A Sociological Study of the Official History of the Perceptrons Controversy

Social Studies of Science

Volumn 26, Issue 3, 1996, Pages 611-659

  Olazaran, Mikel ^a  

^a UNIVERSITY OF THE BASQUE COUNTRY UPV EHU   (Spain)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 84992827630     PISSN: 03063127     EISSN: None     Source Type: Journal    
DOI: 10.1177/030631296026003005     Document Type: Article

References (70)

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3. Collins, ‘An Empirical Relativist Programme in the Sociology of Scientific Knowledge’, in Karin D. Knorr-Cetina and Michael Mulkay (eds), Science Observed: Perspectives on the Social Study of Science (London: Sage, 1983), 85–113
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7. This is Donald MacKenzie's formulation: see D. MacKenzie, Inventing Accuracy: A Historical Sociology of Nuclear Missile Guidance (Cambridge, MA: MIT Press, 1990), 10.
8. As sociologists have argued, since the pioneer work of Barry Barnes and David Bloor, this is not a characteristic of ‘bad’ or ‘ideological’ science, but of all science: see, for example, B. Barnes, Scientific Knowledge and Sociological Theory (London: Routledge & Kegan Paul, 1974)
9. D. Bloor, Knowledge and Social Imagery (London: Routledge & Kegan Paul, 1976).
10. T.J. Pinch, ‘What Does a Proof Do if it Does Not Prove?: A Study of the Social Conditions and Metaphysical Divisions Leading to David Bohm and John von Neumann Failing to Communicate in Quantum Physics’, in Everett Mendelsohn, Peter Weingart and Richard Whitley (eds), The Social Production of Scientific Knowledge, Sociology of the Sciences Yearbook, Vol. 1 (Dordrecht: D. Reidel, 1977), 171–215, at 174. In this paper, I use Pinch's case study as a parallel, and I apply many of the categories he uses to my own case study.
11. For a detailed account of the evolution of neural-network research, see Mikel Olazaran, A Historical Sociology of Neural Network Research (unpublished PhD dissertation, Department of Sociology, University of Edinburgh, 1991)
12. Olazaran, ‘A Sociological History of the Neural Network Controversy’, Advances in Computers, Vol. 37 (1993), 335–425.In the present paper, I examine just one key issue of the evolution of neural nets: the emergence and functions of the official history of the perceptrons controversy.
13. For the history of symbolic AI, see: James Fleck, The Structure and Development of Artificial Intelligence: A Case Study in the Sociology of Science (unpublished MSc dissertation, University of Manchester, 1978)
14. Pamela McCor-duck, Machines Who Think: A Personal Inquiry into the History and Prospects of Artificial Intelligence (New York: W.H. Freeman, 1979)
15. Fleck, ‘Development and Establishment in Artificial Intelligence’, in Norbert Elias, Herminio Martins and Richard Whitley (eds), Scientific Establishments and Hierarchies, Sociology of the Sciences Yearbook, Vol. 6 (Dordrecht: D. Reidel, 1982), 169–217
16. Fleck, ‘Postscript: The Commercialisation of Artificial Intelligence’, in Brian P. Bloomfield (ed.), The Question of AI (London: Croom-Helm, 1987), 149–64
17. Paul N. Edwards, The Closed World: Computers and the Politics of Discourse in Cold War America (Cambridge, MA: MIT Press, Inside Technology series, 1995, forthcoming).
18. M.L. Minsky and S.A. Papert, Perceptrons: An Introduction to Computational Geometry (Cambridge, MA: MIT Press, 1969), 19.
19. Minsky and Papert refer to F. Rosenblatt, ‘The Perceptron: a Probabilistic Model for Information Storage and Organization in the Brain’, Psychological Review, Vol. 65 (1958), 386–408.
20. National Physical Laboratory (NPL), Mechanisation of Thought Processes, Vols I and II (London: Her Majesty's Stationery Office, 1959)
21. Marshall C. Yovits and S. Cameron (eds), Self-organizing Systems: Proceedings of an Interdisciplinary Conference, Chicago, IL, 5–6 May 1959 (New York: Pergamon Press, 1960)
22. H. von Foerster and G.W. Zopf (eds), Illinois Symposium on Principles of Self-organization, University of Illinois, Urbana, IL, 1960 (New York: Pergamon Press, 1962)
23. Yovits, G.T. Jacobi and G.D. Goldstein (eds), Self-organizing Systems 1962 (Washington, DC: Spartan, 1962).
24. see also J.A. Anderson and Eduard Rosenfeld (eds), Neurocomputing: Foundations of Research (Cambridge, MA: MIT Press, 1988).
25. F. Rosenblatt, On the Convergence of Reinforcement Procedures in Simple Perceptrons (Buffalo, NY: Cornell Aeronautical Laboratory Report VG-1196-G-4, 1960)
