Advertisement

AI & SOCIETY

, Volume 34, Issue 2, pp 215–242 | Cite as

Machine intelligence: a chimera

  • Mihai NadinEmail author
Original Article

Abstract

The notion of computation has changed the world more than any previous expressions of knowledge. However, as know-how in its particular algorithmic embodiment, computation is closed to meaning. Therefore, computer-based data processing can only mimic life’s creative aspects, without being creative itself. AI’s current record of accomplishments shows that it automates tasks associated with intelligence, without being intelligent itself. Mistaking the abstract (computation) for the concrete (computer) has led to the religion of “everything is an output of computation”—even the humankind that conceived the computer. The hypostatized role of computers explains the increased dependence on them. The convergence machine called deep learning is only the most recent form through which the deterministic theology of the machine claims more than what it actually is: extremely effective data processing. A proper understanding of complexity, as well as the need to distinguish between the reactive nature of the artificial and the anticipatory nature of the living are suggested as practical responses to the challenges posed by machine theology.

Keywords

Hypostatize Convergence Anticipatory Meaning G-complexity 

Notes

Acknowledgements instead of any conclusion

This study was in progress when in the summer of 2017 Karamjit Gill announced a memorial issue dedicated to Hubert Dreyfus’s legacy. It is the outcome of almost 30 years of work in computation—writing programs, testing ideas, carrying out experiments—and of no less intense dedication to understanding how computation has changed us. During this long preparation, I experienced Dreyfus’s prosopagnosia three times. Indeed, he could not recognize me (as he had the same problems with others). My enthusiasm for computation made him often lose patience. He wanted to write a review of The Civilization of Illiteracy, but in the end could not find time for it. Weizenbaum imparted to me many insights into academic life: you can have a chair at MIT, but if you do not bring in the money, there was no electricity in the room where the chair was located. In Hamburg (Mediale 1998) and later in Berlin (2004), we disagreed as only Talmudic scholars would—mainly because Weizenbaum and I were into debunking the rapidly growing mythology of the “mother of all machines.” Some of the thoughts in my text go back to conversations with both of them. As I was finishing yet another review of this text, the news reached me: a Weizenbaum Institute (for the networked world) was funded in Berlin. Guilt (the Nazis decimated his family in Germany) and a “slap in the face” of America for the recognition it did not give Weizenbaum combine in the establishment of an institution that will have to live up to his provocative thinking. Other conversations—with McCarthy and Minsky—a short exchange with Simon, and another with Pat Hayes are also reflected in the text. Over many years, Lotfi Zadeh listened patiently to my arguments and shared some of his own with me, challenging me with his examples. Also over many years, Pamela McCorduck and Terry Winograd assisted, not always agreeing with what I had to say. After the preprint (https://arxiv.org/ftp/arxiv/papers/1712/1712.04306.pdf) was published, I received feedback from Jaron Lanier, Soren Brier, Ilkka Tuomi, Michael Winkler and Jaime Cárdenas García, Maximilian Schich, Clarissa Sieckenius de Souza, and Frank Dufour. Dr. Eric Topol graciously read the paper and so did Pascal Honoré. It helped a lot. No, this study was not supported by any grant, except that of Elvira Nadin’s generous willingness to be the sounding board for ideas that would not qualify as middle of the road statements—and often coming up with her own insights. I remain responsible for all my inferences, faulty or otherwise. Several reviewers, some more competent than others, not only endorsed publication, but also expressed reserve. For this I am more grateful than for uncritical endorsement.

