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Machine intelligence: a chimera

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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.

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Notes

  1. From the Book of Isaiah, the Prophet, Chap. 44:19–20 (abridged).

  2. Inspired by this notion, Tod Machover, of the MIT Media Lab, wrote and composed the opera Death and the Powers in 2011.

  3. Das Entscheidungsproblem is gelöst, wenn man ein Verfahren kennt, das bei einem vorgelegten logischen Ausdruck durch endlich viele Operationen die Entscheidung über die Allgemeingultigkeit Erfüllbarkeit erlaubt (Hilbert and Ackermann 1928).

  4. United Artists’ 1960. The title comes from Proverbs 11:9.

  5. International Conference, Anticipation – Learning from the past. Early Soviet/Russian contributions to a science of anticipation. Hanse Institute for Advanced Study, Delmenhorst, Germany, September 1–3, 2014.

  6. Nobel Prize for decoding brain’s sense of place. Discoverers of brain’s navigation system get physiology Nobel. See: http://www.nature.com/news/nobel-prize-for-decoding-brain-s-sense-of-place-1.16093.

  7. Н.А. Бернштейн. Физиология движений и активность, 1990.

  8. Does Deep Blue use artificial intelligence? https://www.research.ibm.com/deepblue/meet/html/d.3.3a.ht.

  9. The 2017 Nobel Prize in Physics was awarded for decisive contributions to the LIGO detector and the observation of gravitational waves.

  10. Reproduced on Edge.org a report from the Süddeutsche Zeitung, Oct. 13, 2017. https://www.edge.org/conversation/andrian_kreye-we-are-in-the-presence-of-a-formidable-creature.

  11. Kyle McDonald gives many examples. See http://www.kylemcdonald.net/.

  12. Friedrich Nietsche, Thus Spoke Zarathustra: A Book for All and None, composed in four parts between 1883 and 1885 and published between 1883 and 1891.

  13. For the record: the no-data starting point has been attempted before. Indeed, the “self-learning evolutionary chess program” (Fogel 2004) and the subsequent steps (2005, 2006) in the direction of a “New Philosophy of Machine Intelligence” (Fogel 1995, 2006) deserve at least some reference. The evaluation function is at the core of evolutionary computation.

  14. http://web.mit.edu/16.unified/www/FALL/thermodynamics/notes/node26.html.

  15. Hacker News: Google supercharges machine learning tasks with TPU custom chip (googleblg.com), https://news.ycombinator.com/item?id=11724763. See also: The relationship between clockspeed and power consumption is nonlinear, https://electronics.stackexchange.com/questions/122050/what-limits-cpu-speed.

  16. See http://opsbug.com/deep-learning.

References

  • 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

  • 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

  • Arshavsky YI (1991) Gelfand on mathematics and neurophysiology. http://israelmgelfand.com/bio_work/arshavsky_biomed.pdf

  • Bell P (2005) Psychology or semiotics: persons or subjects? Passau Schriften zur Psychologiegeschiche 13:85–104

    Google Scholar 

  • 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, Moscow

    Google Scholar 

  • Bernstein NA (1967) The co-ordination and regulation of movements. Pergamon Press, Oxford

    Google Scholar 

  • Bolter JD (1984) Turing’s man: western culture in the computer age. University of North Carolina Press, Chapel Hill

    Google Scholar 

  • Cannon WB (1932) The wisdom of the body. W.W. Norton, New York

    Book  Google Scholar 

  • Cannon WB (1945) The way of an investigator. W.W. Norton, New York

    Google Scholar 

  • 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/

  • Church A (1936a) An unsolvable problem of elementary number theory. Am J Math 18

  • Church A (1936b) A Note on the Entscheidungsproblem. J Symb Logic 1(1):40–41

    Article  MATH  Google Scholar 

  • Church A (1936c) Correction to A Note on the Entscheidungsproblem. J Symb Logic 1(3):101–102

    Article  MATH  Google Scholar 

  • Dalakov G History of computers and computing. http://history-computer.com/ModernComputer/thinkers/Peirce.html

  • 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 3–34

    Chapter  Google Scholar 

  • Dieters OFC (1865) Untersuchungen über Gehirn u. Rückenmark des Menschen und der Säugethiere. Vieweg, Braunschweig

    Book  Google Scholar 

  • Dreyfus HL (1965) Alchemy and artificial intelligence, RAND paper P3244 https://www.rand.org/content/dam/rand/pubs/papers/2006/P3244.pdf

  • Dreyfus H (1972) What computers can’t do. A critique of artificial reason. Harper & Row, New York

    Google Scholar 

  • Dreyfus H (1979) What computers can’t do. A critique of artificial reason (revised edition). Harper & Row, New York

