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

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.