Philosophy of Cognitive Science in the Age of Deep Learning

TL;DR

This paper discusses how deep learning advances are impacting the philosophy of cognitive science, highlighting new opportunities for interdisciplinary exploration of cognition and methodological challenges.

Contribution

It surveys foundational issues where philosophy can contribute to understanding deep learning's implications for cognition.

Findings

Deep learning overcomes limitations of older connectionist models

Interdisciplinary collaboration can improve evaluation methods

Philosophical exploration of deep learning's cognitive implications is timely

Abstract

Deep learning has enabled major advances across most areas of artificial intelligence research. This remarkable progress extends beyond mere engineering achievements and holds significant relevance for the philosophy of cognitive science. Deep neural networks have made significant strides in overcoming the limitations of older connectionist models that once occupied the centre stage of philosophical debates about cognition. This development is directly relevant to long-standing theoretical debates in the philosophy of cognitive science. Furthermore, ongoing methodological challenges related to the comparative evaluation of deep neural networks stand to benefit greatly from interdisciplinary collaboration with philosophy and cognitive science. The time is ripe for philosophers to explore foundational issues related to deep learning and cognition; this perspective paper surveys key areas where their contributions can be especially fruitful.