    Google Scholar 

  • 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, Dordrecht

    Google Scholar 

  • 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/London

    Google Scholar 

  • Ellis GFR (2012) Recognising top-down causation. https://arxiv.org/abs/1212.2275. Accessed Dec 9

  • Elsasser WM (1966) Atom and organism. A new approach to theoretical biology. Princeton University Press, Princeton

    Google Scholar 

  • Elsasser WM (1987) Reflections on a theory of organisms. Éditions Orbis Publishing, Frelighsburg

    Google Scholar 

  • 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)

  • Fogel DB (1995, 2006) Evolutionary computation: toward a new philosophy of machine intelligence, 3rd edn. Wiley, Hoboken

    MATH  Google Scholar 

  • Fogel DB (2004) A self-learning evolutionary chess program. Proc IEEE 92(12):1947–1954. http://ieeexplore.ieee.org/document/1360168/

  • Gatys LA, Ecker AS, Bethge M (2015) A neural algorithm for artistic style. https://arxiv.org/abs/1508.06576. Accessed Sept

  • Gödel K (1936) Über die Länge von Beweisen. Ergebnisse eines mathematischen Kolloquiums Heft 7:23–24

    MATH  Google Scholar 

  • Gödel K (1972) Some remarks on the undecidability results, collected works II. Oxford University Press, Oxford, pp 305–306

    Google Scholar 

  • 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

  • Graves A, Wayne G, Danihelka I (2014) Neural turing machines. https://arxiv.org/pdf/1410.5401

  • Graves et al (2016) Hybrid computing using a neural network with dynamic external memory. Nature 538(7626):471–476

    Article  Google Scholar 

  • 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

  • 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

  • Hawking S (2014) Stephen Hawking warns artificial intelligence could end mankind. Interview with R.C. Jones http://www.bbc.com/news/technology-30290540

  • Hilbert D, Ackermann W (1928) Grundzüge der theoretische Logik. Verlag von Julius Springer, Berlin

    MATH  Google Scholar 

  • Hobbes T (1968) Leviathan. Penguin, Harmondsworth

    Google Scholar 

  • Hodgkin AL, Huxley AF (1952) A quantitative description of membrane current and its application to conduction and excitation in nerve. J Physiol 17(4):500–544

    Article  Google Scholar 

  • Hopcroft JE, Ullman JD (1979) Introduction to automata theory, languages, and computation. Addison-Wesley, New York

    MATH  Google Scholar 

  • Hopfield JJ (1982) Neural networks and physical systems with emergent collective computational abilities. Proc Natl Acad Sci USA 9(8):2554–2558

    Article  MathSciNet  MATH  Google Scholar 

  • Hotchkiss RD (1958) Concepts of biology. In: Gerard RW (ed) Behavioral Science, 3:2

  • 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

  • Kline RR (2015) The cybernetics moment: or why we call our age the information age. Johns Hopkins University Press, Baltimore

    MATH  Google Scholar 

  • Kohonen T (1988) An introduction to neural computing. Neural Netw 1(1):3–16

    Article  Google Scholar 

  • Kurzweil R (2005) The singularity is near. Viking, New York

    Google Scholar 

  • Kurzweil R (2013) Immortality by 2045. Global Future 2045 Congress 2013. https://www.youtube.com/watch?v=qlRTbl_IB

  • 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

    Article  Google Scholar 

  • LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521(7553):436–444

    Article  Google Scholar 

  • 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)

  • Lin HW, Tegmark M, Rolnick D (2017) Why does deep and cheap learning work so well?. https://arxiv.org/abs/1608.08225

  • 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 

  • 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/Heidelberg

    Google Scholar 

  • McCarthy J (1976) An unreasonable book. http://jmc.stanford.edu/artificial-intelligence/reviews/weizenbaum.pdf

  • McCarthy J, Hayes PJ (1969) Some philosophical problems from the standpoint of artificial intelligence. http://www-formal.stanford.edu/jmc/mcchay69.pdf

  • McCulloch WS, Pitts W (1943) A logical calculus of the ideas immanent in nervous activity. Bull Math Biophysiol 5:115–133

    Article  MathSciNet  MATH  Google Scholar 

  • 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

  • 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

  • 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 154–166

  • Nadin M (1997) The civilization of Illiteracy. Dresden University Press, Dresden

    Google Scholar 

  • 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 3–47

    Google Scholar 

  • Nadin M (2003) Anticipation—The End Is Where We Start From. Lars Müller Press, Baden

    Google Scholar 

  • 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):3–33

    Article  MATH  Google Scholar 

  • Nadin M (2013) The intractable and the undecidable—computation and anticipatory processes. Int J Appl Res Inform Technol Comput 4(3):99–121

    Article  MathSciNet  Google Scholar 

  • Nadin M (2014) Complexity, quantum computation and anticipatory processes. Comput Commun Collab 2(1):6–34. (DOIC: 2292-1036-2014-01-003-18)

