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

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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.

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Notes

  1. As excerpted in translation, in Du Châtelet (1740/2009). A bit later she is more positive that basic truths have already been found. Like many of her day, she places metaphysics at “the summit of the edifice” of science. Thanks to Katherine Brading for calling this passage to my attention.

  2. About rejecting the traditional conception of knowledge, Kuhn (2000, p. 111) remarked: “Perhaps knowledge, properly understood, is the product of the very process these new studies describe. I think something of that sort is the case”. He was referring to the micro-sociological, social constructivist studies, although he believed they came to the wrong conclusions about scientific knowledge. The early Kuhn (1970) had similar views on the rationality of science: the historical trajectory of the sciences sets the standard for what counts as progress and rationality, not vice versa.

  3. For a computer to justify a knowledge claim is not in itself new. The first use of a computer to justify a major mathematical conjecture was the proof of the four-color theorem for maps, in 1977, by Appel and Haken (see their 1977). The computer program assisted them by running systematically through all possible cases, a task that would have been nearly impossible by hand. The current digital revolution is, of course, a huge leap beyond the capabilities of those days.

  4. In Brockman (2015b), a long series of short pieces by many authors from the online Edge.org conversation.

  5. Dennett (2017, Chap. 4) notes that being able to do something significant does not imply explicit know-how. See below.

  6. Symptomatic of the crude state of today’s deep learning (as compared with the ultimate goals) is the report that Google and Facebook have hired thousands of people to supplement algorithm-based search for inappropriate web content.

  7. Among philosophers, see, e.g., Bishop and Trout (2005).

  8. For entry into the styles of reasoning discussion, see Hacking (2012), his contribution to a special issue of Synthese on the topic.

  9. Today, leading physical scientists typically write about such topics not in formal papers but in informal settings such as the popular books edited by Brockman (e.g., 2015a).

  10. See the short piece by Luca de Biase in Brockman (2015b) and Emiliano Ippoliti’s contribution on “dark data” in Ippoliti and Chen (2017).

  11. And that’s just logic and math. Historiography is even worse.

  12. What counts as understanding is itself a contested issue. See Trout (2002) and the essays in De Regt et al. (2009).

  13. Perhaps I am wrong, but I think of shallow correlations as triggering responses similar to predators’ cognitive responses to the “eyes” on butterfly wings, leading to the action-decision “not-food,” or at least, “not-safe-food”. Similarly for peahens appraising the virility of peacocks on the basis of the number of “eyes” on their spread tails. These are evolved heuristics that are fast and frugal in something like Gigerenzer’s sense, but they convey no explanatory depth and are false, strictly speaking.

  14. See also Eubanks (2018), Noble (2018), and Pasquale (2015). As I write this, several tech people are meeting at a San Francisco event called “Data for Good Exchange,” to consider a digital ethics code or digital “Hippocratic Oath” as a form of self-regulation of the industry.

  15. There are other, more generic challenges to machine learning that must be addressed in individual applications (Domingos 2015). A major one is overfitting—finding significant patterns where there are none. Overfitting makes correct generalization difficult, whereas underfitting does the reverse. Calude and Longo (2015) worry about “the deluge of spurious correlations in big data”. A third challenge is scaling, one form of which is plagued by “the curse of dimensionality”. As dimensions increase, the data density, for a given data set, decreases exponentially. And as dimensions increase, say in robotic control, efficiency also decreases exponentially, e.g., when every orientation of each finger joint needs to be explicitly controlled. “The catastrophic forgetting problem” occurs when new learning overwrites old, although some forgetting is necessary for learning, especially for generalization (Tishby and Zaslavsky 2015). A practical problem is that much deep learning remains terribly expensive. This may well change, although the size of big data from world-wide sensors also increases at a tremendous rate.

  16. But not by those epistemologists who take seriously the work on bounded rationality (as Herbert Simon termed it), heuristic judgment, cognitive biases, and behavioral economics.

  17. See Norman (1993, 2004), Petroski (2003), and Meikle (2005).

  18. Simon’s early model, based on studying human problem-solving protocols, was a “conscious model” that problematically assumes we have access to the sources of our thoughts and decisions. Simon became aware of the problem, and knowledge engineering exacerbated it. The gist of my paper is that we philosophers need to become more aware of it!

  19. For a small sample, see, e.g., Rheinberger (1997), Collins (2010), Chang (2012), Leonelli (2016). See also Ericcson et al. (2006).

  20. I am sympathetic to the naturalistic approach to these topics in the Ippoliti and Cellucci articles in this issue (see also Ippoliti 2008 and; Cellucci 2017). However, I am less confident that the complex neural-causal processes even in visual processing can be reduced to humanly intelligible rules. This is not the place to engage theories of vision or theories of practice.

  21. Some groundbreaking work has been done by scientific philosophers nearly twenty years ago, e.g., Glymour and Cooper (1999) and Spirtes et al. (2000). Pearl (2000) has been fundamental.

  22. More work is needed to sort out different sorts of unintelligibility. Complex as they are, we don’t find the entities and processes to which molecular biologists generally appeal to be as weirdly alien as those in fundamental physics.

  23. That is, rhetorical tropes—the ancient enemy of logic.

  24. I do not deny the heuristic value of individual, intentional realism. What bothers me is strong social or community realism in complex domains, the idea that the specialist community must agree on the near truth and the metaphysical interpretation of anything that is “licensed” as a product on which others may build. Recall the individual, intentional realism of Popper, who, simultaneously, vehemently denied that mature science today is close to the truth. He was not a strong realist in my sense. We can have realist-inspired heuristics without commitment to strong realism.

  25. I have argued for the latter points in Nickles (2017, 2018, forthcoming)  and elsewhere. See also my personal recollections in this special issue, where I explain why, because of the strong realist tenor of the achievement term ‘scientific discovery’, I now prefer to speak of scientific innovation and of creative work at research frontiers. Standard analytic epistemology has not contributed much to what I call “frontier epistemology”.

  26. Ryle (1949) was a major, early exception. Recently, Stanley (2011) has rejected Ryle’s arguments and argued that knowledge-how is reducible to knowledge-that. It seems to me that we are very far, scientifically, from being able to make that sort of case. Even if he has succeeded in refuting Ryle’s ordinary language arguments, that is not enough.

  27. On the two-context distinction, see Schickore and Steinle (2006). My own, Dennett-like view involves a variation-selection process that cannot be reduced to logic “all the way down”.

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Acknowledgements

Thanks to Emiliano Ippoliti for helpful suggestions.

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  1. Department of Philosophy Emeritus, University of Nevada, Reno, Reno, NV, 89557, USA

    Thomas Nickles

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Nickles, T. Alien Reasoning: Is a Major Change in Scientific Research Underway?. Topoi 39, 901–914 (2020). https://doi.org/10.1007/s11245-018-9557-1

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