1. Artificial Intelligence CS 165A September 27, 2007 Prof. Matthew Turk Computer Science Department Media Arts and Technology Program

2. 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

3. 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

4. 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”

5. John McCarthy Herb Simon Allen Newell Marvin 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.”

6. 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

7. 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

8. 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…

9. 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…

10. 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

11. 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

12. 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

13. Terrain between LA and Las Vegas

16. DARPA Urban Challenge 2007

17. 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

18. 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

19. 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

20. 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

21. An experiment... Data/knowledgePerception Operations/Rules ControlEffector The World One at a time Blind! Can only report what’s there

22. 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

23. “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

24. 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.

25. Course administrivia Web sites –http://www.cs.ucsb.edu/~cs165ahttp://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!

26. 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