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Moving Beyond Content‐Specific Computation in Artificial Neural Networks

Citation: Shea, Nicholas (2021) Moving Beyond Content‐Specific Computation in Artificial Neural Networks. Mind & Language . ISSN 1468-0017

Shea_Moving beyond CS comptn_Preprint.pdf

Creative Commons: Attribution-No Derivative Works 4.0

A new wave of deep neural networks (DNNs) have performed astonishingly well on a range of real‐world tasks. A basic DNN is trained to exhibit, in parallel, a large collection of different input‐output dispositions. While this is a good model of the way humans perform some tasks automatically and without deliberative reasoning, more is needed to approach the goal of human‐like artificial intelligence. Indeed, DNN models are increasingly being supplemented to overcome the limitations inherent in dispositional‐style computation. Examining these developments, and earlier theoretical arguments, reveals a deep distinction between two fundamentally different styles of computation, defined here for the first time: content‐ specific computation and non‐content‐specific computation. Deep episodic RL networks, for example, combine content‐specific computations in a DNN with non‐content‐specific computations involving explicit memories. Human concepts are also involved in processes of both kinds. This suggests that the remarkable success of recent AI systems, and the special power of human conceptual thinking are both due, in part, to the ability to mediate between content‐specific and non‐content‐specific computations. Hybrid systems take advantage of the complementary costs and benefits of each. Combining content‐specific and non‐content‐ specific computations both has practical benefits and provides a better model of human cognitive competence.

Creators: Shea, Nicholas (0000-0002-2032-5705) and
Subjects: Philosophy
Keywords: computation, deep neural networks, distributed representation, content‐specific, explicit memory, concepts
Divisions: Institute of Philosophy
Collections: Legal Biography
  • 19 May 2021 (accepted)
  • 5 October 2021 (published)
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