References

  1. Allis LV (1994) Searching for Solutions in Games and Artificial Intelligence. PhD Thesis at the University of Limburg, The Netherlands. https://project.dke.maastrichtuniversity.nl/games/files/phd/SearchingForSolutions.pdf
  2. Andrychowicz M, Denil M, Gomez S, Hoffman MW, Pfau D, Schaul T, Shillingford B, de Freitas N (2016) Learning to learn by gradient descent by gradient descent. https://arxiv.org/abs/1606.04474. Accessed Nov 30 2016
  3. Arshavsky YI (1991) Gelfand on mathematics and neurophysiology. http://israelmgelfand.com/bio_work/arshavsky_biomed.pdf
  4. Bell P (2005) Psychology or semiotics: persons or subjects? Passau Schriften zur Psychologiegeschiche 13:85–104Google Scholar
  5. Bernstein NA (1936) Contemporary inquiries into the physiology in the nervous process (Sovremennye iskaniia v fiziologii nervnovo protsessa). Gsudarstvennoe izdatel’stvo biologicheskoi ii meditzinskoi literatury, MoscowGoogle Scholar
  6. Bernstein NA (1967) The co-ordination and regulation of movements. Pergamon Press, OxfordGoogle Scholar
  7. Bolter JD (1984) Turing’s man: western culture in the computer age. University of North Carolina Press, Chapel HillGoogle Scholar
  8. Cannon WB (1932) The wisdom of the body. W.W. Norton, New YorkCrossRefGoogle Scholar
  9. Cannon WB (1945) The way of an investigator. W.W. Norton, New YorkGoogle Scholar
  10. Chan D (2017) The AI that Has nothing to learn from humans. The Atlantic. https://www.theatlantic.com/technology/archive/2017/10/alphago-zero-the-ai-that-taught-itself-go/543450/
  11. Church A (1936a) An unsolvable problem of elementary number theory. Am J Math 18Google Scholar
  12. Church A (1936b) A Note on the Entscheidungsproblem. J Symb Logic 1(1):40–41CrossRefzbMATHGoogle Scholar
  13. Church A (1936c) Correction to A Note on the Entscheidungsproblem. J Symb Logic 1(3):101–102CrossRefzbMATHGoogle Scholar
  14. Dalakov G History of computers and computing. http://history-computer.com/ModernComputer/thinkers/Peirce.html
  15. Davies CWP (2004) John Archibald wheeler and the clash of ideas. In: Barrow JD, Davies CWP, Harper CL (eds) Science and ultimate reality. Cambridge University Press, Cambridge, pp 3–34CrossRefGoogle Scholar
  16. Dieters OFC (1865) Untersuchungen über Gehirn u. Rückenmark des Menschen und der Säugethiere. Vieweg, BraunschweigCrossRefGoogle Scholar
  17. Dreyfus HL (1965) Alchemy and artificial intelligence, RAND paper P3244 https://www.rand.org/content/dam/rand/pubs/papers/2006/P3244.pdf
  18. Dreyfus H (1972) What computers can’t do. A critique of artificial reason. Harper & Row, New YorkGoogle Scholar
  19. Dreyfus H (1979) What computers can’t do. A critique of artificial reason (revised edition). Harper & Row, New YorkGoogle Scholar
  20. Dreyfus HL, Dreyfus SE (1986) From Socrates to Expert Systems: The Limits of Calculative Rationality. In: Mitcham C, Huning A (eds) Philosophy and Technology II. Boston Studies in the Philosophy of Science, vol 90, Springer, DordrechtGoogle Scholar
  21. Ellis GFR (2009) Top-down causation and the human brain. In: Murphy N, Ellis G, O’Connor T (eds) Downward causation and the neurobiology of free. Springer, Will Heidelberg/LondonGoogle Scholar
  22. Ellis GFR (2012) Recognising top-down causation. https://arxiv.org/abs/1212.2275. Accessed Dec 9
  23. Elsasser WM (1966) Atom and organism. A new approach to theoretical biology. Princeton University Press, PrincetonGoogle Scholar
  24. Elsasser WM (1987) Reflections on a theory of organisms. Éditions Orbis Publishing, FrelighsburgGoogle Scholar
