DA_202_01_Introduction (1).pdf

DA 202 Basic of Artificial Intelligence Introduction These slides were created by Dan Klein and Pieter Abbeel  Textbook ▪ Not required, but for students who want to read more we recommend ▪ Russell & Norvig, AI: A Modern Approac...

DA 202 Basic of Artificial Intelligence Introduction These slides were created by Dan Klein and Pieter Abbeel  Textbook ▪ Not required, but for students who want to read more we recommend ▪ Russell & Norvig, AI: A Modern Approach, 3rd Ed.  Today ▪ What is artificial intelligence? ▪ What can AI do? ▪ What is this course?  What is AI? The science of making machines that: Think like people Think rationally Act like people Act rationally  Rational Decisions We’ll use the term rational in a very specific, technical way: ▪ Rational: maximally achieving pre-defined goals ▪ Rationality 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 utility A better title for this course would be: Computational Rationality  What About the Brain? ▪ Brains (human minds) are very good at making rational decisions, but not perfect ▪ Brains aren’t as modular as software, so hard to reverse engineer! ▪ “Brains are to intelligence as wings are to flight” ▪ Lessons learned from the brain: memory and simulation are key to decision making  A (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—90: 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” ▪ 1990—: 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? ▪ Play a decent game of Jeopardy? ▪ 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 surgical operation? ▪ Put away the dishes and fold the laundry? ▪ Translate spoken Chinese into spoken English in real time? ▪ Write an intentionally funny story?  Natural Language ▪ Speech technologies (e.g. Siri) IBM Watson, stomping the opposition at Jeopardy ▪ Automatic speech recognition (ASR) ▪ Text-to-speech synthesis (TTS) ▪ Dialog systems ▪ Language processing technologies ▪ Question answering ▪ Machine translation ▪ Web search ▪ Text classification, spam filtering, etc…  Robotics ▪ Robotics ▪ Part mech. eng. ▪ Part AI ▪ Reality much harder than simulations! ▪ Technologies ▪ Vehicles ▪ Rescue ▪ Soccer! ▪ Lots of automation… ▪ In this class: ▪ We ignore mechanical aspects ▪ Methods for planning ▪ Methods for control Images from UC Berkeley, Boston Dynamics, RoboCup, Google  Logic ▪ Logical systems ▪ Theorem provers ▪ NASA fault diagnosis ▪ Question answering ▪ Methods: ▪ Deduction systems ▪ Constraint satisfaction ▪ Satisfiability solvers (huge advances!) Image from Bart Selman  Decision Making ▪ Applied AI involves many kinds of automation ▪ Scheduling, e.g. airline routing, military ▪ Route planning, e.g. Google maps ▪ Medical diagnosis ▪ Web search engines ▪ Spam classifiers ▪ Automated help desks ▪ Fraud detection ▪ Product recommendations ▪ … Lots more!  Designing Rational Agents ▪ An agent is an entity that perceives and acts. ▪ A rational agent selects actions that maximize its (expected) utility. ▪ 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 Environment types Sensors Agent Percepts ▪ Learning to recognize when and how a new problem can be solved with an existing ? technique Actuators Actions  Pac-Man as an Agent Agent Environment Sensors Percepts ? Actuators Actions