Download ppt - 人工智慧 50 年 By Shang-Sheng Jeng 思索指南 Why not in traditional chinese character? 什麼是智慧 ? IQ (Intelligence Quotient, 智慧商數 ) EQ (Emotional Quotient, 情緒商數

人工智慧 50 年

By Shang-Sheng Jeng

思索指南 Why not in traditional chinese character? 什麼是智慧 ? IQ (Intelligence Quotient, 智慧商數 ) EQ (Emotional Quotient, 情緒商數 ) SQ (Spirit Quotient, 靈性商數 ) AQ (Adversity Quotient, 逆境商數 ) MQ (Moral Quotient, 道德商數 )

思索指南 CQ (Creative Quotient, 創意商數 ) FQ (Financial Quotient, 理財商數 ) LQ (Lucky Quotient, 幸運商數 ) Nature Intelligence, Human Intelligence Vs.

Artificial Intelligence “ 心事浩茫連廣宇，於無聲處聽驚雷。”

《無題》魯迅

目錄 Prologue 為什麼談論這個題目 ? 人工智慧的史前史人工智慧近代發展的平台人工智慧的誕生– 1956 Dartmouth Confere

nce 人工智慧的前 50 年

目錄人工智慧的大師 21 世紀的人工智慧新方向人工智慧對未來的影響延伸閱讀 Epilogue

Prologue

“Change and Competition is the

Essence of this Real World”

"The Only Constant in Life is Change!“

An Old Dog’s Feeling

Competition comes from Civilization Conflict-- War of Value and

Lifestyle (media industry, cultural industry) Military Conflict-- Global Governance and

Nation Building (military industry and energy industry)

Financial Conflict-- Scarcity and Divide, Hidden Power of Capitalism (financial industry and trust industry)

孫子兵法孫子兵法 : : 兵勢第五兵勢第五

善戰者 , 求之于勢 , 不責于人 .

“ 先處戰地而待敵者佚，

後處戰地而趨戰者勞。”

孫子兵法虛實篇

The Power to defineThe Battlefield

"ye shall know the truth, and the truth

shall make you free."

John 8:32

為什麼談論這個題目 ? The Trends of 5Ms, 5Cs and 4Ps The Future of Civilization Transition

My Observation:5Ms, 5Cs and 4Ps Development Path

by Shang-Sheng Jeng,

It originated around 1994 at MIC III

5Ms: Free Flow for Globalization Material Merchandize Manpower Money Message (information and command)

5Ms: Free Flow for Globalization The Competition Platform The Competition Factor The Competition Infrastructure The Competition of Global Governance

5 Cs Theory Computer Communication Content Consumerization Control

Computer Devices Calculation Processing Application Services Agent

Communication Local Area Network Wide Area Network Metropolitan Area Network Wireless Network Satellite Network Broadcasting network P2P Network

Communication Home Network Body Network Wireless Sensor Network Extraterrestrial Network

Content Information Production Information Representation Information Search Information Retrieval Information Distribution Information Storage Information Exchange

Consumerization Mass Momentum Power Networked Effect Procsumer Phenomena M form Society, Web 2.0, Google style,… We are here now, 1995 ~ 2020

Control Automation Intelligence Sensors Environmental Surveillance Robotics, Digital Warriors, Unmanned Arial

Vehicle, DARPA the Grand Challenge, DARPA the Urban Challenge

Control September 14, 2006 The Air Force has ann

ounced "Reaper" has been chosen as the name for the MQ-9 unmanned aerial vehicle, the Air Force's first hunter-killer UAV.

Control: C4KISR (pronounced C4-Kisser)

C4ISR + Kill ( 擊殺 ) = C4KISR C4ISR: 指揮 , 管制 , 通訊 , 資訊 , 情報 , 監視 , 偵蒐

Find, track and precisely identify targets Dynamically command and control weapon

s and sensors Share information

4 Place-- Digitization and Networking

Working Place Movable Place (now)

Walking On a Moving Machine

Home Place (will in) Ubiquitous (Infrastructure)

Passions of Information Engineers Push Cross-Industry Competition Computer and Computer enabled Services

Industry (Moore’s Law) Communication and Connectivity Industry

(RFC, de facto approach) Content (Information search, Yahoo, Googl

e), Publication (YouTube, Wikipedia), Education (Open Courseware) and Entertainment (Napster) Industry

Passions of Information Engineers Push Cross-Industry Competition

Consumer Industry (Digitalized, mp3, mp4, digital camera and Networktized, Gmail, Google Earth,…)

Passions of Information Engineers Push “Institution Revolution”

E-Government E-Commerce E-Community E-X,…

The Future of Civilization Transition

The Agriculture Civilization The Industry Civilization

The Mechanical Industry Civilization The Electronic Industry Civilization

The Information Civilization

The Driving Force in Civilization Transition

Energy Technology Knowledge

What is AI? The ability of a

computer or other machine to perform those activities that are normally thought to require intelligence.

What is AI? (AAAI)

“The scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."

What is AI? (John McCarthy, November 24, 2004) It is the science and engineering of making

intelligent machines, especially intelligent computer programs.

It is related to the similar task of using computers to understand human intelligence, but AI does not have to confine itself to methods that are biologically observable.

Branches of AI (John McCarthy) logical AI, search, pattern recognition, re

presentation, inference, common sense knowledge and reasoning, learning from experience, planning, epistemology, epistemology, heuristics, genetic programming

What is AI? (Dr. Dobbs)

AI divides roughly into two schools of thought: “Conventional AI” and “Computational Intelligence”.

Conventional AI Conventional AI mostly involves methods

now classified as machine learning, characterized by formalism and statistical analysis. This is also known as symbolic AI, logical AI, neat AI and Good Old Fashioned Artificial Intelligence (GOFAI).

Conventional AI Methods include: Expert systems: apply reasoning capabilities to re

ach a conclusion. An expert system can process large amounts of known information and provide conclusions based on them.

Case based reasoning (Roger Shank) Bayesian networks Behavior based AI: a modular method building AI

systems by hand. (Rodney Brooks)

Computational Intelligence Computational Intelligence involves iterati

ve development or learning (e.g. parameter tuning e.g. in connectionist systems). Learning is based on empirical data and is associated with non-symbolic AI, scruffy AI and soft computing.