  25. Elsasser W (1998) Reflections on the Theory of Organisms. Holism in Biology. Johns Hopkins University Press, Baltimore. (Originally published in 1987 by ORBIS Publishing, Frelighsburg, Quebec)Google Scholar
  26. Fogel DB (1995, 2006) Evolutionary computation: toward a new philosophy of machine intelligence, 3rd edn. Wiley, HobokenzbMATHGoogle Scholar
  27. Fogel DB (2004) A self-learning evolutionary chess program. Proc IEEE 92(12):1947–1954. http://ieeexplore.ieee.org/document/1360168/
  28. Gatys LA, Ecker AS, Bethge M (2015) A neural algorithm for artistic style. https://arxiv.org/abs/1508.06576. Accessed Sept
  29. Gödel K (1936) Über die Länge von Beweisen. Ergebnisse eines mathematischen Kolloquiums Heft 7:23–24zbMATHGoogle Scholar
  30. Gödel K (1972) Some remarks on the undecidability results, collected works II. Oxford University Press, Oxford, pp 305–306Google Scholar
  31. Grace K, Salvatier J, Dafoe A, Zhang B, Evans O (2017) When will AI exceed human performance? Evidence from AI Experts. https://arxiv.org/abs/1705.08807 and https://arxiv.org/pdf/1705.08807.pdf
  32. Graves A, Wayne G, Danihelka I (2014) Neural turing machines. https://arxiv.org/pdf/1410.5401
  33. Graves et al (2016) Hybrid computing using a neural network with dynamic external memory. Nature 538(7626):471–476CrossRefGoogle Scholar
  34. Greff K, Srivastava R, Koutnik J, Steunebrink B, Schmidhuber J (2016) LSTM: a search space odyssey. IEEE Trans Neural Netw Learn Syst 18(10):2222–2232. http://people.idsia.ch/~juergen/rnn.html
  35. Harris M (2017) God is a bot, and anthony lewandowski is his messenger, WIRED Backchannel. https://www.wired.com/story/god-is-a-bot-and-anthony-levandowski-is-his-messenger/. Accessed Sept 27
  36. Hawking S (2014) Stephen Hawking warns artificial intelligence could end mankind. Interview with R.C. Jones http://www.bbc.com/news/technology-30290540
  37. Hilbert D, Ackermann W (1928) Grundzüge der theoretische Logik. Verlag von Julius Springer, BerlinzbMATHGoogle Scholar
  38. Hobbes T (1968) Leviathan. Penguin, HarmondsworthGoogle Scholar
  39. Hodgkin AL, Huxley AF (1952) A quantitative description of membrane current and its application to conduction and excitation in nerve. J Physiol 17(4):500–544CrossRefGoogle Scholar
  40. Hopcroft JE, Ullman JD (1979) Introduction to automata theory, languages, and computation. Addison-Wesley, New YorkzbMATHGoogle Scholar
  41. Hopfield JJ (1982) Neural networks and physical systems with emergent collective computational abilities. Proc Natl Acad Sci USA 9(8):2554–2558MathSciNetCrossRefzbMATHGoogle Scholar
  42. Hotchkiss RD (1958) Concepts of biology. In: Gerard RW (ed) Behavioral Science, 3:2Google Scholar
  43. Karpathy A (2015) The unreasonable effectiveness of recurrent neural networks. Hacker’s Guide to Neural Networks, Andrej Karpathy Blog. http://karpathy.github.io/2015/05/21/rnn-effectiveness/. Accessed 11 Apr 2018
  44. Kline RR (2015) The cybernetics moment: or why we call our age the information age. Johns Hopkins University Press, BaltimorezbMATHGoogle Scholar
  45. Kohonen T (1988) An introduction to neural computing. Neural Netw 1(1):3–16CrossRefGoogle Scholar
  46. Kurzweil R (2005) The singularity is near. Viking, New YorkGoogle Scholar
  47. Kurzweil R (2013) Immortality by 2045. Global Future 2045 Congress 2013. https://www.youtube.com/watch?v=qlRTbl_IB
  48. Latash M (2012) The bliss (not the problem) of motor abundance (not redundancy). Exp Brain Sci 217(1): 1–5.  https://doi.org/10.1007/s00221-012-3000-4 CrossRefGoogle Scholar
