Multiple Realizability and the Rise of Deep Learning
It addresses philosophical and methodological issues in cognitive science, offering an incremental perspective on how deep learning renews the relevance of multiple realizability.
This paper explores the implications of deep learning models for the multiple realizability thesis in philosophy, challenging the view that mind study must be independent of its implementation in brains or artificial systems, and suggests deep neural networks can aid in cognitive science hypotheses.
The multiple realizability thesis holds that psychological states may be implemented in a diversity of physical systems. The deep learning revolution seems to be bringing this possibility to life, offering the most plausible examples of man-made realizations of sophisticated cognitive functions to date. This paper explores the implications of deep learning models for the multiple realizability thesis. Among other things, it challenges the widely held view that multiple realizability entails that the study of the mind can and must be pursued independently of the study of its implementation in the brain or in artificial analogues. Although its central contribution is philosophical, the paper has substantial methodological upshots for contemporary cognitive science, suggesting that deep neural networks may play a crucial role in formulating and evaluating hypotheses about cognition, even if they are interpreted as implementation-level models. In the age of deep learning, multiple realizability possesses a renewed significance.