this course

• To teach you the main ideas of AI

• To introduce you to a set of key techniques and algorithms from AI

• To help you understand what’s hard in AI and why

• To see how AI relates to the rest of computer science

• To get you thinking about how AI can be applied to a variety of real problems

• To have fun

“Proper” background

• Blind search (depth-first, breadth-first)– CS 130A

• Trees (programming)– CS 20, 50, 130A

• Boolean logic, Propositional logic, First-order logic– CS 40

• Probability, Bayes rule– PSTAT 120A

• Parsing– CS 20, 160 (some)

• C++ / Java– several

UCSB CS AI Sequence: 165A and 165B

165A. Artificial Intelligence (Fall)(4) TURK

Prerequisites: Computer Science 130A; open to computer science majors only

An introduction to the field of Artificial Intelligence, which attempts to understand and build intelligent systems. Topics include AI programming languages, search, logic, knowledge representation and reasoning, game playing, planning, uncertainty, perception, and intelligent agents. 

165B. Machine Learning (Winter)(4) SMITH / SU

Prerequisites: Computer Science 165A

The course covers the most important techniques of machine learning (ML) and includes discussions of: well-posed learning problems; artificial neural networks; concept learning and general to specific ordering; decision tree learning; genetic algorithms; learning sets of rules; Bayesian learning; analytical learning; and combining inductive and analytical learning. The course integrates these approaches to learning with fundamental aspects of machine intelligence (MI), including search, knowledge representation and reasoning, and applications. 

Course administrivia

• Web sites– http://www.cs.ucsb.edu/~cs165a

Announcements, syllabus, schedule, lecture notes, assignments, grades, related links

• Expectations– Come to class, and come prepared

– Participate: Ask questions, offer insight, tell me I’m wrong...

– Think!

Reminders

• Peruse the course web site

• Join the mailman group

• Keep up with assigned reading

• Assignment #0 due Tuesday

• First discussion session next Wed. (3pm and 4pm)– Review of relevant prerequisites: data structures, probability and

statistics, logic

– Info on using CSIL (if necessary)

• First “Thursday Quiz” next week