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Artificial Intelligence 人工智能

Artificial Intelligence 人工智能. Xiu-jun GONG (Ph. D) School of Computer Science and Technology, Tianjin University gongxj@tju.edu.cn http://cs.tju.edu.cn/faculties/gongxj/course/ai/. About the instructor. Name: Xiu-jun GONG ( 宫秀军 ) Work experiences

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Artificial Intelligence 人工智能

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  1. Artificial Intelligence人工智能 Xiu-jun GONG (Ph. D) School of Computer Science and Technology, Tianjin University gongxj@tju.edu.cn http://cs.tju.edu.cn/faculties/gongxj/course/ai/

  2. About the instructor • Name: Xiu-jun GONG (宫秀军) • Work experiences • 2006/05-Now： Associate Professor, Tianjin University • 2003/05-2006/03: Research fellow, Nara Institute of Science and Technology • 2003/02-2003/05: Visiting fellow, Institute for Inforcomm Research （I2R), Singapore • 2002/07-2002/12: Research fellow, National University of Singapore • 1999/09-2002/07: Ph. D candidate, Institute of Computing, CAS • Research interests • Data mining: algorithms, standards, and systems • Bioinformatics: gene regulatory network, SNP identifications • Medical informatics: secure, privacy-preserving data mining, medical data integration and sharing framework

  3. About the course • Text book • Artificial Intelligence-A New Synthesis, Nils J. Nillson • Artificial Intelligence: A Modern Approach, Stuart Russell and Peter Norvig • Artificial Intelligence: Structures and Strategies for Complex Problems Solving (Fourth Edition), George F. Luger • Grading • Attendance: 10% • Project & Assignment: 20% • Final exam: 70% • Office hour: any time upon pre-appointment before final exam, 25-B-1208 • Web site: http://cs.tju.edu.cn/~gongxj/course/ai

  4. Outline to the introduction • AI definitions • AI history • AI research • Problems • Approaches • Tools • AI Applications • AI resources

  5. What is AI To make computers think ...machineswith minds (Haugeland, 1985) The study of the computations that make it possible to perceive, reason … (Winston,1992) Machines that perform functions that require intelligence when performed by people (Kurzweil, 1990) The automation of intelligent behavior (Luger, 1993) Thinking humanlyThinking rationally Acting humanlyActing rationally

  6. What is AI (cont.) • AI is a branch of cs that is concerned with the automation of intelligent behavior—Luger • Data structures, algorithms, and language and programming techniques. • What is the “intelligent behavior”? • Think (act) humanly • Think (act) rationally • Can machines think? • Can: Now or someday; theoretically or actually • Machine: biological body (made of proteins), mechanical device? • Think: media? Living cells or physical symbolic systems

  7. Some synonyms Intelligent machine, intelligent system, intelligent agent, computational intelligence, synthetic intelligence Performed by google trends on 7th, Oct, 2008

  8. Beyond the definitions • The definitions differ for different people, different contexts, and different historical periods (see the AI history) • AI has always been more concerned with expanding the capacities of computer science than with defining its limits • AI is the interdisciplinary study of computer science including psychology, philosophy, neuroscience, cognitive science, linguistics, ontology, operations research, economics, control theory, probability, optimization and logic. Collection of problems and methodologies studied by AI researchers

  9. History of AI research • Precursors • 1943−1956: The birth of AI • 1956−1974: The golden years • 1974−1980: The first AI winter • 1980–1987: Boom • 1987−1993: Bust: the second AI winter • 1993−present: AI ?

  10. Precursors (1) • AI in myth, fiction and speculation

  11. Precursors (2) Al-Jazari's programmable automata Automatons Formal reasoning Computer science

  12. 1943−1956: The birth of AI (2) • Turing's test (1950) -ACT Humanly • Decide whether a machine is intelligent or not If a machine could carry on a conversation (over a teletype) that was indistinguishable from a conversation with a human being, then the machine could be called "intelligent."

  13. 1943−1956: The birth of AI (2) • Dartmouth Summer Research Conference on Artificial Intelligence in 1956 • Marvin Minsky, John McCarthy • Coined the term “AI” • Every aspect of learning or any other feature of intelligence can be so precisely described that a machine can be made to simulate it --a clear statement of the philosophical position of AI research • Presentation of game playing programs and Logic Theorist.