26. Rosenblatt, Principles of Neurodynamics (New York: Spartan, 1962)
27. B. Widrow and M.E. Hoff, ‘Adaptive Switching Circuits’, 1960 IRE WESCON Convention Record (New York: IRE, 1960), 96–104.
28. ‘New Navy Device Learns by Doing’, The New York Times (8 July 1958), 25:2.
29. For similar statements, see: ‘Electronic “Brain” Teaches Itself, The New York Times (13 July 1958), at iv 9:6
30. ‘Rival’, The New Yorker (6 December 1958), 44–45.
31. Robert Hecht-Nielsen, Neurocomputing (Reading, MA: Addison-Wesley, 1990), 16–17.
32. This is due in part to the fact that AI affects social discourses about the similarities and differences between human beings and machines: see J. Fleck, ‘Artificial Intelligence and Industrial Robots: An Automatic End for Utopian Thought’, in E. Mendelsohn and Helga Nowotny (eds), Nineteen Eighty-Four: Science between Utopia and Dystopia, Sociology of the Sciences Yearbook, Vol. 8 (Dordrecht: D. Reidel, 1984), 189–231.
33. Cited in Hubert L. Dreyfus, What Computers Can't Do: The Limits of Artificial Intelligence (New York: Harper Colophon, 2nd edn, 1979), 81–82.
34. Then he made the statement, “I was wrong about Dreyfus too, but I haven't admitted it yet”, which brought another round of applause’, from Randolph K. Zeitvogel, ‘ICNN Reviewed’, Synapse Connection (now Neural Technology Update), Vol. 2–8 (1988), 10–11.
35. For a recent discussion about AI from the perspective of the sociology of knowledge, see H.M. Collins, Artificial Experts: Social Knowledge and Intelligent Machines (Cambridge, MA: MIT Press, 1990).
36. J.K. Hawkins, ‘Self-Organizing Systems: A Review and Commentary’, Proceedings of the Institute of Radio Engineers (IRE), Vol. 49 (1961), 31–48, quote at 45–47.
37. Richard P. Lippmann, ‘An Introduction to Computing with Neural Nets’, IEEE ASSP Magazine, Vol. 4 (1987), 4–22, at 15–18
38. DARPA, Darpa Neural Network Study (Fairfax, VA: Armed Forces Communications and Electronics Association [AFCEA] International Press, 1988), 78–80.
39. The history of the analog computer (which was used for solving differential equations) goes back to the 1930s, but its golden age was the 1950s and early 1960s. Voltage precision problems in analog computers contrasted with the advances in accuracy, speed, memory capacity, miniaturization and programming of digital computers. By the mid-1960s, digital technology was the choice for most computer users: see Time-Life Books, Alternative Computers (Alexandria, VA: Time-Life Books, 1989), 26, 27 & 39.
40. Jeremy Bernstein, ‘Profiles: AI, Marvin Minsky’, The New Yorker (14 December 1981), 50–126, quote at 100.
41. S.A. Papert, ‘One AI or Many?’, in Stephen R. Graubard (ed.), The Artificial Intelligence Debate: False Starts, Real Foundations (Cambridge, MA: MIT Press, 1988), 1–14, quote at 4–5, emphasis in original.
42. Igor Aleksander and Helen Morton made this comparison: ‘Minsky and Papert's central argument is that perceptrons are only good if their order remains constant for a particular problem irrespective of the size of the input “retina”. This is similar to the requirement that a program in conventional computing, such as a routine for sorting a list of numbers, should be largely invariant to the size of the task. It is accepted that such a program might need to be given the length of the list as input data, but it would be of little use if it had to be rewritten for lists of different lengths’: I. Aleksander and H. Morton, An Introduction to Neural Computing (London: Chapman & Hall, 1990), 41.
43. Larry Laudan used the term ‘anomalous problem’: see L. Laudan, Progress and its Problems: Towards a Theory of Scientific Growth (Berkeley, CA: University of California Press, 1977), 29. I am, therefore, not using this term in its Kuhnian sense (experimental results which do not fit within the accepted categories of a scientific paradigm).
44. H. David Block, ‘A Review of Perceptrons\ Information and Control, Vol. 17 (1970), 510–22, quote at 517, emphasis in original.
45. For a recent contribution to the issue of proofs of computer-system correctness, see D. MacKenzie, ‘Negotiating Arithmetic, Constructing Proof: The Sociology of Mathematics and Information Technology’, Social Studies of Science, Vol. 23 (1993), 37–65.
