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

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

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

Outline to the introduction AI definitions AI history AI research

Problems Approaches Tools

AI Applications AI resources

What is AITo make computers think ... machines with 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 humanly Thinking rationally

Acting humanly Acting rationally

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

Some synonyms Intelligent machine, intelligent system, intelligent agent,

computational intelligence, synthetic intelligence

Performed by google trends on 7th, Oct, 2008

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

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 ?

Precursors (1) AI in myth, fiction and speculation

Precursors (2)

Al-Jazari's programmable automata

Automatons Formal reasoning

Computer science

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."

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.

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)

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 visionThey 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 .

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

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

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

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

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”

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 …?

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

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。

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

Approaches to AI

Acting rationally

The rational agent approach

Thinking humanly

The cognitive approach

Acting humanly

The Turing Test approach

Thinking rationally

The laws of thought approach

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

Tools of AI research Search and optimization Logic Probabilistic methods for uncertain

reasoning Classifiers and statistical learning

methods Neural networks Control theory

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.

Application domains Machine Learning Natural Language Processing Expert System Patten Recognition Computer Vision Robotics Game Playing Automatic Theorem Proving Automatic Programming

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

Application domains (cont. ) Intelligent Control Intelligent Decision Support

System Artificial Neural Network Knowledge Discovery in

Database & Data Mining Distributed AI Intelligent Agent Intelligent Retrieval from

Database

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

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

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

AI competitions Machine Intelligence Prize

Loebner prize

KDD Cup serires

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)

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