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Deep Learning Applied to Scientific Discovery: A Hot Interface with Philosophy of Science

Vervoort, Louis and Shevlin, Henry and Melnikov, Alexey and Alodjants, Alexander (2021) Deep Learning Applied to Scientific Discovery: A Hot Interface with Philosophy of Science. [Preprint]

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Abstract

We scrutinize publications in automated scientific discovery using deep learning, with the aim of shedding light on problems with strong connections to philosophy of science, of physics in particular. We show that core issues of philosophy of science, related, notably, to the nature of scientific theories; the nature of unification; and of causation loom large in scientific deep learning. Therefore advances in deep learning could, and ideally should, have impact on philosophy of science, and vice versa. We suggest lines of further research, and highlight the role ‘theory-driven’ AI could have in future developments of the field.


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Item Type: Preprint
Creators:
CreatorsEmailORCID
Vervoort, Louisl.vervoort@utmn.ru
Shevlin, Henryhfs35@cam.ac.uk
Melnikov, Alexeyalexey@melnikov.info
Alodjants, Alexanderalexander_ap@list.ru
Keywords: automated scientific discovery in physics; deep learning; unification; theory construction; causation; cognitive neuroscience
Subjects: General Issues > History of Philosophy of Science
Depositing User: Dr. Louis Vervoort
Date Deposited: 14 Oct 2021 01:21
Last Modified: 14 Oct 2021 01:21
Item ID: 19676
Subjects: General Issues > History of Philosophy of Science
Date: 6 July 2021
URI: http://philsci-archive.pitt.edu/id/eprint/19676

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