46. Jon Guice, ‘Lord ARPA and the Battle of Perceptrons: Controversy and the State in Intelligent Computing, 1958–69’, draft paper under consideration by Social Studies of Science; Guice, Designing the Future: The US Advanced Research Projects Agency (La Jolla, CA: Department of Sociology, University of California at San Diego, PhD in progress, working title).
47. Louis Fein, ‘The Artificial Intelligentsia’, Wescon Technical Papers, Vol. 7 (11.1, Part 7) (1963), 1–7.
48. This paper was later published with some minor changes, as: L. Fein, ‘The Artificial Intelligentsia’, IEEE Spectrum (February 1964), 74–87.
49. A. Newell and H.A. Simon, ‘Computer Science as Empirical Enquiry: Symbols and Search’, Communications of the Association for Computing Machinery, Vol. 19 (1976), 113–26
50. reprinted in John Haugeland (ed.), Mind Design (Cambridge, MA: MIT Press, 1981), 35–66, quotation from reprinted version, at 50.
51. A. Newell, ‘Intellectual Issues in the History of Artificial Intelligence’, in Fritz Machlup and Una Mansfield (eds), The Study of Information: Interdisciplinary Messages (New York: John Wiley & Sons, 1983), 187–227, at 201–02, emphasis added.
52. James Lighthill, Artificial Intelligence (London: Science Research Council, 1973).
53. See, for example, Bill Harvey, ‘Plausibility and the Evaluation of Knowledge: A Case-Study of Experimental Quantum Mechanics’, Social Studies of Science, Vol. 11 (1981), 95–130, at 126.
54. For Everett Hughes's methodological principle, see S. Leigh Star, ‘Introduction: The Sociology of Science and Technology’, Social Problems, Vol. 35 (1988), 197–205, at 198.
55. Geoffrey E. Hinton and J.A. Anderson, Parallel Models of Associative Memory (Hillsdale, NJ: Laurence Erlbaum, 1981).
56. Dana H. Ballard, G.E. Hinton and Terrence Sejnowski, ‘Parallel Visual Computation’, Nature, Vol. 306 (3 November 1983), 21–26.
57. Jerome A. Feldman and D.H. Ballard, ‘Connectionist Models and their Properties’, Cognitive Science, Vol. 6 (1982), 205–54.
58. Researchers have also started to design and use special-purpose hardware for implementing neural nets. For Carver Mead's pioneer work on VLSI analog circuits, see C. Mead, Analog VLSI and Neural Systems (Reading, MA: Addison-Wesley, 1989).
59. The two PDP volumes were the manifesto of the new neural-net movement: see David E. Rumelhart, James L. McClelland and The PDP Research Group, Parallel Distributed Processing: Explorations in the Microstructure of Cognition, Vol. 1, Foundations (Cambridge, MA: MIT Press, 1986)
60. See, for example: Michael J. Mulkay, ‘Three Models of Scientific Development’, Sociological Review, Vol. 23 (1975), 509–26
61. Mulkay, G. Nigel Gilbert and S. Woolgar, ‘Problem Areas and Research Networks in Science’, Sociology, Vol. 9 (1985), 187–203.
62. J.J. Hopfield, ‘Neural Networks and Physical Systems with Emergent Collective Computational Abilities’, Proceedings of the National Academy of Sciences, Vol. 79 (1982), 2554–2558
63. David H. Ackley, G.E. Hinton and T.J. Sejnowski, ‘A Learning Algorithm for Boltzmann Machines’, Cognitive Science, Vol. 9 (1985), 147–69
64. T.J. Sejnowski and Halbert White, Introduction to reprinted version of Nils J. Nilsson, The Mathematical Foundations of Learning Machines (San Mateo, CA: Morgan Kaufmann, 1991; original version 1965), vii-xxi, quote at xii.
65. See D.J. Rumelhart, G.E. Hinton and R.J. Williams, ‘Learning Representations by Back-propagating Errors’, Nature, Vol. 323 (9 October 1986), 533–36
66. For Werbos's contributions see: P.J. Werbos, Beyond Regression: New Tools for Prediction and Analysis in the Behavioral Sciences (Cambridge, MA: unpublished PhD dissertation, Harvard University, 1974)
67. Werbos, ‘Applications of Advances in Nonlinear Sensitivity Analysis’, in R.F. Drenick and F. Kozin (eds), Systems Modelling and Optimization: Proceedings of the 10th IFIP Conference, New York City, 31 Augusts September 1981 (New York: Springer-Verlag, 1982), 762–70.
68. See Richard Forsyth, ‘The Brain Mimics Are Back in Business’, The Guardian (London, 12 January 1989), 25.
69. The role of the PDP Group in rewriting the official history of the controversy helps explain Grossberg's priority complaints. Grossberg claimed that some of his models were rediscovered in the neural-net revival, and that he did not receive enough recognition for his previous work: see S. Grossberg, ‘Competitive Learning: From Interactive Activation to Adaptive Resonance’, Cognitive Science, Vol. 11 (1987), 23–63.
70. M.L. Minsky and S.A. Papert, Perceptrons: An Introduction to Computational Geometry (Cambridge, MA: MIT Press, 1988), 260–61