Computational Intelligence Methods include: Neural networks: systems with very strong pattern

recognition capabilities. Fuzzy systems: techniques for reasoning under u

ncertainty, have been widely used in modern industrial and consumer product control systems. (Lotfi A. Zadeh)

Computational Intelligence Evolutionary computation: applies biologically ins

pired concepts such as populations, mutation and survival of the fittest to generate increasingly better solutions to the problem. These methods most notably divide into evolutionary algorithms (e.g. genetic algorithms) and swarm intelligence (e.g. ant algorithms) (Gerardo Beni and Jing Wang ) .

Strong AI vs. Weak AI (wikipedia)

Strong AI vs. Weak AI debates the supposition that some forms of artificial intelligence can truly reason and solve problems.

Strong AI supposes that it is possible for machines to become sapient, or self-aware, but may or may not exhibit human-like thought processes. Weak AI makes no such claim and denies this possibility. The term strong AI was originally coined by John Searle.

人工智慧的史前史 Intelligence Study from: Mythology Philosophy Psychology Mathematics Computer Engineering

人工智慧的史前史 Philosophy of AI Can we build intelligent machines?

If we do, how will we know they’re intelligent?

Should we build intelligent machines? If we do, how should we treat them… …and how will they treat us?

人工智慧的史前史

Information of this section copied

From wikipedia “AI History”

人工智慧的史前史 Greek myths of Hephaestus and Pygmalion incor

porate the idea of intelligent robots. In the 5th century BC, Aristotle invented syllogistic

logic, the first formal deductive reasoning system. Ramon Llull, Spanish theologian, invented paper

"machines" for discovering nonmathematical truths through combinations of words from lists in the 13th century.

人工智慧的史前史 By the 15th century and 16th century, clock

s, the first modern measuring machines, were first produced using lathes. Clockmakers extended their craft to creating mechanical animals and other novelties.

Rabbi Judah Loew ben Bezalel of Prague is said to have invented the Golem, a clay man brought to life (1580).

人工智慧的史前史 Early in the 17th century, René Descartes

proposed that bodies of animals are nothing more than complex machines.

Many other 17th century thinkers offered variations and elaborations of Cartesian mechanism.

Thomas Hobbes published Leviathan, containing a material and combinatorial theory of thinking.

人工智慧的史前史 Wilhelm Schickard created the first mechan

ical calculating machine in 1623, Blaise Pascal created the second mechanical and first digital calculating machine (1642).

人工智慧的史前史 Gottfried Leibniz improved the earlier machi

nes, making the Stepped Reckoner to do multiplication and division (1673). He also invented the binary system and evisioned a universal calculus of reasoning (Alphabet of human thought) by which arguments could be decided mechanically.

人工智慧的史前史 The 18th century saw a profusion of mecha

nical toys, including the celebrated mechanical duck of Jacques de Vaucanson and Wolfgang von Kempelen's phony chess-playing automaton, The Turk (1769).

Mary Shelley published the story of Frankenstein; or the Modern Prometheus (1818).

人工智慧的史前史 George Boole developed a binary algebra

(Boolean algebra) representing (some) "laws of thought."

Charles Babbage & Ada Lovelace worked on programmable mechanical calculating machines.

人工智慧的史前史 In the first years of the 20th century Bertrand Rus

sell and Alfred North Whitehead published Principia Mathematica, which revolutionized formal logic.

Russell, Ludwig Wittgenstein, and Rudolf Carnap lead philosophy into logical analysis of knowledge.

Karel Čapek's play R.U.R. (Rossum's Universal Robots)) opens in London (1923). This is the first use of the word "robot" in English.

人工智慧的史前史 In 1931 Kurt Gödel showed that sufficiently power

ful consistent formal systems permit the formulation of true theorems that are unprovable by any theorem-proving machine deriving all possible theorems from the axioms. To do this he had to build a universal, integer-based programming language, which is the reason why he is sometimes called the "father of theoretical computer science". Since human mathematicians are able to "see" the truth of Goedel's theorems, AIs were deemed inferior by certain philosophers.

人工智慧的史前史 In 1941 Konrad Zuse built the first working progra

m-controlled computers. Warren Sturgis McCulloch and Walter Pitts publis

h "A Logical Calculus of the Ideas Immanent in Nervous Activity" (1943), laying foundations for artificial neural networks.

Arturo Rosenblueth, Norbert Wiener and Julian Bigelow coin the term "cybernetics" in a 1943 paper. Wiener's popular book by that name published in 1948.

人工智慧的史前史 Game theory which would prove invaluable in the

progress of AI was introduced with the 1944 paper, Theory of Games and Economic Behavior by mathematician John von Neumann and economist Oskar Morgenstern.

Vannevar Bush published As We May Think (The Atlantic Monthly, July 1945) a prescient vision of the future in which computers assist humans in many activities.

人工智慧的史前史的補充

人工智慧的史前史的補充 The Legend of the Brazen Head

Roger Bacon was an English Franciscan monk in the 13th Century.

Roger Bacon made a head of brass. It had the power to speak, and was expected to become an oracle, truthfully answering questions about the future. However, left in the care of Bacon's assistant while the Friar slept, it spoke only three times, first saying 'Time is', then, after a long interval, 'Time was' and finally 'Time is past'. After this, it fell to the floor, broke, and never spoke again.

人工智慧的史前史的補充 Daedalus was the very clever man in Greek

mythology who could talk to animals. He built the labyrinth for the Minotaur in Crete.

Daedalus fell out of favour with Minos and was imprisoned; he fashioned wings of wax and feathers for himself and for his son Icarus and escaped to Sicily.

