Marvin L. Minsky

Total Page:16

File Type:pdf, Size:1020Kb

Marvin L. Minsky Marvin Minsky is Toshiba Professor of Media Arts and Sciences, and Professor of Electrical Engineering and Computer Science, at the Mas- sachusetts Institute of Technology. His research has led to both theoretical and practical advances in artificial intelligence, cognitive psychology, RI Lecture Series neural networks, and the theory of Turing Ma- chines and recursive functions. (In 1961 he solved Emil Post's problem of "Tag", and showed that any computer 4623 Wean Hall • 4:30 - 6:30 pm can be simulated by a machine with only two registers and instruc- tions to increment, decrement and jump on zero.) He has made Tuesday, January 25, & Thursday, January 27, 2005 major contributions in the domains of symbolic graphical descrip- tion, computational geometry, knowledge representation, computa- tional semantics, machine perception, symbolic and connectionist learning. He has also been involved with many studies of advanced technologies for space exploration. Professor Minsky was also one of the pioneers of intelligence-based mechanical robotics and telepresence. He designed and built some of the first mechanical hands with tactile sensors, visual scanners, and their software and computer interfaces. He also influenced many robotic projects outside of MIT, and designed and built the Marvin L. Minsky first LOGO "turtle." In 1951 he built the first randomly wired neural network learning machine (called SNARC, for Stochastic Neural-Analog Reinforce- ment Computer), based on the reinforcement of simulated synaptic Massachusetts Institute of Technology transmission coefficients. When a Junior Fellow at Harvard, he invented and built the first Confocal Scanning Microscope, an optical instrument with unprecedented resolution and image quality. Since the early 1950s, Marvin Minsky has worked on using compu- tational ideas to characterize human psychological processes, as well as working to endow machines with intelligence. In 1959, Minsky and John McCarthy founded what became the MIT Artifi- cial Intelligence Laboratory, and his long tenure as its co-director Computers, Emotions placed his imprint upon the entire field of Artificial Intelligence. His seminal 1961 paper, "Steps Towards Artificial Intelligence" surveyed and analyzed all of what had been done before, and set forth the major problems of that infant discipline. The 1963 paper, "Matter, Mind, and Models" addressed the problem of making self- and Common Sense aware machines. In "Perceptrons", 1969, he and Seymour Papert characterized the capabilities and limitations of loop-free learning and pattern recognition machines. In "A Framework for Represent- ing Knowledge" (1974) he put forth a model of knowledge repre- Our present-day robots can help us assemble automobiles in factories-yet none of them yet can make a bed, or sentation to account for many phenomena in cognition, language clean your house or baby-sit. Programs now solve very complex equations, and vanquish the best human players understanding, and visual perception. These representations, called of chess-yet still can't do what most children can do-such as understand a first-grade story. "frames," inherited their variable assignments from previously defined frames, and are often considered to be an early form of Why could computers work at such expert levels before we could make them do everyday things that seem to us object oriented programming. so much easier? That's because each specialized program needs only to exploit some small body of knowledge In the early 1970s, Minsky and Papert began formulating a theory that only applies to a small, special world. Commonsense thinking may seem less profound, yet it applies to called The Society of Mind which combined insights from develop- mental child psychology and their experience with research on much wider realms. Artificial Intelligence. The Society of Mind proposes that intelli- Many people are convinced that minds have special qualities-like feelings, goals, and consciousness-that no gence is not the product of any singular mechanism, but comes from the managed interaction of a diverse variety of resourceful machine could ever possess. However, I'll argue that these differences come not from the nature of our machines- agents. They argued that such diversity is necessary because differ- but from certain constraints we've imposed on them since the earliest days of programming. This lecture will ent tasks require fundamentally different mechanisms; this trans- forms psychology from a fruitless quest for a few "basic" principles suggest some ways to remove those restrictions, and make our computers much more resourceful. Once they're into a search for mechanisms that a mind could use to manage the equipped with more commonsense knowledge then, instead of blaming them for their faults, we'll be able to tell interaction of many diverse elements. them what we want. Bits and pieces of this theory emerged in papers through the 70s Prospective students are encouraged to read Professor Minsky's online draft "The Emotion Machine" and early 80s. Papert turned his energies to applying these new ideas to transforming education while Minsky continued to work (http://web.media.mit.edu/~minsky/) and are also encourage to obtain a copy of "The Society of Mind". primarily on the theory. In 1985, he published "The Society of Mind," a book in which 270 interconnected one-page ideas reflect the structure of the theory itself. Each page either proposes one such mechanism ‚ to account for some psychological phenomena ‚ Host: Matt Mason or addresses a problem introduced by some proposed solution of another page. Marvin Minsky will be visiting Carnegie Mellon for the week of January 23-28. Since the publication of "The Society of Mind," Minsky has contin- His office location will be Newell-Simon Hall 4223. ued to develop the theory in several directions. He is currently working on a new book, "The Emotion Machine," describing the For appointments, please contact Jean Harpley at 8-3802 or [email protected]. roles played by feelings, goals, emotions, and conscious thoughts in terms of processes that motivate and regulate the activities within our personal societies of mind..
