Advertisement

Topoi

pp 1–14 | Cite as

Alien Reasoning: Is a Major Change in Scientific Research Underway?

  • Thomas NicklesEmail author
Article
  • 155 Downloads

Abstract

Are we entering a major new phase of modern science, one in which our standard, human modes of reasoning and understanding, including heuristics, have decreasing value? The new methods challenge human intelligibility. The digital revolution (deep connectionist machine learning, big data, cloud computing, simulation, etc.) inspires such claims, but they are not new. During several historical periods, scientific progress has challenged traditional concepts of reasoning and rationality, intelligence and intelligibility, explanation and knowledge. The increasing intelligence of machine learning and networking is a deliberately sought, somewhat alien intelligence. As such, it challenges the traditional, heuristic foresight of expert researchers. Nonetheless, science remains human-centered in important ways—and yet many of our ordinary human epistemic activities are alien to ourselves. This fact has always been the source of “the discovery problem”. It generalizes to the problem of understanding expert scientific practice. Ironically, scientific progress plunges us ever deeper into complexities beyond our grasp. But how is progress possible without traditional realism and the intelligibility realism requires? Pragmatic flexibility offers an answer.

Keywords

The end of traditional science Scientific reasoning Heuristics Big data Deep neural networks Alien intelligence Intelligibility Scientific realism Scientific progress Expertise 

Notes

Acknowledgements

Thanks to Emiliano Ippoliti for helpful suggestions.

Compliance with Ethical Standards

Conflict of interest

Authors declare that he has no conflict of interest to declare.