人工智慧的史前史的補充Computer Engineering Abacus (7000 years old) Pascaline: mechanical adder & substractor (Pasc

al; mid 1600’s) Leibniz added multiplication, 1694

Analytic Engine: universal computation; never completed (ideas: addressable memory, stored programs, conditional jumps) Charles Babbage (1792-1871), Ada Lovelace

人工智慧的史前史的補充 Computer Engineering Heath Robinson: digital electronic computer for cr

acking codes Alan Turing 1940, England

Z-3: first programmable computer Konrad Zuse 1941, Germany

ABC: first electronic computer John Atanasoff 1940-42, US

ENIAC: first general-purpose, electronic, digital computer John Mauchy & John Eckert

人工智慧近代發展的平台 David Hilbert Kurt Gödel Alan Turing Lugwig Wittgenstein Alonso Church John Louis von Neumann

“Wir mussen wissen,wir werden wissen.”

( 我們要知道 ;我們將知道 )

David Hilbert

David Hilbert The 23 Problems: He put forth a most

influential list of “23 unsolved problems” at the International Congress of Mathematicians in Paris in 1900. This is generally reckoned the most successful and deeply considered compilation of open problems ever to be produced by an individual mathematician.

David Hilbert In 1920 he proposed explicitly a research project

(in metamathematics, as it was then termed) that became known as “Hilbert's program”. He wanted mathematics to be formulated on a solid and complete logical foundation. He believed that in principle this could be done, by showing that: all of mathematics follows from a correctly-chosen finite sy

stem of axioms; and that some such axiom system is provably consistent.

David Hilbert Problem 2: The compatibility of the arithmet

ical axioms Problem 10: Determination of the solvability

of a diophantine equation

David Hilbert 算術公理的相容性：在數學中，公理訂出法

則，依這些法則來從事數學運算。公理系統則是一堆公理的集合，所以必須是彼此相容才有意義。希爾伯特便想要讓各個公理系統是彼此相容的。希爾伯特提出用形式主義的證明論方法加以解決。但 1931 年哥德爾的 " 不完備定理“ 加以否定。 1936 年根茨用超限歸納法證明了算術公理的無矛盾性。而數學的相容性問題至今未解決。

David Hilbert 丟番圖方程的可解性判別，求出一個整係數

方程的整數根，稱為丟番圖方程可解。希爾伯特問：是否能用有限步構成的一般算法判斷一個丟番圖方程的可解？ 1970 年，蘇聯數學家馬蒂塞維奇在美國數學家戴維斯、普特南、羅賓遜工作基礎上證明了希爾伯特所期望的一般算法不能實現。

Kurt Gödel Gödel demonstrated that any non-contradictory fo

rmal system, which was comprehensive enough to include at least arithmetic, cannot demonstrate its completeness by way of its own axioms. In 1931 his “incompleteness theorem” showed that Hilbert's grand plan was impossible as stated. The second point cannot in any reasonable way be combined with the first point, as long as the axiom system is genuinely finitary.

Kurt Gödel “Die Vollstandigkeit der Axiome des

logischen Funktionenkalkuls”, Monatshefte fur Mathematik und Physik 37, p.349-360, 1930

“Uber formal unentscheidbare Satze der Principia Mathematica und verwandter Systeme I”, in I. Monatshefte fur Mathematik und Physik 38, p.173-198. 1931

Kurt Gödel “On formally undecidable

propositions of Principia Mathematica and related systems I” Kurt Gödel, 1931

Kurt Gödel Gödel's first incompleteness theorem, perh

aps the single most celebrated result in mathematical logic, states that: For any consistent formal theory that proves basi

c arithmetical truths, an arithmetical statement that is true but not provable in the theory can be constructed. That is, any theory capable of expressing elementary arithmetic cannot be both consistent and complete.

Kurt Gödel Gödel's second incompleteness theorem ca

n be stated as follows: For any formal theory T including basic arithmetic

al truths and also certain truths about formal provability, T includes a statement of its own consistency if and only if T is inconsistent.

Alan Turing “

On Computable Numbers, With An Application To The Entscheidungsproblem” By A. M. Turing, Proceedings of the London Mathematical Society, Series 2, 42 (1936), pp 230 – 265, 12 November, 1936

“Computing Machinery and Intelligence” By Alan M. Turing, MIND: A Quarterly Review Of Psychology And Philosophy, VOL. LIX. No.236. October, 1950

Alan Turing: Turing Machine Turing machines are extremely basic abstract

symbol-manipulating devices which, despite their simplicity, can be adapted to simulate the logic of any computer that could possibly be constructed. They were described in 1936 by Alan Turing.

Though they were intended to be technically feasible, Turing machines were not meant to be a practical computing technology, but a thought experiment about the limits of mechanical computation; thus they were not actually constructed.

Alan Turing: Turing Machine Studying their abstract properties yields many insi

ghts into computer science and complexity theory. A Turing machine that is able to simulate any othe

r Turing machine is called a Universal Turing machine (UTM, or simply a universal machine).

A more mathematically-oriented definition with a similar "universal" nature was introduced by Alonzo Church, whose work on lambda calculus intertwined with Turing's in a formal theory of computation known as the Church–Turing thesis.

Alan Turing: Turing Machine The thesis states that Turing machines inde

ed capture the informal notion of effective method in logic and mathematics, and provide a precise definition of an algorithm or 'mechanical procedure'.

Alan Turing: Halting Problem In computability theory the halting problem is a d

ecision problem which can be informally stated as follows: Given a description of a program and a finite input, decide

whether the program finishes running or will run forever, given that input.

Alan Turing proved in 1936 that a general algorithm to solve the halting problem for all possible program-input pairs cannot exist. We say that the halting problem is undecidable over Turing machines.

Alan Turing Turing Test Total Turing Test The CAPTCHA (Completely Automated

Turing Test to Tell Computers and Humans Apart) Project:

The Turing Test Created by Alan

Turing in 1950 A test designed to

assess a machine’s intelligence

No machine has consistently passed the test

How does it work? A human interrogator is connected to both a

human and a machine, neither of which can be seen.

The interrogator asks questions to both the machine and human, trying to determine which is the machine.

The machine and human subject both try to convince the interrogator that they are human

If the interrogator incorrectly identifies the computer as human, the machine has passed the test and is considered intelligent

How does it work?

The Turing Test-- Chinese Room argument

John R. Searle, “Minds, Brains, and Programs” 1980

Chinese Room argument The Chinese Room argument, devised by John S

earle, is an argument against the possibility of true artificial intelligence.