Recommended publications
  • A Framework for Representing Knowledge Marvin Minsky MIT-AI Laboratory Memo 306, June, 1974. Reprinted in the Psychology of Comp
    A Framework for Representing Knowledge Marvin Minsky MIT-AI Laboratory Memo 306, June, 1974. Reprinted in The Psychology of Computer Vision, P. Winston (Ed.), McGraw-Hill, 1975. Shorter versions in J. Haugeland, Ed., Mind Design, MIT Press, 1981, and in Cognitive Science, Collins, Allan and Edward E. Smith (eds.) Morgan-Kaufmann, 1992 ISBN 55860-013-2] FRAMES It seems to me that the ingredients of most theories both in Artificial Intelligence and in Psychology have been on the whole too minute, local, and unstructured to account–either practically or phenomenologically–for the effectiveness of common-sense thought. The "chunks" of reasoning, language, memory, and "perception" ought to be larger and more structured; their factual and procedural contents must be more intimately connected in order to explain the apparent power and speed of mental activities. Similar feelings seem to be emerging in several centers working on theories of intelligence. They take one form in the proposal of Papert and myself (1972) to sub-structure knowledge into "micro-worlds"; another form in the "Problem-spaces" of Newell and Simon (1972); and yet another in new, large structures that theorists like Schank (1974), Abelson (1974), and Norman (1972) assign to linguistic objects. I see all these as moving away from the traditional attempts both by behavioristic psychologists and by logic-oriented students of Artificial Intelligence in trying to represent knowledge as collections of separate, simple fragments. I try here to bring together several of these issues by pretending to have a unified, coherent theory. The paper raises more questions than it answers, and I have tried to note the theory's deficiencies.
    [Show full text]
  • Mccarthy As Scientist and Engineer, with Personal Recollections
    Articles McCarthy as Scientist and Engineer, with Personal Recollections Edward Feigenbaum n John McCarthy, professor emeritus of com - n the late 1950s and early 1960s, there were very few people puter science at Stanford University, died on actually doing AI research — mostly the handful of founders October 24, 2011. McCarthy, a past president (John McCarthy, Marvin Minsky, and Oliver Selfridge in of AAAI and an AAAI Fellow, helped design the I Boston, Allen Newell and Herbert Simon in Pittsburgh) plus foundation of today’s internet-based computing their students, and that included me. Everyone knew everyone and is widely credited with coining the term, artificial intelligence. This remembrance by else, and saw them at the few conference panels that were held. Edward Feigenbaum, also a past president of At one of those conferences, I met John. We renewed contact AAAI and a professor emeritus of computer sci - upon his rearrival at Stanford, and that was to have major con - ence at Stanford University, was delivered at the sequences for my professional life. I was a faculty member at the celebration of John McCarthy’s accomplish - University of California, Berkeley, teaching the first AI courses ments, held at Stanford on 25 March 2012. at that university, and John was doing the same at Stanford. As – AI Magazine Stanford moved toward a computer science department under the leadership of George Forsythe, John suggested to George, and then supported, the idea of hiring me into the founding fac - ulty of the department. Since we were both Advanced Research Project Agency (ARPA) contract awardees, we quickly formed a close bond concerning ARPA-sponsored AI research and gradu - ate student teaching.
