1CS 188: Artificial IntelligenceFall 2010Lecture 1: Introduction8/26/2010Dan Klein – UC BerkeleyMultiple slides over the course adapted fromeither Stuart Russell or Andrew Moore Course Information Communication: Announcements on webpage Questions? Try the newsgroup! Staff email: [email protected]://inst.cs.berkeley.edu/~cs188 Course StaffCourse StaffArjunSinghDan KleinGSIsProfessorTaylor Berg-KirkpatrickDavid HallJon BarronJieTangAdam PaulsCourse Information Book: Russell & Norvig, AI: A Modern Approach, 3rdEd. Prerequisites: (CS 61A or B) and (Math 55 or CS 70) Strongly recommended: CS61A, CS61B and CS70 There will be a lot of math and programming Work and Grading: 5 programming projects: Python, groups of 1-2 5 late days, 2 per project 4 written projects: solve together, write-up alone Midterm and final Participation Fixed scale Academic integrity policy Contests!Today What is artificial intelligence? What can AI do? What is this course?Sci-Fi AI?2What is AI?Think like humans Think rationallyAct like humans Act rationallyThe science of making machines that:Rational DecisionsWe’ll use the term rational in a particular way: Rational: maximally achieving pre-defined goals Rational only concerns what decisions are made (not the thought process behind them) Goals are expressed in terms of the utility of outcomes Being rational means maximizing your expected utilityA better title for this course would be:Computational RationalityMaximize Your Expected UtilityWhat About the Brain? Brains (human minds) are very good at making rational decisions (but not perfect) “Brains are to intelligence as wings are to flight” Brains aren’t as modular as software Lessons learned: prediction and simulation are key to decision makingA (Short) History of AI 1940-1950: Early days 1943: McCulloch & Pitts: Boolean circuit model of brain 1950: Turing's “Computing Machinery and Intelligence” 1950—70: Excitement: Look, Ma, no hands! 1950s: Early AI programs, including Samuel's checkers program, Newell & Simon's Logic Theorist, Gelernter's Geometry Engine 1956: Dartmouth meeting: “Artificial Intelligence” adopted 1965: Robinson's complete algorithm for logical reasoning 1970—88: Knowledge-based approaches 1969—79: Early development of knowledge-based systems 1980—88: Expert systems industry booms 1988—93: Expert systems industry busts: “AI Winter” 1988—: Statistical approaches Resurgence of probability, focus on uncertainty General increase in technical depth Agents and learning systems… “AI Spring”? 2000—: Where are we now? What Can AI Do?Quiz: Which of the following can be done at present? Play a decent game of table tennis? Drive safely along a curving mountain road? Drive safely along Telegraph Avenue? Buy a week's worth of groceries on the web? Buy a week's worth of groceries at Berkeley Bowl? Discover and prove a new mathematical theorem? Converse successfully with another person for an hour? Perform a complex surgical operation? Unload a dishwasher and put everything away? Translate spoken Chinese into spoken English in real time? Write an intentionally funny story?3Unintentionally Funny Stories One day Joe Bear was hungry. He asked his friend Irving Bird where some honey was. Irving told him there was a beehive in the oak tree. Joe walked to the oak tree. He ate the beehive. The End. Henry Squirrel was thirsty. He walked over to the river bank where his good friend Bill Bird was sitting. Henry slipped and fell in the river. Gravity drowned. The End. Once upon a time there was a dishonest fox and a vain crow. One day the crow was sitting in his tree, holding a piece of cheese in his mouth. He noticed that he was holding the piece of cheese. He became hungry, and swallowed the cheese. The fox walked over to the crow. The End.[Shank, Tale-Spin System, 1984]Natural Language Speech technologies Automatic speech recognition (ASR) Text-to-speech synthesis (TTS) Dialog systems Language processing technologies Machine translation Information extraction Information retrieval, question answering Text classification, spam filtering, etc…[demos: language]Vision (Perception)Image from Erik Sudderth• Object and character recognition• Scene segmentation• Image classificaiton[videos: vision]Robotics Robotics Part mech. eng. Part AI Reality muchharder thansimulations! Technologies Vehicles Rescue Soccer! Lots of automation… In this class: We ignore mechanical aspects Methods for planning Methods for controlImages from stanfordracing.org, CMU RoboCup, Honda ASIMO sites[videos: robotics]Logic Logical systems Theorem provers NASA fault diagnosis Question answering Methods: Deduction systems Constraint satisfaction Satisfiability solvers (huge advances here!)Image from Bart SelmanGame Playing May, '97: Deep Blue vs. Kasparov First match won against world-champion “Intelligent creative” play 200 million board positions per second! Humans understood 99.9 of Deep Blue's moves Can do about the same now with a big PC cluster Open question: How does human cognition deal with thesearch space explosion of chess? Or: how can humans compete with computersat all?? 1996: Kasparov Beats Deep Blue“I could feel --- I could smell --- a new kind of intelligence across the table.” 1997: Deep Blue Beats Kasparov“Deep Blue hasn't proven anything.”Text from Bart Selman, image from IBM’s Deep Blue pages4Decision Making• Scheduling, e.g. airline routing, military• Route planning, e.g. mapquest• Medical diagnosis• Automated help desks• Fraud detection• Spam classifiers• Web search engines• … Lots more!Designing Rational Agents An agent is an entity that perceives and acts. A rational agent selects actions that maximize its utility function. Characteristics of the percepts, environment,and action space dictate techniques for selecting rational actions. This course is about: General AI techniques for a variety of problem types Learning to recognize when and how a new problem can be solved with an existing techniqueAgentSensors?ActuatorsEnvironmentPerceptsActionsPacman as an
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