DOCSLIB.ORG

The Wrong Kind of AI? Artificial Intelligence and the Future of Labour Demand

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
The Wrong Kind of AI? Artificial Intelligence and the Future of Labour Demand Cambridge Journal of Regions, Economy and Society 00, 00, 0–0 doi:10.1093/cjres/rsz022 Downloaded from https://academic.oup.com/cjres/advance-article-abstract/doi/10.1093/cjres/rsz022/5680462 by MIT Libraries user on 19 December 2019 The wrong kind of AI? Artificial intelligence and the future of labour demand Daron Acemoglua and Pascual Restrepob aMIT, Cambridge, MA, USA, [email protected] bBoston University, Boston, MA, USA, [email protected] Received on May 30, 2019; editorial decision on October 16, 2019; accepted on November 18, 2019 Artificial intelligence (AI) is set to influence every aspect of our lives, not least the way pro- duction is organised. AI, as a technology platform, can automate tasks previously performed by labour or create new tasks and activities in which humans can be productively employed. Recent technological change has been biased towards automation, with insufficient focus on creating new tasks where labour can be productively employed. The consequences of this choice have been stagnating labour demand, declining labour share in national income, rising inequality and lowering productivity growth. The current tendency is to develop AI in the direction of further automation, but this might mean missing out on the promise of the ‘right’ kind of AI, with better economic and social outcomes. Keywords: automation, artificial intelligence, jobs, inequality, innovation, labour demand, productivity, tasks, technology, wages JEL Classifications:J23, J24 Artificial intelligence (AI) is one of the most we can still shape the direction of AI research promising technologies currently being de- and the future of work. veloped and deployed. Broadly speaking, AI refers to the study and development of “intel- ligent (machine) agents”, which are machines, AI as a technology platform softwares or algorithms that act intelligently Human (or natural) intelligence comprises sev- by recognising and responding to their envir- eral different types of mental activities. These onment.1 There is a lot of excitement, some include simple computation, data processing, hype and a fair bit of apprehension about pattern recognition, prediction, various types of what AI will mean for our security, society problem solving, judgment, creativity, and com- and economy. But a critical question has been munication. Early AI, pioneered in the 1950s by largely overlooked: are we investing in the researchers from computer science, psychology “right” type of AI, the kind with the greatest po- and economics, such as Marvin Minsky, Seymour tential for raising productivity and generating Papert, John McCarthy, Herbert Simon and broad-based prosperity? We do not have a de- Allen Newell, sought to develop machine intel- finitive answer right now—nobody does. But ligence capable of performing all of these dif- this is the right time to ask this question while ferent types of mental activities.2 The goal was © The Author(s) 2019. Published by Oxford University Press on behalf of the Cambridge Political Economy Society. All rights reserved. For permissions, please email: [email protected] Acemoglu and Restrepo Downloaded from https://academic.oup.com/cjres/advance-article-abstract/doi/10.1093/cjres/rsz022/5680462 by MIT Libraries user on 19 December 2019 nothing short of creating truly intelligent ma- Even if we focus on its narrow version, AI chines. Herbert Simon and Allen Newell, for should be thought of as a technology plat- example, claimed in 1958 “there are now in the form—there are many ways AI technology can world machines that think, that learn and that be developed as a commercial or production create. Moreover, their ability to do these things technology, with widely varying applications. is going to increase rapidly until—in a visible This matters greatly because it implies that the future—the range of problems they can handle economic and social consequences of AI tech- will be coextensive with the range to which the nologies are not preordained but depend on human mind has been applied.”3 how we decide to advance and build on this These ambitious goals were soon dashed. AI platform. To some degree, this is true of all clus- came back into fashion in the 1990s, but with ters of technologies, but it is more emphatically a different and more modest ambition: to rep- so for AI.4 To see this, contrast it with a related licate and then improve upon human intelli- but distinct new technology, robotics. Robotics gence in pattern recognition and prediction often makes use of AI and other digital tech- (pre-AI computers were already better than nologies for processing data, but is distin- humans in computation and data processing). guished from other digital technologies by its Many decision problems and activities we rou- focus on interacting with the physical world tinely engage in can be viewed as examples (moving around, transforming, rearranging or of pattern recognition and prediction. These