The argument centers on a thought experiment in which someone who knows only English sits alone in a room following English instructions for manipulating strings of Chinese characters, such that to those outside the room it appears as if someone in the room understands Chinese.

Chinese Room argument The argument is intended to show that while suita

bly programmed computers may appear to converse in natural language, they are not capable of understanding language, even in principle.

Searle argues that the thought experiment underscores the fact that computers merely use syntactic rules to manipulate symbol strings, but have no understanding of meaning or semantics.

Chinese Room argument Searle's argument is a direct challenge to p

roponents of Artificial Intelligence, and the argument also has broad implications for functionalist and computational theories of meaning and of mind. As a result, there have been many critical replies to the argument.

The Loebner Prize for Artificial Intelligence The Loebner Prize for artificial intelligence ( AI ) is

the first formal instantiation of a Turing Test. The test is named after Alan Turing the brilliant Br

itish mathematician. Among his many accomplishments was basic research in computing science.

In 1950, in the article Computing Machinery and Intelligence which appeared in the philosophy journal Mind, Alan Turing asked the question "Can a Machine Think?"

The Loebner Prize for artificial intelligence He answered in the affirmative, but a centra

l question was: "If a computer could think, how could we tell?"

Turing's suggestion was, that if the responses from the computer were indistinguishable from that of a human, the computer could be said to be thinking. This field is generally known as natural language processing.

The Loebner Prize for artificial intelligence In 1990 Hugh Loebner agreed with The Cambridg

e Center for Behavioral Studies to underwrite a contest designed to implement the Turing Test.

Dr. Loebner pledged a Grand Prize of $100,000 and a Gold Medal (pictured above) for the first computer whose responses were indistinguishable from a human's.

Such a computer can be said "to think." Each year an annual prize of $2000 and a bronze medal is awarded to the most human-like computer.

Total Turing Test Stevan Harnad’s main contribution to the T

T debate has been the proposal of the Total Turing Test (TTT), which is, like the TT, an indistinguishability test but one that requires the machines to respond to all of our inputs rather than just verbal ones. Evidently the candidate machine for the TTT is a robot with sensorimotor capabilities.

Lugwig Wittgenstein “Tractatus Logico-Philosophicus” “Logical Investigation”

Alonso Church Church–Turing thesis In computability theory the Church–Turing thesis (also kno

wn as Church's thesis, Church's conjecture and Turing's thesis) is a hypothesis about the nature of computers, such as a digital computer or a human with a pencil and paper following a set of rules. The thesis claims that any calculation that is possible can be performed by an algorithm running on a computer, provided that sufficient time and storage space are available. The thesis cannot be mathematically proven; it is sometimes proposed as a physical law or as a definition.

The thesis was first proposed by Stephen C. Kleene in 1943

Alonso Church Alonzo Church, "An unsolvable problem of

elementary number theory", American Journal of Mathematics, 58 (1936), pp 345 - 363

Alonzo Church, "A note on the Entscheidungsproblem", Journal of Symbolic Logic, 1 (1936), pp 40 - 41.

Alonso Church: Lambda calculus Lambda calculus In mathematical logic and computer science, lam

bda calculus, also λ-calculus, is a formal system designed to investigate function definition, function application, and recursion.

It was introduced by Alonzo Church and Stephen Cole Kleene in the 1930s; Church used lambda calculus in 1936 to give a negative answer to the Entscheidungsproblem.

Lambda calculus can be used to define what a computable function is.

Alonso Church: Lambda calculus The question of whether two lambda calculus expr

essions are equivalent cannot be solved by a general algorithm.

This was the first question, even before the halting problem, for which undecidability could be proved.

Lambda calculus has greatly influenced functional programming languages, such as Lisp, ML and Haskell.

Alonso Church: Lambda calculus Lambda calculus can be called the smallest unive

rsal programming language. It consists of a single transformation rule (variable

substitution) and a single function definition scheme.

Lambda calculus is universal in the sense that any computable function can be expressed and evaluated using this formalism.

It is thus equivalent to the Turing machine formalism.

Alonso Church: Lambda calculus However, lambda calculus emphasizes the

use of transformation rules, and does not care about the actual machine implementing them.

It is an approach more related to software than to hardware.

John Louis von Neumann John von Neumann and Oskar Morgenster

n. 1944. “Theory of Games and Economic Behavior”, Princeton Univ. Press, Princeton NJ.

John Louis von Neumann The von Neumann architecture is a computer

design model that uses a single storage structure to hold both instructions and data.

Postwar von Neumann concentrated on the development of “the Institute for Advanced Studies (IAS) computer” and its copies around the world. His work with the Los Alamos group continued and he continued to develop the synergism between computers capabilities and the needs for computational solutions to nuclear problems related to the hydrogen bomb.

John Louis von Neumann John von Neumann, 30 June 1945. First Draft of a

Report on the EDVAC, Contract No. W-670-ORD-492, Moore School of Electrical Engineering, Univ. of Penn., Philadelphia. Reprinted (in part) in Randell, Brian. 1982. “Origins of Digital Computers: Selected Papers”, Springer-Verlag, Berlin Heidelberg, pp. 383-392.

John von Neumann, 1946. "The Principles of Large-Scale Computing Machines", reprinted in Ann. Hist. Comp., Vol. 3, No. 3, pp. 263-273.

John Louis von Neumann John von Neumann, 1958. “The Computer

and the Brain”, Yale Univ. Press, New Haven.

John von Neumann and Arthur W. Burks. 1966. “Theory of Self-Reproducing Automata”, Univ. of Illinois Press, Urbana IL.

John Louis von Neumann In a remarkable 1956 letter, the great logician

Kurt Gödel asked the famous mathematician and computer pioneer John von Neumann “whether certain computational problems could be solved without resorting to brute force search”. In so doing, he foreshadowed the P versus NP problem, one of the great unanswered questions of contemporary mathematics and theoretical computer science.

人工智慧的誕生– 1956 Dartmouth Conference workshop for 2 months Dartmouth AI Project Proposal We propose that a 2 month, 10 man study of artificial

intelligence be carried out during the summer of 1956 at Dartmouth College in Hanover, New Hampshire.