    [Show full text]
  • Arxiv:2106.11534V1 [Cs.DL] 22 Jun 2021 2 Nanjing University of Science and Technology, Nanjing, China 3 University of Southampton, Southampton, U.K
    Noname manuscript No. (will be inserted by the editor) Turing Award elites revisited: patterns of productivity, collaboration, authorship and impact Yinyu Jin1 · Sha Yuan1∗ · Zhou Shao2, 4 · Wendy Hall3 · Jie Tang4 Received: date / Accepted: date Abstract The Turing Award is recognized as the most influential and presti- gious award in the field of computer science(CS). With the rise of the science of science (SciSci), a large amount of bibliographic data has been analyzed in an attempt to understand the hidden mechanism of scientific evolution. These include the analysis of the Nobel Prize, including physics, chemistry, medicine, etc. In this article, we extract and analyze the data of 72 Turing Award lau- reates from the complete bibliographic data, fill the gap in the lack of Turing Award analysis, and discover the development characteristics of computer sci- ence as an independent discipline. First, we show most Turing Award laureates have long-term and high-quality educational backgrounds, and more than 61% of them have a degree in mathematics, which indicates that mathematics has played a significant role in the development of computer science. Secondly, the data shows that not all scholars have high productivity and high h-index; that is, the number of publications and h-index is not the leading indicator for evaluating the Turing Award. Third, the average age of awardees has increased from 40 to around 70 in recent years. This may be because new breakthroughs take longer, and some new technologies need time to prove their influence. Besides, we have also found that in the past ten years, international collabo- ration has experienced explosive growth, showing a new paradigm in the form of collaboration.
    [Show full text]
  • WHY PEOPLE THINK COMPUTERS CAN't Marvin
    WHY PEOPLE THINK COMPUTERS CAN'T Marvin Minsky, MIT First published in AI Magazine, vol. 3 no. 4, Fall 1982. Reprinted in Technology Review, Nov/Dec 1983, and in The Computer Culture, (Donnelly, Ed.) Associated Univ. Presses, Cranbury NJ, 1985 Most people think computers will never be able to think. That is, really think. Not now or ever. To be sure, most people also agree that computers can do many things that a person would have to be thinking to do. Then how could a machine seem to think but not actually think? Well, setting aside the question of what thinking actually is, I think that most of us would answer that by saying that in these cases, what the computer is doing is merely a superficial imitation of human intelligence. It has been designed to obey certain simple commands, and then it has been provided with programs composed of those commands. Because of this, the computer has to obey those commands, but without any idea of what's happening. Indeed, when computers first appeared, most of their designers intended them for nothing only to do huge, mindless computations. That's why the things were called "computers". Yet even then, a few pioneers -- especially Alan Turing -- envisioned what's now called "Artificial Intelligence" - or "AI". They saw that computers might possibly go beyond arithmetic, and maybe imitate the processes that go on inside human brains. Today, with robots everywhere in industry and movie films, most people think Al has gone much further than it has. Yet still, "computer experts" say machines will never really think.