joining objects). Industrial robots are already include recognising faces (from visual data), widespread in many manufacturing industries recognising speech (from auditory data), rec- and in some retail and wholesale establish- ognising abstract patterns in data we are pre- ments. But their economic use is quite specific, sented with, and making decisions on the and centres on automation of narrow tasks, that basis of past experience and current informa- is, substituting machines for certain specific ac- tion. Though there are researchers working on tivities and functions previously performed by “Artificial General Intelligence”, much of the humans.5 research and almost all commercial applica- tions of AI are in these more modest domains referred to as “Narrow AI”—even if the rele- Implications of technology for work vant applications are numerous and varied. The and labour big breakthroughs and the renewed excitement How do new technologies impact the nature of in AI are coming from advances in hardware production and work? Employment and wages and algorithms that enable the processing and of different types of workers? The standard analysis of vast amounts of unstructured data approach, both in popular discussions and in (for example, speech data that cannot be repre- academic writings, presumes that any advance sented in the usual structured ways, such as in that increases productivity (value added per simple, Excel-like databases). Central to this re- worker) also tends to raise the demand for naissance of AI have been methods of machine labour, and thus employment and wages. Of learning (which are the statistical techniques course, technological progress might lead to that enable computers and algorithms to learn, job loss in some sectors. But even when that predict and perform tasks from large amounts happens, the standard narrative goes, other sec- of data without being explicitly programmed) tors will expand and contribute to overall em- and what is called “deep learning” (algorithms ployment and wage growth. Moreover, even if that use multi-layered programs, such as neural technological progress benefits some workers nets, for improved machine learning, statistical more than others and increases inequality, the inference and optimisation). standard approach still predicts that it will Page 2 of 11 The wrong kind of AI? Downloaded from https://academic.oup.com/cjres/advance-article-abstract/doi/10.1093/cjres/rsz022/5680462 by MIT Libraries user on 19 December 2019 tend to raise the labour demand for all types not reduce overall labour demand. A second of workers.6 conclusion is that whether they reduce overall This view is critically underpinned by the labour demand depends on the strength of the way in which the economic impact of new tech- productivity effect. nology is conceptualised—as enabling labour This last observation has important implica- to become more productive in pretty much all tions: contrary to popular claims that the future of the activities and tasks that it performs. Yet, of labour is threatened by “brilliant” new tech- this not only lacks descriptive realism (what nologies, the greater danger for labour comes technology makes labour uniformly more pro- from technology that is not raising productivity ductive in everything?), but may paint an ex- sufficiently. In particular, if new automation cessively rosy picture of the implications of new technologies are not great but just “so-so” (just technologies. Indeed, in such a world Luddites’ good enough to be adopted but not so much concerns about the disruptive and job displacing more productive than the labour they are re- implications of technology would be misplaced, placing), there is a double jeopardy for labour— and they would have smashed all of those ma- there is a displacement effect, taking passed chines in vain. away from labour, but no powerful productivity The reality of technological change is rather gains redressing some of the decline in labour different. Many new technologies—those we demand generated by the displacement effects. call automation technologies—are not intended Is this far-fetched? Not really. We have previ- to increase labour’s productivity, but are ex- ously studied the implications of one of the most plicitly aimed at replacing it by substituting important automation technologies, industrial cheaper capital (machines) in a range of tasks robots.9 Industrial robots are not technologies performed by humans.7 As a result, automa- aimed at increasing labour’s productivity but tion technologies, by displacing workers from are designed to automate tasks that were previ- the tasks they were previously performing, al- ously performed by production workers on the ways reduce the labour’s share in value added. factory floor. The evidence is fairly clear that Put differently, these technologies raise prod- industries where more industrial robots are uctivity by more than wages and employment. introduced experience declines in labour de- They may even reduce overall labour demand mand (especially for production workers) and (and thus reduce wages, employment or both). sizable falls in their labour share.