The study is to proceed on the basis of the conjecture that every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it. An attempt will be made to find how to make machines use language, form abstractions and concepts, solve kinds of problems now reserved for humans, and improve themselves.

J. McCarthy et al.; Aug. 31, 1955

人工智慧的誕生– 1956 Dartmouth Conference

“Computers and Thought” Edited by Edward A. Feigenbaum and Julian Feldman, 1963

“Computers and Thought” Pt. 1 Artificial Intelligence

Sect. 1 Can a Machine Think? Computing Machinery and Intelligence Sect. 2 Machines That Play Games Chess-Playing Programs and the Problem of

Complexity Some Studies in Machine Learning Using the

Game of Checkers


Sect. 3 Machines That Prove Mathematical Theorems

Empirical Explorations with the Logic Theory Machine: A Case Study in Heuristics Realization of a Geometry-Theorem Proving

Machine Empirical Explorations of the Geometry-Theorem

Proving Machine


Sect. 4 Two Important Applications Summary of a Heuristic Line Balancing Procedure A Heuristic Program That Solves Symbolic Integration

Problems in Freshman Calculus Sect. 5 Question-answering Machines Baseball: An Automatic Question Answerer Inferential Memory as the Basis of Machines Which

Understand Natural Language


Sect. 6 Pattern Recognition Pattern Recognition by Machine A Pattern-Recognition Program That Generates,

Evaluates, and Adjusts its Own Operators

“Computers and Thought” Pt. 2 Simulation of Cognitive Processes

Sect. 1 Problem-Solving GPS, A Program That Simulates Human

Thought Sect. 2 Verbal Learning and Concept Formation The Simulation of Verbal Learning Behavior Programming a Model of Human Concept

Formulation

“Computers and Thought” Pt. 2 Simulation of Cognitive Processes

Sect. 3 Decision-making under Uncertainty Simulation of Behavior in the Binary Choice

Experiment A Model of the Trust Investment Process Sect. 4 Social Behavior A Computer Model of Elementary Social

Behavior

“Computers and Thought” Pt. 3 Survey of Approaches and Attitudes

Attitudes Toward Intelligent Machines Steps Toward Artificial Intelligence

Pt. 4 Bibliography: A Selected Descriptor-Indexed Bibliography to the Literature on Artificial Intelligence

人工智慧的前 50 年

Information of this section copied

From wikipedia “AI History”

人工智慧的前 50 年 1950 Alan Turing (who introduced “the universal T

uring machine” in 1936) published "Computing Machinery and Intelligence", which suggested the “Turing test” as a way of operationalizing a test of intelligent behavior.

1950 Claude Shannon published a detailed analysis of chess playing as search.

1950 Isaac Asimov published his Three Laws of Robotics.

人工智慧的前 50 年 1951 The first working AI programs were written i

n 1951 to run on the Ferranti Mark I machine of the University of Manchester: a checkers-playing program written by Christopher Strachey and a chess-playing program written by Dietrich Prinz.

1952-1962 Arthur Samuel (IBM) wrote the first game-playing program, for checkers (draughts), to achieve sufficient skill to challenge a world champion. His first checkers-playing program was written in 1952, and in 1955 he created a version that learned to play (Samuel 1959).

人工智慧的前 50 年 1956 John McCarthy coined the term "artificial

intelligence" as the topic of the Dartmouth Conference, the first conference devoted to the subject.

1956 The first demonstration of the Logic Theorist (LT) written by Allen Newell, J.C. Shaw and Herbert Simon (Carnegie Institute of Technology, now CMU). This is often called the first AI program, though Samuel's checkers program also has a strong claim.

人工智慧的前 50 年 1957 The General Problem Solver (GPS) demons

trated by Newell, Shaw and Simon. 1958 John McCarthy (MIT) invented the Lisp prog

ramming language. 1958 Herb Gelernter and Nathan Rochester (IBM)

described a theorem prover in geometry that exploits a semantic model of the domain in the form of diagrams of "typical" cases.

人工智慧的前 50 年 1958 Teddington Conference on the Mechanizatio

n of Thought Processes was held in the UK and among the papers presented were John McCarthy's Programs with Common Sense, Oliver Selfridge's Pandemonium, and Marvin Minsky's Some Methods of Heuristic Programming and Artificial Intelligence.

Late 1950s, early 1960s Margaret Masterman and colleagues at University of Cambridge design semantic nets for machine translation.

人工智慧的前 50 年 1960s Ray Solomonoff lays the foundations of a

mathematical theory of AI, introducing universal Bayesian methods for inductive inference and prediction.

1961 James Slagle (PhD dissertation, MIT) wrote (in Lisp) the first symbolic integration program, SAINT, which solved calculus problems at the college freshman level.

1962 First industrial robot company, Unimation, founded.

人工智慧的前 50 年 1963 Thomas Evans' program, ANALOGY,

written as part of his PhD work at MIT, demonstrated that computers can solve the same analogy problems as are given on IQ tests.

1963 Edward Feigenbaum and Julian Feldman published “Computers and Thought”, the first collection of articles about artificial intelligence.

人工智慧的前 50 年 1963 Leonard Uhr and Charles Vossler pub

lished "A Pattern Recognition Program That Generates, Evaluates, and Adjusts Its Own Operators", which described one of the first machine learning programs that could adaptively acquire and modify features and thereby overcome the limitations of simple perceptrons of Rosenblatt

人工智慧的前 50 年 1964 Danny Bobrow's dissertation at MIT (t

echnical report #1 from MIT's AI group, Project MAC), shows that computers can understand natural language well enough to solve algebra word problems correctly.

1964 Bertram Raphael's MIT dissertation on the SIR program demonstrates the power of a logical representation of knowledge for question-answering systems.

人工智慧的前 50 年 1965 J. Alan Robinson invented a mechanical pro

of procedure, the Resolution Method, which allowed programs to work efficiently with formal logic as a representation language.

1965 Joseph Weizenbaum (MIT) built ELIZA (program), an interactive program that carries on a dialogue in English language on any topic. It was a popular toy at AI centers on the ARPANET when a version that "simulated" the dialogue of a psychotherapist was programmed.