    [Show full text]
  • The Qualia Manifesto (C) Ken Mogi 1998 [email protected] the Qualia Manifesto Summar
    The Qualia Manifesto (c) Ken Mogi 1998 [email protected] http://www.qualia-manifesto.com The Qualia Manifesto Summary It is the greatest intellectual challenge for humanity at present to elucidate the first principles behind the fact that there is such a thing as a subjectiveexperience. The hallmark of our subjective experiences is qualia. It is the challenge to find the natural law behind the neural generation of qualia which constitute the percepts in our mind, or to go beyond the metaphor of a "correspondence" between physical and mental processes. This challenge is necessary to go beyond the present paradigm of natural sciences which is based on the so-called objective point of view of description. In order to pin down the origin of qualia, we need to incorporate the subjective point of view in a non-trivial manner. The clarification of the nature of the way how qualia in our mind are invoked by the physical processes in the brain and how the "subjectivity" structure which supports qualia is originated is an essential step in making compatible the subjective and objective points of view. The elucidation of the origin of qualia rich subjectivity is important not only as an activity in the natural sciences, but also as a foundation and the ultimate justification of the whole world of the liberal arts. Bridging the gap between the two cultures (C.P.Snow) is made possible only through a clear understanding of the origin of qualia and subjectivity. Qualia symbolize the essential intellectual challenge for the humanity in the future.
    [Show full text]
  • A Programming and Problem-Solving Seminar
    Dcccm bcr 1983 Report No. STAN-B-83-990 A Programming and Problem-Solving Seminar bY John D. Hobby and Donald E. Knuth Department of Computer Science Stanford University Stanford, CA 94305 . -_ -_y-- _ - Computer Science Department A PROGRAMMING AND PROBLEM-SOLVING SEMINAR bY John D. Hobby and Donald E. Knuth This report contains edited transcripts of the discussions held in Stanford’s course CS204, Problem Seminar, during autumn quarter 1982. Since the topics span a large range of ideas in computer science, and since most of the important research paradigms and programming paradigms were touched on during the discussions, these notes may be ‘of interest to graduate students of computer science at other universities, as well as to their professors and to professional people in the “real world.” The present report is the fifth in a series of such transcripts, continuing the tradi- tion established in STAN-CS-77-606 (Michael J. Clancy, 1977), STAN-CS-79-707 (Chris Van’ Wyk, 1979), STAN-CS-81-863 (Allan A. Miller, 1981), STAN-CS-83-989 (Joseph S. Weening, 1983). The production of this report was partially supported by National Science Foundation grant MCS-8300984. Table of Contents Index to problems in the five seminar transcripts . 3 Cast of characters . 4 Intro due tion . ..*.................................. 5 Problem l-Bulgarian Solitaire ........................................................ 6 October 5 ........................................................................ 6 October 7 .......................................................................
    [Show full text]
  • Transforming Ad Hoc EDA to Algorithmic EDA
    Transforming Ad Hoc EDA to Algorithmic EDA Jason Cong Chancellor’s Professor, UCLA Director, Center for Domain-Specific Computing 1 The Early Years 蔡高中學 成功大學 麻省理工 學 院 Choikou Middle School National Cheng Kung University MIT Macau Taiwan USA 1952 1956 1958 2 Graduate Study at MIT (1958 – 1963) ▪ MS thesis: A Study in Machine-Aided Learning − A pioneer work in distant learning ▪ Advisor: Ronald Howard 3 Graduate Study at MIT ▪ PhD thesis: “Some Memory Aspects of Finite Automata” (1963) ▪ Advisor: Dean Arden − Professor of EE, MIT, 1956-1964 − Involved with Whirlwind Project − Also PhD advisor of Jack Dennis ▪ Jack was PhD advisor of Randy Bryant -- another Phil Kaufman Award Recipient (2009) 4 Side Story: Dean Arden’s Visit to UIUC in 1992 I am glad that I have better students than you 5 Side Story: Dean