Load more

Recommended publications
  • John Mccarthy
    JOHN MCCARTHY: the uncommon logician of common sense Excerpt from Out of their Minds: the lives and discoveries of 15 great computer scientists by Dennis Shasha and Cathy Lazere, Copernicus Press August 23, 2004 If you want the computer to have general intelligence, the outer structure has to be common sense knowledge and reasoning. — John McCarthy When a five-year old receives a plastic toy car, she soon pushes it and beeps the horn. She realizes that she shouldn’t roll it on the dining room table or bounce it on the floor or land it on her little brother’s head. When she returns from school, she expects to find her car in more or less the same place she last put it, because she put it outside her baby brother’s reach. The reasoning is so simple that any five-year old child can understand it, yet most computers can’t. Part of the computer’s problem has to do with its lack of knowledge about day-to-day social conventions that the five-year old has learned from her parents, such as don’t scratch the furniture and don’t injure little brothers. Another part of the problem has to do with a computer’s inability to reason as we do daily, a type of reasoning that’s foreign to conventional logic and therefore to the thinking of the average computer programmer. Conventional logic uses a form of reasoning known as deduction. Deduction permits us to conclude from statements such as “All unemployed actors are waiters, ” and “ Sebastian is an unemployed actor,” the new statement that “Sebastian is a waiter.” The main virtue of deduction is that it is “sound” — if the premises hold, then so will the conclusions.
    [Show full text]
  • The Computer Scientist As Toolsmith—Studies in Interactive Computer Graphics
    Frederick P. Brooks, Jr. Fred Brooks is the first recipient of the ACM Allen Newell Award—an honor to be presented annually to an individual whose career contributions have bridged computer science and other disciplines. Brooks was honored for a breadth of career contributions within computer science and engineering and his interdisciplinary contributions to visualization methods for biochemistry. Here, we present his acceptance lecture delivered at SIGGRAPH 94. The Computer Scientist Toolsmithas II t is a special honor to receive an award computer science. Another view of computer science named for Allen Newell. Allen was one of sees it as a discipline focused on problem-solving sys- the fathers of computer science. He was tems, and in this view computer graphics is very near especially important as a visionary and a the center of the discipline. leader in developing artificial intelligence (AI) as a subdiscipline, and in enunciating A Discipline Misnamed a vision for it. When our discipline was newborn, there was the What a man is is more important than what he usual perplexity as to its proper name. We at Chapel Idoes professionally, however, and it is Allen’s hum- Hill, following, I believe, Allen Newell and Herb ble, honorable, and self-giving character that makes it Simon, settled on “computer science” as our depart- a double honor to be a Newell awardee. I am pro- ment’s name. Now, with the benefit of three decades’ foundly grateful to the awards committee. hindsight, I believe that to have been a mistake. If we Rather than talking about one particular research understand why, we will better understand our craft.
    [Show full text]
  • The Dartmouth College Artificial Intelligence Conference: the Next
    AI Magazine Volume 27 Number 4 (2006) (© AAAI) Reports continued this earlier work because he became convinced that advances The Dartmouth College could be made with other approaches using computers. Minsky expressed the concern that too many in AI today Artificial Intelligence try to do what is popular and publish only successes. He argued that AI can never be a science until it publishes what fails as well as what succeeds. Conference: Oliver Selfridge highlighted the im- portance of many related areas of re- search before and after the 1956 sum- The Next mer project that helped to propel AI as a field. The development of improved languages and machines was essential. Fifty Years He offered tribute to many early pio- neering activities such as J. C. R. Lick- leiter developing time-sharing, Nat Rochester designing IBM computers, and Frank Rosenblatt working with James Moor perceptrons. Trenchard More was sent to the summer project for two separate weeks by the University of Rochester. Some of the best notes describing the AI project were taken by More, al- though ironically he admitted that he ■ The Dartmouth College Artificial Intelli- Marvin Minsky, Claude Shannon, and never liked the use of “artificial” or gence Conference: The Next 50 Years Nathaniel Rochester for the 1956 “intelligence” as terms for the field. (AI@50) took place July 13–15, 2006. The event, McCarthy wanted, as he ex- Ray Solomonoff said he went to the conference had three objectives: to cele- plained at AI@50, “to nail the flag to brate the Dartmouth Summer Research summer project hoping to convince the mast.” McCarthy is credited for Project, which occurred in 1956; to as- everyone of the importance of ma- coining the phrase “artificial intelli- sess how far AI has progressed; and to chine learning.