人工智慧的前 50 年 1966 Ross Quillian (PhD dissertation, Carnegie In

st. of Technology, now CMU) demonstrated semantic nets.

1966 First Machine Intelligence workshop at Edinburgh: the first of an influential annual series organized by Donald Michie and others.

1966 Negative report on machine translation kills much work in Natural language processing (NLP) for many years.

人工智慧的前 50 年 1967 Dendral program (Edward Feigenbaum, Jos

hua Lederberg, Bruce Buchanan, Georgia Sutherland at Stanford University) demonstrated to interpret mass spectra on organic chemical compounds. First successful knowledge-based program for scientific reasoning.

1968 Joel Moses (PhD work at MIT) demonstrated the power of symbolic reasoning for integration problems in the Macsyma program. First successful knowledge-based program in mathematics.

人工智慧的前 50 年 1968 Richard Greenblatt (programmer) at

MIT built a knowledge-based chess-playing program, MacHack, that was good enough to achieve a class-C rating in tournament play.

1969 Stanford Research Institute (SRI): Shakey the Robot, demonstrated combining animal locomotion, perception and problem solving.

人工智慧的前 50 年 1969 Roger Schank (Stanford) defined con

ceptual dependency model for natural language understanding. Later developed (in PhD dissertations at Yale University) for use in story understanding by Robert Wilensky and Wendy Lehnert, and for use in understanding memory by Janet Kolodner.

人工智慧的前 50 年 1969 Yorick Wilks (Stanford) developed the

semantic coherence view of language called Preference Semantics, embodied in the first semantics-driven machine translation program, and the basis of many PhD dissertations since such as Bran Boguraev and David Carter at Cambridge.

人工智慧的前 50 年 1969 First International Joint Conference on Artifi

cial Intelligence (IJCAI) held at Stanford. 1969 Marvin Minsky and Seymour Papert publish

Perceptrons, demonstrating previously unrecognized limits of a simple form of neural nets. This may have helped trigger the AI winter of the 1970s, a failure of confidence and funding for AI. Nevertheless significant progress in the field continued.

人工智慧的前 50 年 Early 1970s Jane Robinson and Don Walker esta

blished an influential Natural Language Processing group at SRI.

1970 Jaime Carbonell (Sr.) developed SCHOLAR, an interactive program for computer assisted instruction based on semantic nets as the representation of knowledge.

1970 Bill Woods described Augmented Transition Networks (ATN's) as a representation for natural language understanding.

人工智慧的前 50 年 1970 Patrick Winston's PhD program, ARCH, at

MIT learned concepts from examples in the world of children's blocks.

1971 Terry Winograd's PhD thesis (MIT) demonstrated the ability of computers to understand English sentences in a restricted world of children's blocks, in a coupling of his language understanding program, SHRDLU, with a robot arm that carried out instructions typed in English.

人工智慧的前 50 年 1972 Prolog programming language developed by

Alain Colmerauer. 1973 The Assembly Robotics Group at University

of Edinburgh builds Freddy Robot, capable of using visual perception to locate and assemble models.

1973 “The Lighthill report” gives a largely negative verdict on AI research in Great Britain and forms the basis for the decision by the British government to discontine support for AI research in all but two universities.

人工智慧的前 50 年 1974 Edward H. Shortliffe's PhD dissertation on th

e MYCIN program (Stanford) demonstrated the power of rule-based systems for knowledge representation and inference in the domain of medical diagnosis and therapy. Sometimes called the first expert system.

1974 Earl Sacerdoti developed one of the first planning programs, ABSTRIPS, and developed techniques of hierarchical planning.

人工智慧的前 50 年 1975 Marvin Minsky published his widely-read an

d influential article on Frames as a representation of knowledge, in which many ideas about schemas and semantic links are brought together.

1975 The Meta-Dendral learning program produced new results in chemistry (some rules of mass spectrometry) the first scientific discoveries by a computer to be published in a referreed journal.

人工智慧的前 50 年 Mid 1970s Barbara Grosz (SRI) established limits

to traditional AI approaches to discourse modeling. Subsequent work by Grosz, Bonnie Webber and Candace Sidner developed the notion of "centering", used in establishing focus of discourse and anaphoric references in Natural language processing.

Mid 1970s David Marr and MIT colleagues describe the "primal sketch" and its role in visual perception.

人工智慧的前 50 年 1976 Douglas Lenat's AM program (Stanford PhD

dissertation) demonstrated the discovery model (loosely-guided search for interesting conjectures).

1976 Randall Davis demonstrated the power of meta-level reasoning in his PhD dissertation at Stanford.

1978 Tom Mitchell, at Stanford, invented the concept of Version Spaces for describing the search space of a concept formation program.

人工智慧的前 50 年 1978 Herbert Simon wins the Nobel Prize in Econ

omics for his theory of bounded rationality, one of the cornerstones of AI known as "satisficing".

1978 The MOLGEN program, written at Stanford by Mark Stefik and Peter Friedland, demonstrated that an object-oriented programming representation of knowledge can be used to plan gene-cloning experiments.

人工智慧的前 50 年 1979 Bill VanMelle's PhD dissertation at Stanford

demonstrated the generality of MYCIN's representation of knowledge and style of reasoning in his EMYCIN program, the model for many commercial expert system "shells".

1979 Jack Myers and Harry Pople at University of Pittsburgh developed INTERNIST, a knowledge-based medical diagnosis program based on Dr. Myers' clinical knowledge.

人工智慧的前 50 年 1979 Cordell Green, David Barstow, Elaine

Kant and others at Stanford demonstrated the CHI system for automatic programming.

1979 The Stanford Cart, built by Hans Moravec, becomes the first computer-controlled, autonomous vehicle when it successfully traverses a chair-filled room and circumnavigates the Stanford AI Lab.

人工智慧的前 50 年 1979 Drew McDermott and Jon Doyle at MI

T, and John McCarthy at Stanford begin publishing work on non-monotonic logics and formal aspects of truth maintenance.

