Multiple Realizability and the Rise of Deep Learning

TL;DR

This paper examines how deep learning models exemplify multiple realizability of cognitive functions, challenging traditional views and highlighting their methodological importance in cognitive science and philosophy.

Contribution

It argues that deep neural networks exemplify multiple realizability and have significant implications for studying cognition and its physical implementations.

Findings

Deep learning models serve as plausible realizations of cognitive functions.

Multiple realizability remains significant in the context of deep learning.

Deep neural networks can inform hypotheses about cognition despite being implementation-level models.

Abstract

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.