    [Show full text]
  • Pioneers of Computing
    Pioneers of Computing В 1980 IEEE Computer Society учредило Золотую медаль (бронзовую) «Вычислительный Пионер» Пионерами учредителями стали 32 члена IEEE Computer Society, связанных с работами по информатике и вычислительным наукам. 1 Pioneers of Computing 1.Howard H. Aiken (Havard Mark I) 2.John V. Atanasoff 3.Charles Babbage (Analytical Engine) 4.John Backus 5.Gordon Bell (Digital) 6.Vannevar Bush 7.Edsger W. Dijkstra 8.John Presper Eckert 9.Douglas C. Engelbart 10.Andrei P. Ershov (theroretical programming) 11.Tommy Flowers (Colossus engineer) 12.Robert W. Floyd 13.Kurt Gödel 14.William R. Hewlett 15.Herman Hollerith 16.Grace M. Hopper 17.Tom Kilburn (Manchester) 2 Pioneers of Computing 1. Donald E. Knuth (TeX) 2. Sergei A. Lebedev 3. Augusta Ada Lovelace 4. Aleksey A.Lyapunov 5. Benoit Mandelbrot 6. John W. Mauchly 7. David Packard 8. Blaise Pascal 9. P. Georg and Edvard Scheutz (Difference Engine, Sweden) 10. C. E. Shannon (information theory) 11. George R. Stibitz 12. Alan M. Turing (Colossus and code-breaking) 13. John von Neumann 14. Maurice V. Wilkes (EDSAC) 15. J.H. Wilkinson (numerical analysis) 16. Freddie C. Williams 17. Niklaus Wirth 18. Stephen Wolfram (Mathematica) 19. Konrad Zuse 3 Pioneers of Computing - 2 Howard H. Aiken (Havard Mark I) – США Создатель первой ЭВМ – 1943 г. Gene M. Amdahl (IBM360 computer architecture, including pipelining, instruction look-ahead, and cache memory) – США (1964 г.) Идеология майнфреймов – система массовой обработки данных John W. Backus (Fortran) – первый язык высокого уровня – 1956 г. 4 Pioneers of Computing - 3 Robert S. Barton For his outstanding contributions in basing the design of computing systems on the hierarchical nature of programs and their data.
    [Show full text]
  • W. Brodersen Prof. Michael Dertouzos Lab. Prof. Jerome A. Feldman Dr. Michael Frankel
    * DARPA PRINCIPAL INVESTIGATORS MEETING DOUBLETREE INN MONTEREY, CALIFORNIA APRIL 10 - 13, 1983 ATTENDEES ( Dr. Robert Balzer USC-lnformation Sciences Inst. Prof. Robert W. Brodersen DR. BRUCE BUCHANAN Univ. of Calif.-Berkeley STANFORD UNIVERSITY Dr. John H. Cafarella MIT-Lincoln Lab. Prof. Thomas Cheatham Harvard University Aiken Computation Lab. Dr. Danny Cohen USC-lnformation Sciences Inst. Prof. Michael Dertouzos MIT-Computer Science Lab. Prof. Edward A. Feigenbaum Stanford University Prof. Jerome A. Feldman University of Rochester Dr. Michael Frankel SRI International Prof. Robert Gallager MIT-EE and Computer Science Dr. Corded Green Kestrel Institute Dr. Anthony Hearn Rand Corporation Prof. John L Hennessy Stanford University Dr. Estil V. Hoversten Linkabit Corporation "* 2 Prof. Leonard Kleinrock Univ. of Calif.-Los Angeles Prof. Victor R. Lesser University of Massachusetts Dr. Donald Nielson SRI International Prof. Jon McCarthy Stanford University Mr. Alan McLaughlin MIT-Lincoln Lab. Prof. James Meindl Stanford University Prof. Allen Newell Carnegie Mellon University Dr. Nils J. Nilsson SRI International Prof. Allan Oppenheim MIT-EE and Computer Science Prof. Paul Penfield, Jr. Massachusetts Inst, of Technology Dr. Gerald Popek Univ. of Calif. -Los Angeles Dr. Raj D. Reddy Carnegie Mellon University Dr. Daniel R. Ries Computer Corp. of America Prof. Edward R. Riseman Univ. of Massachusetts Prof. Roger C. Shank Yale University \ 3 Prof. Chuck Seitz Calif. Inst, of Technology Prof. Carlo H. Sequin Univ. of Calif.-Berkeley Prof. Joseph F. Traub Columbia University Mr. Keith W. Uncapher USC-lnformation Sciences Inst. Mr. Albert Vezza MIT-Laboratory for Computer Science Mr. David C. Walden Bolt, Beranek and Newman Prof. Patrick Winston MIT-Artificial Intelligence Lab.