Arden’s Visit to UIUC in 1992 I feel blessed that I had a better advisor than all of you 6 Two Important Books in Computer Science in 1968 ▪ The Art of Computer Programming, Vol. 1, Fundamental Algorithms, Donald E. Knuth, 1968 ▪ Introduction to Combinatorial Mathematics, C. L. Liu, 1968 7 Sample Chapters in “Introduction to Combinatorial Mathematics” ▪ Chapter 3: Recurrence Relations ▪ Chapter 6: Fundamental Concepts in the Theory of Graphs ▪ Chapter 7: Trees, Circuits, and Cut-sets ▪ Chapter 10: Transport Networks ▪ Chapter 11: Matching Theory ▪ Chapter 12: Linear Programming ▪ Chapter 13: Dynamic Programming 8 Project MAC ▪ Project MAC (Project on Mathematics and Computation) was launched 7/1/1963 − Backronymed for Multiple Access Computer,
    [Show full text]
  • The Logic of Qualia 1 Introduction
    The Logic of Qualia Drew McDermott, 2014-08-14 Draft! Comments welcome! Do not quote! Abstract: Logic is useful as a neutral formalism for expressing the contents of mental representations. It can be used to extract crisp conclusions regarding the higher- order theory of phenomenal consciousness developed in (McDermott, 2001, 2007). A key aspect of conscious perceptions is their connection to the distinction between appearance and reality. Perceptions must often be corrected. To do so requires that the logic of perception be able to represent the logical structure of judgment events, that is, to include the formulas of the logic as objects to be reasoned about. However, there is a limit to how finely humans can examine their own represen- tations. Terms representing primary and secondary qualities seemed to be locked, so that the numbers (or levels of neural activation) that are their essence are not directly accessible. Humans feel a need to invoke “intrinsic,” “nonrelational” prop- erties of many secondary qualities — their qualia — to “explicate” how we compare and discriminate among them, although this is not actually how the comparisons are accomplished. This model of qualia explains several things: It accounts for the difference between “normal” and “introspective” access to a perceptual module in terms of quotation. It dissolves Jackson’s knowledge argument by explaining what Mary learns as a fictional but undoubtable belief structure. It makes spectrum in- version logically impossible by providing a degree of freedom between the physical structure of the brain and the representations it contains that redescribes putative cases of spectrum inversion as alternative but equivalent ways of mapping physical states to representational states.
    [Show full text]
  • Russell & Norvig, Chapters
    DIT411/TIN175, Artificial Intelligence Russell & Norvig, Chapters 1–2: Introduction to AI RUSSELL & NORVIG, CHAPTERS 1–2: INTRODUCTION TO AI DIT411/TIN175, Artificial Intelligence Peter Ljunglöf 16 January, 2018 1 TABLE OF CONTENTS What is AI? (R&N 1.1–1.2) What is intelligence? Strong and Weak AI A brief history of AI (R&N 1.3) Notable AI moments, 1940–2018 “The three waves of AI” Interlude: What is this course, anyway? People, contents and deadlines Agents (R&N chapter 2) Rationality Enviroment types Philosophy of AI Is AI possible? Turing’s objections to AI 2 WHAT IS AI? (R&N 1.1–1.2) WHAT IS INTELLIGENCE? STRONG AND WEAK AI 3 WHAT IS INTELLIGENCE? ”It is not my aim to surprise or shock you – but the simplest way I can summarize is to say that there are now in the world machines that can think, that learn, and that create. Moreover, their ability to do these things is going to increase rapidly until — in a visible future — the range of problems they can handle will be coextensive with the range to which human mind has been applied.” by Herbert A Simon (1957) 4 STRONG AND WEAK AI Weak AI — acting intelligently the belief that machines can be made to act as if they are intelligent Strong AI — being intelligent the belief that those machines are actually thinking Most AI researchers don’t care “the question of whether machines can think… …is about as relevant as whether submarines can swim.” (Edsger W Dijkstra, 1984) 5 WEAK AI Weak AI is a category that is flexible as soon as we understand how an AI-program works, it appears less “intelligent”.