References

  1. Anderson C (2008) The end of theory: The data deluge makes the scientific method obsolete. Wired Magazine 16:16-07Google Scholar
  2. Appel K, Haken W (1977) Every planar map is four colorable. Part I: Discharging. Illinois J Math 21(3):429–490Google Scholar
  3. Baird D (2004) Thing knowledge: a philosophy of scientific instruments. University of California Press, BerkeleyGoogle Scholar
  4. Bishop M, Trout JD (2005) Epistemology and the psychology of human judgment. Oxford University Press, New YorkCrossRefGoogle Scholar
  5. Brockman J (ed) (2015a) This idea must die: scientific theories that are blocking progress. Harper, New YorkGoogle Scholar
  6. Brockman J (ed) (2015b) What to think about machines that think. Harper, New YorkGoogle Scholar
  7. Calude CS, Longo G (2015) The deluge of spurious correlations in big data. http://www.di.ens.fr/users/longo/files/BigData-Calude-LongoAug21.pdf. Accessed 4 Feb 2018
  8. Cellucci C (2017) Rethinking knowledge: the heuristic view. Springer, ChamCrossRefGoogle Scholar
  9. Chang H (2012) Is water H2O? Springer, DordrechtCrossRefGoogle Scholar
  10. Collins H (2010) Tacit and explicit knowledge. University of Chicago Press, ChicagoCrossRefGoogle Scholar
  11. Daston L (1988) Classical probability in the Enlightenment. Princeton University Press, PrincetonGoogle Scholar
  12. Daston L (2016) History of science without structure. In: Richards R, Daston L (eds) Kuhn’s structure of scientific revolutions at fifty. University of Chicago Press, ChicagoGoogle Scholar
  13. Dawes R (1988) Rational choice in an uncertain world. Harcourt, New York. 2nd edn. Sage, Thousand OaksGoogle Scholar
  14. De Langhe R (2014) To specialize or to innovate? An internalist account of pluralistic ignorance in economics. Synthese 191:2499–2511Google Scholar
  15. De Regt H, Leonelli S, Eigner K (eds) (2009) Scientific understanding: philosophical perspectives. University of Pittsburgh Press, PittsburghGoogle Scholar
  16. Dear P (2009) Revolutionizing the sciences: european knowledge and its ambitions, 1500–1700, 2nd edn. Princeton University Press, PrincetonCrossRefGoogle Scholar
  17. Dennett D (1971) Intentional systems. J Philos 68:87–106CrossRefGoogle Scholar
  18. Dennett D (1995) Darwin’s dangerous idea. Simon & Schuster, New YorkGoogle Scholar
  19. Dennett D (2017) From bacteria to Bach and back: the evolution of minds. Norton, New YorkGoogle Scholar
  20. Dewey J (1929/1984) The quest for certainty. In: Boydston J (ed) John Dewey: the later works, vol 4. Southern Illinois University Press, CarbondaleGoogle Scholar
  21. Domingos P (2015) The master algorithm: how the search for the ultimate learning machine will remake our world. Basic Books, New YorkGoogle Scholar
  22. Du Châtelet É (1740/2009) Institutions de physique (translated as The foundations of physics). ParisGoogle Scholar
  23. Du Châtelet E (1739) Selected philosophical and scientific writings. In: Zinsser J, Bour I (eds and translators). University of Chicago Press, ChicagoGoogle Scholar
  24. Ericsson KA, Charness N, Feltovich P, Hoffman RR (2006) The Cambridge handbook of expertise and expert performance. Cambridge University Press, New YorkCrossRefGoogle Scholar
  25. Eubanks V (2018) Automating inequality: how high-tech tools profile, police, and punish the poor. St. Martin’s Press, New YorkGoogle Scholar
  26. Funkenstein A (1986) Theology and the scientific imagination. Princeton University Press, PrincetonGoogle Scholar
  27. Giere R (2006) Scientific perspectivism. University of Chicago Press, ChicagoCrossRefGoogle Scholar
  28. Gigerenzer G, Todd P (eds) (1999) Simple heuristics that make us smart. Oxford University Press, OxfordGoogle Scholar
  29. Glymour C, Cooper GF (eds) (1999) Computation, causation, & discovery. MIT Press, CambridgeGoogle Scholar
  30. Gomez MA, Skiba RM, Snow JC (2017) Graspable objects grab attention more than images do. Psychol Sci.  https://doi.org/10.1177/0956797617730599 Google Scholar
  31. Hacking I (2012) Language, truth and reason’ 30 years later. Stud Hist Philos Sci A 43:599–609CrossRefGoogle Scholar
  32. Heng K (2014) The nature of scientific proof in the age of simulations. Am Sci 102:174–177CrossRefGoogle Scholar
  33. Hume D (1738) A treatise of human nature. Everyman, LondonGoogle Scholar
  34. Humphreys P (2004) Extending ourselves: computational science, empiricism, and scientific method. Oxford University Press, New YorkCrossRefGoogle Scholar
  35. Ippoliti E (2008) Inferenze ampliative: Visualizzazione, analogia e rappresentazioni multiple. Lulu Press, MorrisvilleGoogle Scholar
  36. Ippoliti E, Chen P (eds) (2017) Methods and finance: a unifying view on finance, mathematics and philosophy. Springer, ChamGoogle Scholar
  37. James W (1907/1981) Pragmatism. Hackett, IndianapolisGoogle Scholar
  38. Knight W (2017) The dark secret at the heart of AI: no one really knows how the most advanced algorithms do what they do. MIT Technology Review, Cambridge, pp 55–63Google Scholar
  39. Koza J (1992) Genetic programming: on the programming of computers by means of natural selection this is the first volume of a multi-year series. MIT Press, CambridgeGoogle Scholar
  40. Kuhn TS (1962/1970) The structure of scientific revolutions, 2nd edn. Univ of Chicago Press, ChicagoGoogle Scholar
  41. Kuhn TS (1977) The essential tension. University of Chicago Press, ChicagoGoogle Scholar
  42. Kuhn TS (2000) The road since structure. University of Chicago Press, ChicagoGoogle Scholar
  43. Laudan L (1981) Science and hypothesis. Reidel, DordrechtCrossRefGoogle Scholar
  44. Leonelli S (2016) Data-centric biology: a philosophical study. University of Chicago Press, ChicagoCrossRefGoogle Scholar
  45. Loghmani RL, Caputo B, Vincze M (2017) Recognizing objections in-the-wild: where do we stand? arXiv.org:1709.05862v1. Accessed 5 Feb 2018Google Scholar
  46. Lynch MP (2016) The internet of us: Knowing more and understanding less in the age of big data. Liveright/W.W. Norton, New YorkGoogle Scholar
  47. Marcus G (2018a) Deep learning: A critical appraisal. arXiv.org:1891.00631. Accessed 5 Feb 2018Google Scholar
