CS 2710, ISSP 2610 Foundations of Artificial Intelligence introduction
Welcome! Syllabus I wonder if the notes show up here?
What is Artificial Intelligence? 4 views on what makes an AI system (next slide; R&N 1.1) Depends on what matters more in the evaluation Reasoning vs. Behavior [diagram] Human performance vs. Rationality Human model Rational model
4 Views of AI Think like humans Think rationally Act like humans Act rationally
Acting Like Humans? Turing (1950) “Can machines think?” “Can machines behave intelligently?” Operational test for intelligent behavior: the Imitation Game
Player C tries to determine which of A and B are human/computer
Turing Test Suggested the major components of AI: NLP (communicate in language) Knowledge representation (to store what it knows) Automated reasoning (use knowledge to answer questions and draw conclusions) Machine learning (adapt to new circumstances; prediction) Vision Robotics (manipulate objects and move around)
Turing Test Not how we evaluate AI methods; Why? [board + next slide]
Sample Eliza Dialogue Joseph Weizenbaum ~1965 young woman: Men are all alike. eliza: In what way? young woman: They're always bugging us about something specific or other. eliza: Can you think of a specific example? young woman: Well, my boyfriend made me come here. eliza: Your boyfriend made you come here? young woman: He says I'm depressed much of the time. eliza: I am sorry to hear you are depressed. young woman: It's true. I'm unhappy. eliza: Do you think coming here will help you not to be unhappy? young woman: I need some help, that much seems certain .eliza: What would it mean to you if you got some help? young woman: Perhaps I could learn to get along with my mother .eliza: Tell me more about your family .young woman: My mother takes care of me .eliza: Who else in your family takes care of you? young woman: My father …
Thinking Like Humans? For this approach, need a way to determine how humans think Introspection (rejected by researchers as too subjective) Psychological experiments: predicting (based on cognitive theory) and then testing, behavior of human subjects; matched predictions give evidence for the theory Brain imaging – observing the brain in action Cognitive Science: Express a theory as a computer program; input/output behavior matching human behavior is evidence for the theory Computer models from AI and experimental techniques from Psychology; also Neurophysiological evidence incorporated into computational models, e.g. vision
Thinking Like Humans AI and Cognitive Science are now largely distinct research areas [board]
Thinking Rationally?
The “Laws of thought” approach Logicist tradition: Logic: notation and rules of derivation for thoughts Aristotle: what are correct arguments/thought processes? Direct line through mathematics, philosophy, to modern AI Problems: Not all intelligent behavior is mediated by logical deliberation It is difficult to express informal knowledge in logic It is not sufficient: Need a search process to go down fruitful reasoning paths logical systems tend to do the wrong thing in the presence of uncertainty Logic is important in AI; but a pure logicist approach (early AI history) to intelligence is not effective That leaves us with ….
Acting Rationally: Our Basic Framework Getting computers to do the right thing based on their circumstances and what they know. Irrational != insane; irrationality is sub-optimal action Rational != successful; the most rational action may not succeed due to some circumstance beyond our control or due to incomplete knowledge Make the best choice, given the options Rational agents [board]
19401950: 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: Knowledgebased approaches 1969—79: Early development of knowledgebased 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”?
AI applications AI techniques are used in many common applications; just a sample Intelligent user interfaces Search Engines Spell/grammar checkers Context sensitive help systems Medical diagnosis systems Regulating/Controlling hardware devices and processes (e.g, in automobiles) Voice/image recognition (more generally, pattern recognition) Scheduling systems (airlines, hotels, manufacturing) Error detection/correction in electronic communication Program verification / compiler and programming language design Web search engines / Web spiders Web personalization and Recommender systems (collaborative/content filtering) Personal agents Customer relationship management Credit card verification in e-commerce / fraud detection Data mining and knowledge discovery in databases Computer games
What to expect Abstractive thinking/imagination sometimes needed Extreme range of problem domains (as we just saw on the sample of applications). We need to look for frameworks that apply to a hugely diverse range of problem domains. Abstract distinctions abound. Real problem domains are often so complex we need to work with simpler ones, and imagine what would be needed in a realistic domain Not a definitive answer about which method is best; depends on the problem! AI problems are those that we really don’t know how to solve. Otherwise, we would use a direct solution (and it would not be considered AI anymore) Real AI systems are often mixtures of various algorithms/techniques, experimentally determined
Course Topics Four major areas: Problem solving and search. Formulating a search problem, uninformed and informed search; constraint satisfaction, optimization, and adversarial search. Logic and knowledge representation First-order logic; reasoning; knowledge representation schemes Planning Situation calculus, STRIPS, Partial-order planning, GraphPlan and SAT planners Uncertainty and Learning Modeling uncertainty, Bayesian belief networks, decision theory, classification, density estimation
Wrap Up Chapter 1: Chapter 2: You will not be tested on Sections 1.2 and 1.3 (history; foundations). But it’s interesting! Be able to explain the different possible approaches to AI and why AI has settled on the rational action approach Chapter 2: Will be covered on homework 1; Any explicit exam question will be related to its coverage on homework 1