    Article  Google Scholar 

  • Nadin M (ed) (2015a) Learning from the past. Early Soviet/Russian contributions to a science of anticipation. Cognitive Science Monographs, vol 25. Springer, Cham

    Google Scholar 

  • 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 329–340

    Google Scholar 

  • 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 283–339

    Chapter  Google Scholar 

  • Nadin M (2016) Anticipation and the brain, anticipation and medicine. Springer International Publishers, Cham, pp 147–175

    Google Scholar 

  • 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 645–659

    Google Scholar 

  • Nadin M (2017b) In folly ripe. In reason rotten. Putting machine theology to rest. https://arxiv.org/abs/1712.04306

  • 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

    Article  Google Scholar 

  • Nietsche F (1883–1891) Also sprach Zarathustra: Ein Buch für Alle und Keinen. Chemnitz: Verlag von Ernst Schmeitzner

  • 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

  • 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

  • Peirce CS (1887a), Logical machines. Am J Psychol I:65–70

    Google Scholar 

  • 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 1886–1890

    Google Scholar 

  • Peirce Edition Project (ed) (1998) The essential Peirce. Selected philosophical writings, vol 2, pp 1893–1913

  • Polanyi M (1966) The tacit dimension. The University of Chicago Press, Chicago

    Google Scholar 

  • Purkyně JE (1823) Commentatio de examine physiologico organi visus et systematis cutanei. University of Breslau Press, Breslau

    Google Scholar 

  • Radford A, Jozefowicz R, Sutzkever I (2017) Learning to generate reviews and discovering sentiment. https://arxiv.org/abs/1704.01444

  • 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, Madrid

    Google Scholar 

  • Rosen R (1972) Some relational cell models: the metabolism-repair systems, foundations of mathematical biology, vol 2. Academic Press, New York

    MATH  Google Scholar 

  • Rosen R (1999) Essays on life itself. Columbia University Press, New York

    Google Scholar 

  • 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, Cambridge

    Google Scholar 

  • Sabour S, Frosst N, Hinton GE (2017) Dynamic routing between capsules. arXiv:1710.09829. Submitted on 26 Oct 2017

  • 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

  • Scheffler I (2004) Gallery of scholars. A philosopher’s recollections. Kluwer Academic Publishers, Doredrecht/Boston

    Google Scholar 

  • Schrödinger E (1951) What is life? The physical aspect of the living cell. Cambridge University Press, Cambridge

    MATH  Google Scholar 

  • 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 

  • Siegelmann HT, Sontag ED (1991) Turing computability with neural nets. Appl Math Lett 4(6)77–80

    Article  MathSciNet  MATH  Google Scholar 

  • 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

  • Solomonoff R (1956) Notes. http://raysolomonoff.com/dartmouth/boxbdart/dart56ray622716talk710.pdf

  • 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, Chichester

    Google Scholar 

  • Thagard P (1986) Charles Peirce, Sherlock Holmes, and Artificial Intelligence. Review of the sign of three. In: Eco U, Sebeok TA (eds), Semiotics, vol 60 289–295

  • 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)

  • 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

  • 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, Oxford

  • 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

  • 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, Cambridge

    Google Scholar 

  • Wang H, Raj B (2017) On the origin of deep learning. arXiv:1702.07800v4. Accessed 11 Apr 2018

  • 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

  • Weizenbaum J (1975) Computer power and human reason: from judgment to calculation. W. H. Freeman & Co, New York

    Google Scholar 

  • Werbos PJ (2008) Bell’s theorem, many worlds and backwards-time physics: not just a matter of interpretation. Int J Theor Phys 47(11):2862–2874

    Article  MathSciNet  MATH  Google Scholar 

  • 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

  • Whitehead AN (1992) Science and the Modern World. Free Press, New York

    MATH  Google Scholar 

  • Wittgenstein L (1980) Remarks on the philosophy of psychology, vol 1. Anscombe GEM, von Wright GH, Nyman H (eds). Blackwell, Oxford

    Google Scholar 

  • Yao M (2017) Understanding the limits of deep learning. https://www.topbots.com/understanding-limits-deep-learning-artificial-intelligence/. Accessed 11 Apr 2018

  • Zadeh LA (1950) Thinking machines—a new field in electrical engineering. Columbia Engineering Quarterly

  • Zarkadakis G (2015) In our own image: will artificial intelligence save or destroy us?. Rider Books, London

    Google Scholar 

  • 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

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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.

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    Mihai Nadin

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Chimera: something hoped or wished for but in fact is illusory.

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Nadin, M. Machine intelligence: a chimera. AI & Soc 34, 215–242 (2019). https://doi.org/10.1007/s00146-018-0842-8

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