  48. Marcus G (2018b) In defense of skepticism about deep learning. Submitted to arXiv.orgGoogle Scholar
  49. Marcus G (2018c) Innateness, AlphZero, and artificial intelligence. arXiv.org:1801.05667. Accessed 5 February 2018Google Scholar
  50. McLuhan M (1964) Understanding media: the extensions of man. McGraw-Hill, New YorkGoogle Scholar
  51. Meehl P (1954) Clinical versus statistical prediction: a theoretical analysis and a review of the evidence. University of Minnesota Press, MinneapolisCrossRefGoogle Scholar
  52. Meikle J (2005) Ghost in the machine: why it’s hard to write about design. Technol Culture 46(2):385–392CrossRefGoogle Scholar
  53. Newell A, Simon HA (1972) Human problem solving. Prentice-Hall, Englewood CliffsGoogle Scholar
  54. Nguyen A, Yosinski J, Clune J (2015) Deep neural networks are easily fooled: High Confidence predictions for unrecognizable images. 2015 IEEE Conference on computer vision and pattern recognition (CVPR), 427–436Google Scholar
  55. Nickles T (1987) From natural philosophy to metaphilosophy of science. In Kargon R, Achinstein P (eds) Kelvin's baltimore lectures and modern theoretical physics: historical and philosophical perspectives. MIT Press, Cambridge, MA, pp 507–541Google Scholar
  56. Nickles T (2017) Strong realism as scientism: are we at the end of history? In Boudry M, Pigliucci M (eds) Science unlimited? The challenges of scientism. Univ of Chicago Press, ChicagoGoogle Scholar
  57. Nickles T (2018) TTT: a fast heuristic to new theories? In Danks D, Ippoliti E (eds) Building theories. Springer, Cham, Switzerland, pp 169–118CrossRefGoogle Scholar
  58. Nickles T (forthcoming) Do cognitive illusions make scientific realism deceptively attractive? In: González WJ (ed) New approaches to scientific realismGoogle Scholar
  59. Nielson M (2012) Reinventing discovery: the new era of networked science. Princeton University Press, PrincetonGoogle Scholar
  60. Noble SU (2018) Algorithms of oppression: how search engines reinforce racism. New York University Press, New YorkGoogle Scholar
  61. Norman D (1993) Things that make us smart: defending human attributes in the age of the machine. Addison-Wesley, ReadingGoogle Scholar
  62. Norman D (2004) Emotional design: why we love (or hate) everyday things. Basic Books, New YorkGoogle Scholar
  63. O’Neil C (2016) Weapons of math destruction: how big data increases inequality and threatens democracy. Crown, New YorkGoogle Scholar
  64. Pasquale F (2015) The black box society: the secret algorithms that control money and information. Harvard University Press, CambridgeCrossRefGoogle Scholar
  65. Pearl J (2000) Causality: models, reasoning and inference, 2nd edn. Cambridge University Press, CambridgeGoogle Scholar
  66. Petroski H (2003) Small things considered: why there is no perfect design. Alfred Knopf, New YorkGoogle Scholar
  67. Polanyi M (1958) Personal knowledge. University of Chicago Press, ChicagoGoogle Scholar
  68. Rescher N (1984) The limits of science. University of California Press, BerkeleyGoogle Scholar
  69. Rheinberger H-G (1997) Toward a history of epistemic things: synthesizing proteins in the test tube. Stanford University Press, StanfordGoogle Scholar
  70. Rozenblit L, Keil F (2002) The misunderstood limits of folk science: an illusion of explanatory depth. Cogn Sci 26(5):521–562CrossRefGoogle Scholar
  71. Ryle G (1949) The concept of mind. Hutchinson, LondonGoogle Scholar
  72. Schickore J, Steinle F (2006) Revisiting discovery and justification: historical and philosophical perspectives on the context distinction. Springer, DordrechtCrossRefGoogle Scholar
  73. Shapere D (1984) Reason and the search for knowledge. Reidel, DordrechtGoogle Scholar
  74. Shapin S, Schaffer S (1985) Leviathan and the air-pump. Princeton University Press, PrincetonGoogle Scholar
  75. Shapiro B (1983) Probability and certainty in seventeenth-century England. Princeton University Press, PrincetonGoogle Scholar
  76. Somers J (2017) Is AI riding a one-trick pony? MIT Technology Review, CambridgeGoogle Scholar
  77. Spirtes P, Glymour C, Scheines R (2000) Causation, prediction, and search, 2nd edn. MIT Press, CambridgeGoogle Scholar
  78. Stanley J (2011) Know how. Oxford University Press, OxfordCrossRefGoogle Scholar
  79. Sweeney P (2017) Deep learning, alien knowledge and other UFOs. https://medium.com/inventing-intelligent-machines/machine-learning-alien-knowledge-and-other-ufos-1a44c66508d1. Accessed Nov 18 2017
  80. Szegedy C, Zaremba W et al (2014) Intriguing properties of neural networks. arXiv.org 1312.6199. Accessed 5 Feb 2018Google Scholar
  81. Teller P (2001) Twilight of the perfect model model. Erkenntnis 55(3):393–415CrossRefGoogle Scholar
  82. Tishby N, Zaslavsky N (2015) Deep learning and the information bottleneck principle. arXiv.org:1503.02406v1 [cs.LG]. Accessed 5 February 2018Google Scholar
  83. Trout JD (2002) Scientific explanation and the sense of understanding. Philos Sci 69:212–233CrossRefGoogle Scholar
  84. Wachter-Boettcher S (2017) Technically wrong: sexist apps, biased algortihms, and other threats of toxic tech. Norton, New YorkGoogle Scholar
  85. Wilson T (2002) Strangers to ourselves. Harvard University/Belknap Press, Cambridge, MAGoogle Scholar
  86. Weinberger D (2014) Too big to know: rethinking knowledge. Basic Books, New YorkGoogle Scholar
  87. Weinberger D (2017) Alien knowledge: when machines justify knowledge. Wired MagazineGoogle Scholar
  88. Wimsatt WC (2007) Re-engineering philosophy for limited beings. Harvard University Press, CambridgeGoogle Scholar
  89. Wise MN (2011) Science as (historical) narrative. Erkenntnis 75:349–376CrossRefGoogle Scholar
  90. Wittgenstein L (1953) Philosophical investigations. Macmillan, LondonGoogle Scholar
  91. Zenil H et al (2017) What are the main criticism and limitations of deep learning? https://www.quora.com/What-are-the-main-criticsm-and-limitations-of-deep-learning. Accessed 5 Feb 2018

Copyright information

© Springer Science+Business Media B.V., part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Philosophy EmeritusUniversity of Nevada, RenoRenoUSA

Personalised recommendations