  49. LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521(7553):436–444CrossRefGoogle Scholar
  50. Levin M et al (2017) The brain is required for normal muscle and nerve patterning during early Xenopus development. Nat Commun. https://www.nature.com/articles/s41467-017-00597-2 (Article 587)
  51. Lin HW, Tegmark M, Rolnick D (2017) Why does deep and cheap learning work so well?. https://arxiv.org/abs/1608.08225
  52. Longo G, Montevil M (2013) Extended criticality, phase spaces and enablement in biology, chaos, solitons & fractals. Emerg Crit Brain Dyn 55:64–79.  https://doi.org/10.1016/j.chaos.2013.03.008 Google Scholar
  53. Lungarella M, Iida F, Bongard J, Pfeifer R (eds) (2007) 50 years of artificial intelligence. Essays Dedicated to the 50th Anniversary of Artificial Intelligence. Springer, Berlin/HeidelbergGoogle Scholar
  54. McCarthy J, Hayes PJ (1969) Some philosophical problems from the standpoint of artificial intelligence. http://www-formal.stanford.edu/jmc/mcchay69.pdf
  55. McCulloch WS, Pitts W (1943) A logical calculus of the ideas immanent in nervous activity. Bull Math Biophysiol 5:115–133MathSciNetCrossRefzbMATHGoogle Scholar
  56. Murphy M (2015) Computers can now paint like van Gogh and Picasso. Quartz. https://qz.com/495614/computers-can-now-paint-like-van-gogh-and-picasso/. Accessed 6 Sept 2015
  57. Musk E (2014) One-on-one with Elon Musk. MIT Centennial Symposium. http://aeroastro.mit.edu/videos/centennial-symposium-one-one-one-elon-musk. Accessed Oct 24
  58. Nadin M (1983) The logic of vagueness and the category of synechism. In: Freeman E (ed) The relevance of Charles Peirce La Salle IL: the Monist library of philosophy, pp 154–166Google Scholar
  59. Nadin M (1997) The civilization of Illiteracy. Dresden University Press, DresdenGoogle Scholar
  60. Nadin M (1999) Anticipation: a spooky computation. In: Dubois D (ed) CASYS, International Journal of Computing Anticipatory Systems Partial Proceedings of CASYS 99, vol. 6. CHAOS, Liege, pp 3–47Google Scholar
  61. Nadin M (2003) Anticipation—The End Is Where We Start From. Lars Müller Press, BadenGoogle Scholar
  62. Nadin M (2010) Anticipation and dynamics: Rosen’s anticipation in the perspective of time. Spec Issue Int J Gen Syst London: Taylor Francis 39(1):3–33CrossRefzbMATHGoogle Scholar
  63. Nadin M (2013) The intractable and the undecidable—computation and anticipatory processes. Int J Appl Res Inform Technol Comput 4(3):99–121MathSciNetCrossRefGoogle Scholar
  64. Nadin M (2014) Complexity, quantum computation and anticipatory processes. Comput Commun Collab 2(1):6–34. (DOIC: 2292-1036-2014-01-003-18)CrossRefGoogle Scholar
  65. Nadin M (ed) (2015a) Learning from the past. Early Soviet/Russian contributions to a science of anticipation. Cognitive Science Monographs, vol 25. Springer, ChamGoogle Scholar
  66. Nadin M (2015b) Variability by Another Name: “Repetition Without Repetition.” Learning from the Past. Early Soviet/Russian contributions to a science of anticipation. Cognitive Science Monographs, vol 25. Springer, Cham, pp 329–340Google Scholar
  67. Nadin M (2015c) Anticipation and computation. Is anticipatory computing possible? In: Nadin M (ed) Anticipation across disciplines. Cognitive science monographs, vol 29. Springer, Cham, pp 283–339CrossRefGoogle Scholar
  68. Nadin M (2016) Anticipation and the brain, anticipation and medicine. Springer International Publishers, Cham, pp 147–175Google Scholar