Late 1970s Stanford's SUMEX-AIM resource, headed by Ed Feigenbaum and Joshua Lederberg, demonstrates the power of the ARPAnet for scientific collaboration.

人工智慧的前 50 年 Early 1980s The team of Ernst Dickmanns at Bun

deswehr University Munich builds the first robot cars, driving up to 55 mph on empty streets.

1980s Lisp machines developed and marketed. First expert system shells and commercial applications.

1980 First National Conference of the American Association for Artificial Intelligence (AAAI) held at Stanford.

人工智慧的前 50 年 1981 Danny Hillis designs the connection machin

e, which utilizes Parallel computing to bring new power to AI, and to computation in general. (Later founds Thinking Machines, Inc.)

1982 The Fifth Generation Computer Systems project (FGCS), an initiative by Japan's Ministry of International Trade and Industry, begun in 1982, to create a "fifth generation computer" (see history of computing hardware) which was supposed to perform much calculation utilizing massive parallelism.

人工智慧的前 50 年 1983 John Laird and Paul Rosenbloom, working

with Allen Newell, complete CMU dissertations on Soar (program).

1983 James F. Allen invents the Interval Calculus, the first widely used formalization of temporal events.

Mid 1980s Neural Networks become widely used with the Backpropagation algorithm (first described by Paul Werbos in 1974).

人工智慧的前 50 年 1985 The autonomous drawing program, AARON,

created by Harold Cohen, is demonstrated at the AAAI National Conference (based on more than a decade of work, and with subsequent work showing major developments).

1987 Marvin Minsky published The Society of Mind, a theoretical description of the mind as a collection of cooperating agents. He had been lecturing on the idea for years before the book came out (c.f. Doyle 1983).

人工智慧的前 50 年 1987 Around the same time, Rodney Brook

s introduced the subsumption architecture and behavior-based robotics as a more minimalist modular model of natural intelligence.

1989 Dean Pomerleau at CMU creates ALVINN (An Autonomous Land Vehicle in a Neural Network).

人工智慧的前 50 年 Early 1990s TD-Gammon, a backgammon

program written by Gerry Tesauro, demonstrates that reinforcement (learning) is powerful enough to create a championship-level game-playing program by competing favorably with world-class players.

人工智慧的前 50 年 1990s Major advances in all areas of AI,

with significant demonstrations in machine learning, intelligent tutoring, case-based reasoning, multi-agent planning, scheduling, uncertain reasoning, data mining, natural language understanding and translation, vision, virtual reality, games, and other topics.

人工智慧的前 50 年 1993 Ian Horswill extended behavior-based

robotics by creating Polly, the first robot to navigate using vision and operate at animal-like speeds (1 meter/second).

1993 Rodney Brooks, Lynn Andrea Stein and Cynthia Breazeal started the widely-publicized MIT Cog project with numerous collaborators, in an attempt to build a humanoid robot child in just five years.

人工智慧的前 50 年 1993 ISX corporation wins "DARPA contractor of t

he year" for the Dynamic Analysis and Replanning Tool (DART) which reportedly repaid the US government's entire investment in AI research since the 1950s.

1995 ALVINN steered a car coast-to-coast under computer control for all but about 50 of the 2850 miles. Throttle and brakes, however, were controlled by a human driver.

人工智慧的前 50 年 1995 In the same year, one of Ernst Dickmanns' r

obot cars (with robot-controlled throttle and brakes) drove more than 1000 miles from Munich to Copenhagen and back, in traffic, at up to 120 mph, occasionally executing maneuvers to pass other cars (only in a few critical situations a safety driver took over). Active vision was used to deal with rapidly changing street scenes.

人工智慧的前 50 年 1997 The Deep Blue chess program (IBM) beats t

he world chess champion, Garry Kasparov, in a widely followed match.

1997 First official RoboCup football (soccer) match featuring table-top matches with 40 teams of interacting robots and over 5000 spectators.

1998 Tim Berners-Lee published his Semantic Web Road map paper.

人工智慧的前 50 年 Late 1990s Web crawlers and other AI-based

information extraction programs become essential in widespread use of the World Wide Web.

Late 1990s Demonstration of an Intelligent room and Emotional Agents at MIT's AI Lab.

Late 1990s Initiation of work on the Oxygen architecture, which connects mobile and stationary computers in an adaptive network.

人工智慧的前 50 年 2000 Interactive robopets ("smart toys") become c

ommercially available, realizing the vision of the 18th century novelty toy makers.

2000 Cynthia Breazeal at MIT publishes her dissertation on Sociable machines, describing Kismet (robot), with a face that expresses emotions.

2000 The Nomad robot explores remote regions of Antarctica looking for meteorite samples.

人工智慧的前 50 年 2004 OWL Web Ontology Language W3C

Recommendation (10 February 2004). 2006 The Dartmouth Artificial Intelligence C

onference: The Next 50 Years (AI@50) AI@50 (July 14-16 2006)

2006 Release 1.0 of the OpenCyc top-level ontology engine is released as open source at sourceforge.net.

人工智慧的前 50 年的補充

人工智慧的前 50 年的補充 1997 Microsoft's Office Assistant, part of Of

fice 97, uses AI to offer customized help 2001 The Global Hawk uncrewed aircraft u

ses an AI navigation system to guide it on a 13,000-kilometre journey from California to Australia

人工智慧的前 50 年的補充 1968 The movie 2001: A Space Odyssey b

rought AI to the public’s attention 1965 Fuzzy Set and 1973 Fuzzy Logic by

Lotfi A. Zadeh

人工智慧的前 50 年的補充 Asimov’s Three Laws of Robotics:

A robot may not injure a human being or, through inaction, allow a human being to come to harm.

A robot must obey orders given it by human beings except where such orders would conflict with the First Law.

A robot must protect its own existence as long as such protection does not conflict with the First or Second Law.