    [Show full text]
  • Tlie Case of Artificial Intelligence and Some Historical Parallels
    -------- Judith V. Grabiner -------- Partisans and Critics of a New Science: Tlie Case of Artificial Intelligence and Some Historical Parallels Everywhere in our society-at the supermarket, in the stock market, in mathematics courses-we see the presence of the computer. We are told that we are entering a new age, that of the Computer Revolution, and that the field known as Artificial Intelligence is at the forefront of that revolution. Its practitioners have proclaimed that they will solve the age­ old problems of the nature of human thought and of using technology to build a peaceful and prosperous world. These claims have provoked considerable controversy. The field known as AI is, of course, vast, but the subject matters that have fueled the most debate are these: computer systems that exhibit behaviors that, if exhibited by people, should be called "intelligent"; and "expert" computer systems that, though not exhibiting a broad range of such behaviors, can, because of speed and size of memory and built-in knowledge, outperform humans at specific tasks. The chief implications of research in these areas, according to its advocates, and the claims most viewed with alarm by the research's critics are that AI has a new way of understanding the nature of human beings and their place in the universe and that the "technology" of task-oriented AI-sometimes called expert systems, sometimes knowledge engineering-will be able to solve the outstanding problems facing man and society. Critics of AI enthusiasts perceive these claims as wrong: as based, first, on assumptions about the nature of man and society that are inadequate at best and dangerously misleading at worst; and, second, as overly op­ timistic about the feasibility of finding technical solutions to human prob­ lems.
    [Show full text]
  • Marvin Minsky & Websites ………………………
    Chancellor's Distinguished Fellows Program 2005-2006 Selective Bibliography UC Irvine Libraries Marvin Lee Minsky Prepared by: Julia Gelfand Applied Sciences & Engineering Librarian [email protected] Table of Contents Books & Edited Works 1 PhD Dissertation ……………………….. 2 Chapters in Books …………………... 2 Translations ………………………………... 3 Journal Articles 4 Conference Proceedings & Government Reports 6 Inventions & Patents 6 Filmography, Media & Computer Files. 7 Interviews 8 Honors, Awards. Societies & Corporate Affiliations 8 About Marvin Minsky & Websites ………………………... 9 Books & Edited Works (call numbers provided for UCI Library holdings) Emotion Machine: Commonsense Thinking, Artificial Intelligence and the Future of the Human Mind. New York: Simon & Schuster. Forthcoming September 2006. ISBN: 0743276639. The Turing Option: A Novel, (1992). Science Fiction Thriller with Harry Harrison. New York: Warner Books. Call Number: PS 3558 A667 T88 1992 Perceptrons: An Introduction to Computational Geometry, (1988, 1969). With Seymour Papert. Cambridge, MA: MIT Press. Call Number: Q327 M55 1988 The Society of Mind, (1986). New York: Simon & Schuster. CD ROM Version released, Voyager, 1996. Call Number: BF 431 M553 1986 1 Robotics: The First Authoritative Report from the Ultimate High-Tech Frontier, (1985). Garden City, NY: Anchor Press/Doubleday. Call Number: TJ211 R557 1985 Catalog of the Artificial Intelligence Memoranda of the MIT AI Lab, (1982). New York: Comtex Scientific Corporation. Call Number: SRLF D0008100281. Includes the Artificial Intelligence memos on microfiche. Artificial Intelligence, (1974). With Seymour Papert. Condon Lectures. Eugene, OR: Oregon State System of Higher Education. Call Number: Q335 M56 Semantic Information Processing, (1968). Cambridge, MA: MIT Press. Call Number: Q335.5 M5 Computation: Finite and Infinite Machines (1967). Englewood Cliffs, NJ: Prentice Hall.
    [Show full text]
  • Philip Wadler
    Racket is my Mjolnir ����������������� ����������������� ��������������� � ��������� ���������������������������������������������� � ��������� ���������������������������������������������� � ��������������������������������������������� ����������� � ��������� ���������������������������������������������� � ��������������������������������������������� ����������� � Well...���������������������������������� � ������������������������������������� � ������������������������������������������������� � ��������� ���������������������������������������������� � ��������������������������������������������� ����������� � Well...���������������������������������� � ������������������������������������� � ������������������������������������������������� ����������������������������������������������������� � ����������������������������������������������� �������������������������������������������������� ������������������������������������������������������������������� ����������������������������������������������������������������������� ������������������������������������������������������������������������ �������������������������������������������������������������������� ����������������������������������������������������������������������� �������������������������������������������������������������������������� ��������������������������������������������������������������������������� ������������������������������������������������������ choronological order from the 1970s � �����������������������������������������������
    [Show full text]