    [Show full text]
  • Introduction
    INTRODUCTION In which we try to explain why we consider artificial intelligence to be a subject most worthy of study, and in which we try to decide what exactly it is, this being a good thing to decide before embarking. INTELLIGENCE We call ourselves _Homo sapiens—man the wise—because our intelligence is so important to us. For thousands of years, we have tried to understand how we think; that is, how a mere handful of matter can perceive, understand, predict, and manipulate a world far larger and ARTIFICIAL NTIIII GERHIE more complicated than itself. The field of artificial intelligence, or Al, goes further still: it attempts not just to understand but also to build intelligent entities. AI is one of the newest fields in science and engineering. Work started in earnest soon after World War II, and the name itself was coined in 1956. Along with molecular biology, AI is regularly cited as the "field I would most like to be in" by scientists in other disciplines. A student in physics might reasonably feel that all the good ideas have already been taken by Galileo, Newton, Einstein, and the rest. AI, on the other hand, still has openings for several full-time Einsteins and Edisons. Al currently encompasses a huge variety of subfields, ranging from the general (learning and perception) to the specific, such as playing chess, proving mathematical theorems, writing poetry, driving a car on a crowded street, and diagnosing diseases. AI is relevant to any intellectual task; it is truly a universal field. 1.1 WHAT IS AI? We have claimed that AI is exciting, but we have not said what it is.
    [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]
  • The Problems with Problem Solving: Reflections on the Rise, Current Status, and Possible Future of a Cognitive Research Paradigm1
    View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by Purdue E-Pubs The Problems with Problem Solving: Reflections on the Rise, Current Status, and Possible Future of a Cognitive Research Paradigm1 Stellan Ohlssoni Abstract The research paradigm invented by Allen Newell and Herbert A. Simon in the late 1950s dominated the study of problem solving for more than three decades. But in the early 1990s, problem solving ceased to drive research on complex cognition. As part of this decline, Newell and Simon’s most innovative research practices – especially their method for inducing subjects’ strategies from verbal protocols - were abandoned. In this essay, I summarize Newell and Simon’s theoretical and methodological innovations and explain why their strategy identification method did not become a standard research tool. I argue that the method lacked a systematic way to aggregate data, and that Newell and Simon’s search for general problem solving strategies failed. Paradoxically, the theoretical vision that led them to search elsewhere for general principles led researchers away from stud- ’ ies of complex problem solving. Newell and Simon’s main enduring contribution is the theory that people solve problems via heuristic search through a problem space. This theory remains the centerpiece of our understanding of how people solve unfamiliar problems, but it is seriously incomplete. In the early 1970s, Newell and Simon suggested that the field should focus on the question where problem spaces and search strategies come from. I propose a breakdown of this overarching question into five specific research questions. Principled answers to those questions would expand the theory of heuristic search into a more complete theory of human problem solving.
    [Show full text]
  • John W. Mauchly Papers Ms
    John W. Mauchly papers Ms. Coll. 925 Finding aid prepared by Holly Mengel. Last updated on April 27, 2020. University of Pennsylvania, Kislak Center for Special Collections, Rare Books and Manuscripts 2015 September 9 John W. Mauchly papers Table of Contents Summary Information....................................................................................................................................3 Biography/History..........................................................................................................................................4 Scope and Contents....................................................................................................................................... 6 Administrative Information........................................................................................................................... 7 Related Materials........................................................................................................................................... 8 Controlled Access Headings..........................................................................................................................8 Collection Inventory.................................................................................................................................... 10 Series I. Youth, education, and early career......................................................................................... 10 Series II. Moore School of Electrical Engineering, University of Pennsylvania.................................