  69. Nadin M (2017a) Predictive and anticipatory computation. In: Laplante P (ed) Encyclopedia of computer science and technology, 2nd edn. Taylor and Francis/CRC Press, London, pp 645–659Google Scholar
  70. Nadin M (2017b) In folly ripe. In reason rotten. Putting machine theology to rest. https://arxiv.org/abs/1712.04306
  71. Newell A, Simon HA (1976) Computer science as empirical inquiry: symbols and search. Commun ACM 19(3):113–126.  https://doi.org/10.1145/360018.360022 CrossRefGoogle Scholar
  72. Nietsche F (1883–1891) Also sprach Zarathustra: Ein Buch für Alle und Keinen. Chemnitz: Verlag von Ernst SchmeitznerGoogle Scholar
  73. Noble R, Noble D (2017) Was the watchmaker blind? Or was she one-eyed? Biology(Basel) 6:4 http://www.mdpi.com/2079-7737/6/4/47
  74. Papert S (1968) Technical report. The artificial intelligence of Hubert L. Dreyfus. A Budget of Fallacies (The Artificial Intelligence Memo No. 154). https://dl.acm.org/citation.cfm?id=889111
  75. Peirce CS (1887a), Logical machines. Am J Psychol I:65–70Google Scholar
  76. Peirce CS (1887b) Logical Machines. In: Peirce Edition Project. Writings of Charles Sanders Peirce. A Chronological Edition, vol 6. Indiana University Press, Bloomington/Indianapolis, pp 1886–1890Google Scholar
  77. Peirce Edition Project (ed) (1998) The essential Peirce. Selected philosophical writings, vol 2, pp 1893–1913Google Scholar
  78. Polanyi M (1966) The tacit dimension. The University of Chicago Press, ChicagoGoogle Scholar
  79. Purkyně JE (1823) Commentatio de examine physiologico organi visus et systematis cutanei. University of Breslau Press, BreslauGoogle Scholar
  80. Radford A, Jozefowicz R, Sutzkever I (2017) Learning to generate reviews and discovering sentiment. https://arxiv.org/abs/1704.01444
  81. Ramon y Cajal S (1899) Histologia del system nervioso del homre y de los vertebrados, Textura del sistema nervioso del hombre y de los vertebrados. Imprenta y Liberia de Nicolas Moya, MadridGoogle Scholar
  82. Rosen R (1972) Some relational cell models: the metabolism-repair systems, foundations of mathematical biology, vol 2. Academic Press, New YorkzbMATHGoogle Scholar
  83. Rosen R (1999) Essays on life itself. Columbia University Press, New YorkGoogle Scholar
  84. Rumelhart DE, Hinton GE, McClelland JL (1986) A General Framework for Parallel Distributed Processing. In: Rumelhart DE, McClelland JL, Group PDP Research (eds) Parallel distributed processing: explorations in the microstructure of cognition, vol 1. MIT Press, CambridgeGoogle Scholar
  85. Sabour S, Frosst N, Hinton GE (2017) Dynamic routing between capsules. arXiv:1710.09829. Submitted on 26 Oct 2017Google Scholar
  86. Schaeffer J et al (2007) Checkers is solved. Science 317(5844):1518–1522. AAAS, Washington DC. http://science.sciencemag.org/content/early/2007/07/19/science.1144079
  87. Scheffler I (2004) Gallery of scholars. A philosopher’s recollections. Kluwer Academic Publishers, Doredrecht/BostonGoogle Scholar
  88. Schrödinger E (1951) What is life? The physical aspect of the living cell. Cambridge University Press, CambridgezbMATHGoogle Scholar
  89. Shannon CE (1950) XXII. Programming a computer for playing chess. Philos Mag Ser 7(41):314, (First presented at the National IRE Convention, March 9, 1949, New York, USA)Google Scholar
  90. Siegelmann HT, Sontag ED (1991) Turing computability with neural nets. Appl Math Lett 4(6)77–80MathSciNetCrossRefzbMATHGoogle Scholar