  14. 1956−1974: The golden years (1) • Reasoning as search • Maze problem--backtracking • Combinatorial explosion-- heuristics or "rules of thumb “ • Projects • Simon etc, General Problem Solver (1951) • Herbert Gelernter , Geometry Theorem Prover (1958) • James Slagle, SAINT (Symbolic Automatic Integrator )(1961) • Nils Nilsson , STRIPS(Stanford Research Institute Problem Solver ) (1971)

  15. 1956−1974: The golden years (2) • Natural language • Allow computers to communicate in natural languages--semantic network • STUDENT, solve high school algebra word problems (1964) • ELIZA, rephrasing many of the patient's statements as questions and posing them to the patients (1966) • ALICE: http://www.alicebot.org • Micro-worlds • Marvin Minsky, machine vision They pointed out that in successful sciences were often best understood using simplified models like frictionless planes or perfectly rigid bodies. Much of the research focused on the so-called "blocks world," which consists of colored blocks of various shapes and sizes arrayed on a flat surface .

  16. 1974−1980: The first AI winter (1) • Critiques from across campus (mainly from philosophers) • John Lucas, argued Gödel's incompleteness theorem (a formal system could never see the truth of certain statements, while a human being could) • Hubert Dreyfus, argued that human reasoning actually involved very little "symbol processing" and a great deal of embodied, instinctive, unconscious "know how". • John Searle‘, Chinese Room argument (a program could not be said to "understand" the symbols that it uses ) • Perceptrons and the dark age of connectionism • perceptron may eventually be able to learn, make decisions, and translate languages (Frank Rosenblatt, 1958) • Minsky and Papert's, book Perceptrons.1969

  17. 1974−1980: The first AI winter (2) • The neats: logic, Prolog and expert systems • Logic into AI: McCathy 1958 • Deduction on computers: J. Alan Robinson 1963 • Prolog: Philippe Roussel, Alain Colmerauer, 1972 • Critics: human beings rarely used logic when they solved problems • The scruffies: frames and scripts • Gerald Sussman observed that "using precise language to describe essentially imprecise concepts doesn't make them any more precise." • Minsky noted that many of his fellow "scruffy" researchers were using the same kind of tool: a framework that captures all our common sense assumptions about something. 1975

  18. 1980–1987: Boom (1) • The rise of expert systems (main stream of AI) • MYCIN, 1972, diagnosed infectious blood diseases • XCON (eXpert CONfigurer), 1980, automatically selecting the computer system components based on the customer's requirements • The knowledge revolution • The power of expert systems came from the expert knowledge they contained • Cyc (enCyclopedia), assemble a comprehensive ontology and database of everyday common sense knowledg, Douglas Lenat 1984

  19. 1980–1987: Boom (2) • The revival of connectionism • John Hopfield (associative neural network ,1982) • David Rumelhart (backpropagation) • The money returns • the fifth generation project ($850 million,1982, 10-year program) • “epoch-making computer” • massive parallel processing • Failure in 1992 • Alvey (England, ₤350 )(1983-1987) • Strategic Computing Initiative (DARPA) (1984) PIM/m-1 machine

  20. 1987−1993: the second AI winter • Market changed • Desktop computers from Apple and IBM had been steadily gaining speed and power • Robotics facts—having a body essentially • A machine needs to have a body— it needs to perceive, move, survive and deal with the world • David Marr, AI needed to understand the physical machinery of vision from the bottom up before any symbolic processing took place. • Rodney Brooks, Elephants Don't Play Chess , symbols are not always necessary since "the world is its own best model”. “physical symbol system hypothesis”

  21. 1993−present: AI ? • Deep Blue beats Kasparov (1997) • DARPA grand challenge: Autonomous vehicle navigates across desert. (Urban Challenge next) 2005 • NASA Remote Agent in Deep Space I probe explores solar system • iRobot Roomba automated vacuum cleaner • Automated speech/language systems for airline travel • Usable machine translation thru Google • …?