    [Show full text]
  • Glimm and Witten Receive National Medal of Science, Volume 51, Number 2
    Glimm and Witten Receive National Medal of Science On October 22, 2003, President Bush named eight of the nation’s leading scientists and engineers to receive the National Medal of Science. The medal is the nation’s highest honor for achievement in sci- ence, mathematics, and engineering. The medal James G. Glimm Edward Witten also recognizes contributions to innovation, in- dustry, or education. Columbia University in 1959. He is the Distin- Among the awardees are two who work in the guished Leading Professor of Mathematics at the mathematical sciences, JAMES G. GLIMM and EDWARD State University of New York at Stony Brook. WITTEN. Edward Witten James G. Glimm Witten is a world leader in “string theory”, an attempt Glimm has made outstanding contributions to by physicists to describe in one unified way all the shock wave theory, in which mathematical models known forces of nature as well as to understand are developed to explain natural phenomena that nature at the most basic level. Witten’s contributions involve intense compression, such as air pressure while at the Institute for Advanced Study have set in sonic booms, crust displacement in earthquakes, the agenda for many developments, such as progress and density of material in volcanic eruptions and in “dualities”, which suggest that all known string other explosions. Glimm also has been a leading theories are related. theorist in operator algebras, partial differential Witten’s earliest papers produced advances in equations, mathematical physics, applied mathe- quantum chromodynamics (QCD), a theory that matics, and quantum statistical mechanics. describes the interactions among the fundamental Glimm’s work in quantum field theory and particles (quarks and gluons) that make up all statistical mechanics had a major impact on atomic nuclei.
    [Show full text]
  • RAND and the Information Evolution a History in Essays and Vignettes WILLIS H
    THE ARTS This PDF document was made available from www.rand.org as a public CHILD POLICY service of the RAND Corporation. CIVIL JUSTICE EDUCATION ENERGY AND ENVIRONMENT Jump down to document6 HEALTH AND HEALTH CARE INTERNATIONAL AFFAIRS NATIONAL SECURITY The RAND Corporation is a nonprofit research POPULATION AND AGING organization providing objective analysis and effective PUBLIC SAFETY solutions that address the challenges facing the public SCIENCE AND TECHNOLOGY and private sectors around the world. SUBSTANCE ABUSE TERRORISM AND HOMELAND SECURITY TRANSPORTATION AND INFRASTRUCTURE WORKFORCE AND WORKPLACE Support RAND Purchase this document Browse Books & Publications Make a charitable contribution For More Information Visit RAND at www.rand.org Learn more about the RAND Corporation View document details Limited Electronic Distribution Rights This document and trademark(s) contained herein are protected by law as indicated in a notice appearing later in this work. This electronic representation of RAND intellectual property is provided for non-commercial use only. Unauthorized posting of RAND PDFs to a non-RAND Web site is prohibited. RAND PDFs are protected under copyright law. Permission is required from RAND to reproduce, or reuse in another form, any of our research documents for commercial use. For information on reprint and linking permissions, please see RAND Permissions. This product is part of the RAND Corporation corporate publication series. Corporate publications describe or promote RAND divisions and programs, summarize research results, or announce upcoming events. RAND and the Information Evolution A History in Essays and Vignettes WILLIS H. WARE C O R P O R A T I O N Funding for the publication of this document was provided through a generous gift from Paul Baran, an alumnus of RAND, and support from RAND via its philanthropic donors and income from operations.
    [Show full text]
  • History and Survey of AI August 31, 2012
    CS671 Artificial General Intelligence History and Survey of AI August 31, 2012 Where did it go wrong and where did it go right. 1 Observation: AI has made steady progress since its inception about 60 years ago. This is contrary to some accounts in the popular press. 2 The \Gullibility Wave" Phenomenon • First reaction to AI: Skepticism • Second (perhaps having seen a robot demo or learned to pro- gram a computer): Wow! Computers can do a lot. Gullibility Doing intelligent task X isn't so hard. When I think of doing it myself, it seems straightforward! Enthusiasm • Third reaction: Computers are impossibly literal-minded. They can never break out of this mold. Disenchantment • Fourth reaction: The brain doesn't do computation anyway. It does holograms, or dynamical systems, or quantum gravity, or neural nets, or soulstuff. Denial • Fifth reaction: There's really no alternative on the horizon to computation (which is what neural nets actually do). Let's get to work! Maturity 3 The Beginning of Computer Science Was the Beginning of AI Alan Turing (1912{1954) invented both. Perhaps it was because he was weird, but even before the end of World War II he discussed machine intelligence with col- leagues at the British Code and Cipher School (\Bletch- ley Park"). 4 1940s: A Gleam In Turing's Eye In 1950 Alan Turing published \Computing Machinery and Intelligence." (Mind 49) He predicted that a machine would pass the Turing Test by 2000 (i.e., would fool a (naive?) judge 70% of the time into thinking it was human after a short interview).
    [Show full text]