  91. Silver D et al (2017) Mastering the game of Go without human knowledge. Nature 550(7676):354–359. https://www.nature.com/nature/journal/v550/n7676/full/nature24270.html
  92. Sterling P, Eyer J (1988) Allostasis: a new paradigm to explain arousal pathology. In: Fisher S, Reason JT (eds) Handbook of life stress, cognition and health. Wiley, ChichesterGoogle Scholar
  93. Thagard P (1986) Charles Peirce, Sherlock Holmes, and Artificial Intelligence. Review of the sign of three. In: Eco U, Sebeok TA (eds), Semiotics, vol 60 289–295Google Scholar
  94. Turing AM (1936–7) On computable numbers, with application to the Entscheidungsproblem. Proc London Math Soc Ser. 2. 42:230–265. (Correction, ibid, Vol. 43, 1937, 544–546)Google Scholar
  95. Turing AM (1948a) Intelligent machinery. A report by A.M. Turing. National Physical Laboratory. http://www.npl.co.uk/about/history/notable-individuals/turing/intelligent-machinery. Accessed 11 Apr 2018
  96. Turing AM (1948b) Intelligent machinery [technical report]. Teddington: National Physical Laboratory. cf. Copeland, BJ (ed) 2004. The Essential Turing: Seminal Writings in Computing, Logic, Philosophy, Artificial Intelligence, and Artificial Life Plus the Secrets of Enigma. Oxford University Press, OxfordGoogle Scholar
  97. Turing AM (1951) Programmers’ Handbook for Manchester Electronic Computer. Mark II. http://curation.cs.manchester.ac.uk/computer50/www.computer50.org/kgill/mark1/progman.aux.html. Accessed 11 Apr 2018
  98. Turing AM (1986) The ACE Report. A.M. In: Carpenter BJ, Doran RW (eds) Turing’s ace report of 1946 and other papers. MIT Press, CambridgeGoogle Scholar
  99. Wang H, Raj B (2017) On the origin of deep learning. arXiv:1702.07800v4. Accessed 11 Apr 2018
  100. Weiser M (1991) The computer for the 21st century, communications, computers, and network. Spec Issue Sci Am 265(3):94–104. https://www.lri.fr/~mbl/Stanford/CS477/papers/Weiser-SciAm.pdf. Accessed 11 Apr 2018
  101. Weizenbaum J (1975) Computer power and human reason: from judgment to calculation. W. H. Freeman & Co, New YorkGoogle Scholar
  102. Werbos PJ (2008) Bell’s theorem, many worlds and backwards-time physics: not just a matter of interpretation. Int J Theor Phys 47(11):2862–2874MathSciNetCrossRefzbMATHGoogle Scholar
  103. Wheeler JA (1989) Information, physics, quantum: the search for links. Proceedings of the 3rd International Symposium of Foundations of Quantum Mechanics, Tokyo, August 1989, pp 351–368. http://cqi.inf.usi.ch/qic/wheeler.pdf. Accessed 11 Apr 2018
  104. Whitehead AN (1992) Science and the Modern World. Free Press, New YorkzbMATHGoogle Scholar
  105. Wittgenstein L (1980) Remarks on the philosophy of psychology, vol 1. Anscombe GEM, von Wright GH, Nyman H (eds). Blackwell, OxfordGoogle Scholar
  106. Yao M (2017) Understanding the limits of deep learning. https://www.topbots.com/understanding-limits-deep-learning-artificial-intelligence/. Accessed 11 Apr 2018
  107. Zadeh LA (1950) Thinking machines—a new field in electrical engineering. Columbia Engineering QuarterlyGoogle Scholar
  108. Zarkadakis G (2015) In our own image: will artificial intelligence save or destroy us?. Rider Books, LondonGoogle Scholar
  109. Zoph B, Le QV (2016) Neural Architecture Search with Reinforcement Learning. {under review as a conference paper at ICLR 2017]. https://arxiv.org/pdf/1611.01578.pdf/. Accessed 11 Apr 2018

Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2018

Authors and Affiliations

  1. 1.Institute for Research in Anticipatory SystemsUniversity of Texas at DallasRichardsonUSA

Personalised recommendations