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Textbook CS440 - Introduction to Artificial Intelligence Textbook: S. Russell and P. Norvig. Artificial Intelligence: A Modern Approach. Prentice Hall, 2003, 2nd edition. 1 Course website 2 Course staff Web Site: www.cs.colostate.edu/~cs440 What you can find there: Instructor: Asa Ben-Hur All slides (after or shortly before class) All homework assignments Important announcements Office: 448 Office Hours: TBD, or by appt. Email: asa at cs dot colostate dot edu Teaching Assistant: Doug Hains 3 Workload 4 Grading Written/programming assignments (~6) Programming in python Project Exams (midterm/final) 5 Exams: midterm: 15% final: 25% Assignments: 30% Project: 30% 6 1 Course Outline Search: How to explore the space of potential solutions to a problem. Logic: How to make inferences from stored/ learned knowledge. Learning - How can a computer learn from data. + brief discussion of other topics What is AI? Definitions according to several AI textbooks…. 7 AI: Think Like Humans 8 AI: Think Like Humans “The exciting new effort to make computers think … machines with minds, in the full and literal sense” Haugeland, 1985 “(The automation of) activities that we associate with human thinking, activities such as decision making, problem solving, learning ….” Bellman, 1978 How do humans think? Requires understanding of brain activity (cognitive model). Cognitive science vs. cognitive neuroscience. The available theories do not explain anything resembling human intelligence! 9 AI: Act Like Humans 10 The Turing Test “The art of creating machines that perform functions that require intelligence when performed by people” Kurzweil, 1990 “The study of how to make computers do things at which, at the moment, people are better” Rich and Knight, 1991 When does a system behave intelligently? Turing (1950) Computing Machinery and Intelligence Operational test of intelligence. 11 Test still relevant now, yet might be the wrong question. Requires the successful application of major fields of AI: knowledge representation, reasoning, natural language processing, machine learning 12 2 Role of the Turing Test To articulate a performance goal. To avoid defining intelligence. How significant is the Turing test? Eliza Eliza, the computer therapist (J. Weizenbaum at MIT, 1966). How would you administer it? What would you ask? Would we all agree on the outcome? How close are we? 13 14 AI: Think Rationally Thinking rationally “The study of mental faculties through the use of computational models” Charniak and McDermott, 1985 The logicist approach. Logic: notation and rules of derivation for thoughts Problems: Not all intelligent behavior is mediated by logical deliberation What is the purpose of thinking? What thoughts should I (bother to) have? 15 16 AI: Acting Rationally “A field of study that seeks to explain and emulate intelligent behavior in terms of computation processes” Schalkoff, 1990 “The branch of computer science that is concerned with the automation of intelligent behavior” Luger and Stubblefield Rational behavior: doing the right thing What is AI? Definitions of artificial intelligence: The “right thing” is that which is expected to maximize goal given the available information. Our focus: rational agents, and how to construct them. Systems that think rationally Systems that act like humans Systems that act rationally The definitions vary by: 17 Systems that think like humans Thought processes vs. action Judged according to human standards vs. success according to an ideal concept of intelligence: rationality. 18 3 Tools Lisp Application areas The traditional AI language Prolog Logic programming: fundamentally different! Planning: What to do when. Computer Vision: Seeing is knowing. Speech Recognition: What words are spoken. Speech Understanding (NLP): What do the words mean. 19 CSU AI Faculty Darrell Whitley Adele Howe Ross Beveridge Bruce Draper Charles Anderson Asa Ben-Hur 20 AI in everyday life Genetic algorithms Planning Computer vision (face recognition) Computer vision (biomimetics) Machine learning / computational neuroscience Applications of machine learning in bioinformatics AI systems we use on a daily basis: Scanning documents (OCR). Talking with customer service or your cellphone (voice recognition, NLP). Applying for a loan. Online map services. Computer games (chess etc.) Web searches 21 22 AI Systems The Sojourner Mars rover (1996) Deep Space 1 Deep Blue DARPA grand challenge 2005: a 130 mile race of driverless cars in the mojave desert. AI planner controls space probe - NASA 1999. Defeats Kasparov, Chess Grand Master - IBM 1997 Sandstorm The entries from CMU pictures from: http://www.grandchallenge.org/ The Sojourner rover 23 Highlander 24 4 Mundane Versus Expert Tasks Mundane Expert The Stanford winning entry: Stanley Identifying objects in an image Answering a question Picking up an arbitrary object Chess Medical diagnosis Configuring computer hardware (circuit layout) Special purpose robots picture from http://stanfordracing.org 25 AI: How Close Are We to the Goal? Military systems, space probes… Machine learning: Medical diagnosis, fault detection… Planning: Foundations of AI Expert systems: Philosophy: Logic, reasoning, rationality. Mathematics: Logic, computability, tractability Psychology: understanding how humans think and act. Neuroscience: how do brains process information? Economics: theory of rational decisions, game theory. Computer Engineering: building the hardware and software that make AI Speech recognition, adaptive control… Computer Vision: 26 Manufacturing, medicine, security… Linguistics: how to deal with language … 27 Foundations of AI: Mathematics Logic has limits The leap to formal science needs Computation Logic Probability Algorithms Symbolic Inference Quantifying “Maybe” Hilbert: “Truth of any logical proposition regarding natural numbers … may be proven using logic.” Godel’s Incompleteness Theorem: In 1931, the Czech-born mathematician Kurt Gödel demonstrated that within any given branch of mathematics, there would always be some propositions that couldn't be proven either true or false using the rules and axioms ... of that mathematical branch itself. You might be able to prove every conceivable statement about numbers within a system by going outside the system in order to come up with new rules an axioms, but by doing so you'll only create a larger system with its own unprovable statements. The implication is that all logical system of any complexity are, by definition, incomplete; each of them contains, at any given time, more true statements than it can possibly prove according to its own defining set of rules. * Key Mathematical Questions: 28 Just how far does logic take us? What is computable? *From Jones and Wilson, An Incomplete Education, http://www.miskatonic.org/godel.html 29 30 5 Beware of combinatorics! “Solvable in Principle”: little help in practice Beware of intractability… Foundations of AI: Neuroscience Use ideas from neuroscience to design computer architectures that “learn”. Considering all possibilities often leads to correct, but intractable, algorithms. Intractable means exponential time to solution. NP-Complete Problems Class of intractable problems One View: AI proposes imperfect, but practical, algorithms to solve NP-Complete problems. Artist’s depiction of a neural network 31 A Brief History of AI The beginning of AI Early enthusiasm A dose of reality 1952 - 1969 1966 - 1974 - Progress was slower than expected: Unrealistic predictions. - Some systems lacked scalability: combinatorial explosion - Fundamental limitations on techniques and representations: Minsky and Papert (1969) Perceptrons. Knowledge is power 1969 - 1979 - Domain-specific systems - expert systems - MYCIN: medical diagnosis of blood infections using ~450 rules. AI becomes an industry AI becomes a science http://en.wikipedia.org/wiki/Neural_network 32 Primary Areas of AI 1943 - 1956 1950: Alan Turing’s “Computing Machinery and Intelligence” The term “AI” proposed at the Dartmouth workshop (1956) http://www.bitspin.net/images/neuron.jpg Abstraction as an artificial neural network 1980 – 1988 1988 - … Knowledge Representation Automated Reasoning Game Playing Planning Machine Learning Search and Optimization Computer Vision Robotics Natural Language Processing - Neats vs. scruffies 33 34 6