人工智慧的前 50 年的補充 “What Computers Can't Do: The Limits of A

rtificial Intelligence” by Hubert L. Dreyfus, 1979

“What Computers Still Can't Do: A Critique of Artificial Reason” by Hubert L. Dreyfus, 1992

Some AI in UK before 1963 (By Jim Doran, 2002)

Turing’s off duty talk at Bletchley Park (ca 1943)

Turing’s National Physical Lab. paper (1947)

The Ratio Club (1949-58) Turing’s Mind paper (1950) Ross Ashby’s “Design for a Brain” (1952 &

1960)

Some AI in UK before 1963 “Faster than Thought” (1953) Grey Walter’s “The Living Brain” and his

pseudo-Turtles (1953) NPL Symposium on “Mechanisation of

Thought Processes” (1958) Michie on Game Learning (1961/3)

Some Earlier ConnectionsLady and Lord Byron

Ada

Anne

Judith

Wilfrid Scawen Blunt

Charles Babbage

Alan TuringB V Bowden’s “Faster

than Thought”

Donald Michie Jack Good

Racehorse Breeding

30s Cambridge Anthony Blunt

W Grey Walter D.G.Champernowne

Victor Rothschild

Max NewmanMichael Swann

Winning ways The Economist, Jan 25th 2007 Brute force will not work in Go. In the past two decades researchers have e

xplored several alternative strategies, from neural networks to general rules based on advice from expert players, with indifferent results. Now, however, programmers are making impressive gains with a technique known as “the Monte Carlo method”.

Artificial Intelligence, With Help From the Humans, NYTimes March 25, 2007 Mr. Bezos figured that what had been useful to A

mazon would be valuable to other businesses, too. The company opened “Mechanical Turk” as a public site in November 2005. Today, there are more than 100,000 “Turk Workers” in more than 100 countries who earn micropayments in exchange for completing a wide range of quick tasks called HITs, for human intelligence tasks, for various companies.

人工智慧的大師 Noam Chomsky Nobert Wiener Allen Newell Herbert A. Simon John McCarthy Marvin Minsky

人工智慧的大師 Noam Chomsky: Syntactic Structures, 1957 Norbert Wiener: Cybernetics: the Control a

nd Communication in the Animal and the Machine, 1965

Herbert Simon: The Sciences of the Artificial, 1969

Alan Newell & Herbert Simon: Human Problem Solving, 1972

人工智慧的大師 Marvin Minsky & Seymour Papert: Perceptr

ons: An Introduction to Computational Geometry, 1969

Marvin Minsky: The Society of Mind, 1985 Marvin Minsky: Emotion Machine, 2006

Emotion Machine (Marvin Minsky)

It argues that, contrary to popular conception, emotions aren't distinct from rational thought; rather, they are simply another way of thinking, one that computers could perform.

My view is that the reason we're so good at things is not that we have the best way but because we have so many ways, so when any one of them fails, you can switch to another way of thinking.

Emotion Machine So instead of thinking of the mind as basically a r

ational process which is distorted by emotion, or colored and made more exciting by emotion -- that's the conventional view -- emotions themselves are different ways to think.

Being angry is a very useful way to solve problems, for instance, by intimidating an opponent or getting rid of people who bother you.

Emotion Machine The theme of the book is really resourcefulness a

nd why are people so much better at controlling the world than animals are? The argument is: because they have far more different ways to think than any competitor.

Emotion Machine Your mind can work on several levels at once so,

when you think about any particular subject, you also can think about the way you've been thinking -- and then use that experience to change yourself.

Similarly, when you admire some teacher or leader, you can try to imitate their ways to think -- instead of just learning the things that they say.

Emotion Machine We often imagine that there's a little person inside

ourselves who makes our important decisions for us.

However, a more useful idea is that you build many different models of yourself for dealing with different situations -- and each of those self-images can add to your resourcefulness.

人工智慧的大師 Donald E. Knuth:

The Art of Computer Programming Donald E. Knuth: Computers & Typesetting Donald E. Knuth:

3:16 Bible Texts Illuminated, 1990 Donald E. Knuth:

Things a Computer Scientist Rarely Talks About, 2001

21 世紀的人工智慧新方向 Semantics and Ontology Study Autonomous Intelligent Robots Nanotechnology & Swarm Intelligence Emotion Machine Bionics and Brain Machine Interface Cognition and Mind Study Computational Thinking of Jeannette Wing

21 世紀的人工智慧新方向 Drugs and Mind Torsion Study Computer Games, Mass Communication a

nd Mass Control (Experience Economy, Virtual Reality 2, Second Life,…)

人工智慧對未來的影響 The Use of Intelligent Products The Construction of Intelligent

Infrastructure The Rise of Machine The Extension of Human Intelligence The Search for “Ultimate Reality”

延伸閱讀 “Emotion Machine” by Marvin Minsky, Publi

shed September 2006 “

Making Robots Conscious of their Mental States” by John McCarthy, 1995 July 24 to July 15, 2002

“How Are We To Know” by Nils Nilsson, January 4, 2006 Version

延伸閱讀 “ 數學巨人哥德爾：關於邏輯的故事” 約

翰．卡斯提 , 維納．德包利 , 究竟 , 2002/12/30

“ 沒有時間的世界” 帕利．尤格拉 , 商周出版 , 2006/07/17

“ 在柏拉圖的天堂裡：普林斯頓高等學術研究院與科學巨人的故事” 約翰．卡斯提為 /著 , 商周出版 , 2005/11/06

延伸閱讀 “ 機器人的進化：人工智慧與機器人學的新

世紀” 彼得曼瑟 , 費斯德魯修 / 著 , 商周出版 , 2002/05/14

“ 我們都是機器人：人機合一的大時代” 羅德尼布魯克斯 / 著 , 究竟 , 2003/04/29

“ 機器人：由機器邁向超越人類心智之路” 漢斯摩拉維克 / 著 , 台灣商務 , 2004/05/01

延伸閱讀 “Artificial Intelligence: A Modern Approach”

(Second Edition), by Stuart Russell and Peter Norvig

Epilogue

"The Best Way to Predict the Future Is to Invent It"

Alan Kay, Apple Fellow, Apple Computer

Thursday, September 23, 19934:00 P.M. Pacific Daylight Time (2300 GMT) PARC Auditorium

“I’d Rather Burn outThan Rust out.”

“ 寧願燒盡，不願銹壞”

馬偕博士

George Leslie MacKay(1844-1901)