  22. Advanced Intelligence • Close interactions and coordination between Natural Intelligence and Artificial Intelligence • The frontiers in both Artificial Intelligence and Natural Intelligence • Large-scale Distributed Intelligence and Web Intelligence

  23. China’ s Programs on AI • 国家中长期科学和技术发展规划纲要（2006-2020） • 重点领域及其优先主题 • 传感器网络及智能信息处理 重点开发多种新型传感器及先进条码自动识别、射频标签、基于多种传感信息的智能化信息处理技术，发展低成本的传感器网络和实时信息处理系统，提供更方便、功能更强大的信息服务平台和环境。 • 基础研究： • 脑科学与认知科学 主要研究方究向：脑功能的细胞和分子机理，脑重大疾病的发生发展机理，脑发育、可塑性与人类智力的关系，学习记忆和思维等脑高级认知功能的过程及其神经基础，脑信息表达与脑式信息处理系统，人脑与计算机对话等。

  24. Problems of AI • Deduction, reasoning, problem solving • Knowledge representation • Planning • Learning • Natural language processing • Motion and manipulation • Perception • Social intelligence • Creativity • General intelligence

  25. Approaches to AI Thinking rationally The laws of thought approach Thinking humanly The cognitive approach Acting rationally The rational agent approach Acting humanly The Turing Test approach

  26. Approaches to AI cont. • Symbolism • Cognitive simulation: Psychologism- Herbert Simon and Alan Newell) • Logical AI: Logicism - John McCarthy • "Scruffy" symbolic AI : Computerism, commonsense knowledge bases - Marvin Minsky • Connectionism –Hopfield, Pitts • Neural networks • Actionism –Brooks • Cybernetics and brain simulation

  27. Tools of AI research • Search and optimization • Logic • Probabilistic methods for uncertain reasoning • Classifiers and statistical learning methods • Neural networks • Control theory

  28. Specialized languages • Lisp is a practical mathematical notation for computer programs based on lambda calculus • Prolog is a declarative language where programs are expressed in terms of relations, and execution occurs by running queries over these relations • STRIPS a language for expressing automated planning problem instances. • Planner is a hybrid between procedural and logical languages.

  29. Application domains 机器学习 自然语言处理 专家系统 模式识别 计算机视觉 机器人学 博弈 自动定理证明 自动程序设计 • Machine Learning • Natural Language Processing • Expert System • Patten Recognition • Computer Vision • Robotics • Game Playing • Automatic Theorem Proving • Automatic Programming

  30. Application domains (cont. ) 智能控制 智能决策支持系统 人工神经网络 知识发现和数据挖掘 分布式人工智能 智能代理 智能数据库检索 • Intelligent Control • Intelligent Decision Support System • Artificial Neural Network • Knowledge Discovery in Database & Data Mining • Distributed AI • Intelligent Agent • Intelligent Retrieval from Database

  31. AI resources: Journals (premium) • Artificial Intelligence • Computational Linguistics • IEEE Trans on Pattern Analysis and Machine Intl • IEEE Trans on Robotics and Automation • IEEE Trans on Image Processing • Journal of AI Research • Neural Computation • Machine Learning • Intl Jnl of Computer Vision • IEEE Trans on Neural Networks

  32. AI resources: Journals (leading) • Artificial Intelligence Review • ACM Transactions on Asian Language Information Processing • AI Magazine • Applied Artificial Intelligence • Artificial Intelligence in Medicine • Computational Intelligence • Computer Speech and Language • Expert Systems with Applications: An Intl Jnl • IEEE Trans on Systems, Man, & Cybernetics, Part A & B • Intl Jnl on Artificial Intelligence Tools • Jnl of Experimental & Theoretical AI • Journal of East Asian Linguistics • Knowledge Engineering Review • Machine Translation • Neural Networks • Pattern Recognition • Neurocomputing

  33. AI competitions • Machine Intelligence Prize • Loebner prize • KDD Cup serires

  34. AI resources: Conferences • AAAI: American Association for AI National Conference • CVPR: IEEE Conf on Comp Vision and Pattern Recognition • IJCAI: Intl Joint Conf on AI • ICCV: Intl Conf on Computer Vision • ICML: Intl Conf on Machine Learning • KDD: Knowledge Discovery and Data Mining • KR: Intl Conf on Principles of KR & Reasoning • NIPS: Neural Information Processing Systems • UAI: Conference on Uncertainty in AI • AAMAS: Intl Conf on Autonomous Agents and Multi-Agent Systems • ACL: Annual Meeting of the ACL (Association of Computational Linguistics)

  35. Summary • AI definition • Whatever the definition is, Collection of problems and methodologies studied by AI researchers is an important clue for investigating AI problems • AI history • History is a mirror. AI researchers are getting more intelligent • AI research • Integration of multi-disciplines. Bring AI into practice and reality

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