Moving beyond content‐specific computation in artificial neural networks

Mind and Language 38 (1):156-177 (2021)
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Abstract

A basic deep neural network (DNN) is trained to exhibit a large set of input–output dispositions. While being a good model of the way humans perform some tasks automatically, without deliberative reasoning, more is needed to approach human‐like artificial intelligence. Analysing recent additions brings to light a distinction between two fundamentally different styles of computation: content‐specific and non‐content‐specific computation (as first defined here). For example, deep episodic RL networks draw on both. So does human conceptual reasoning. Combining the two takes advantage of the complementary costs and benefits of each. It also offers a better model of human cognitive competence.

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Nicholas Shea
School of Advanced Study, University of London

Citations of this work

Distributed traces and the causal theory of constructive memory.John Sutton & Gerard O'Brien - 2023 - In Current Controversies in the Philosophy of Memory. Routledge. pp. 82-104. Translated by Andre Sant' Anna, Christopher McCarroll & Kourken Michaelian.

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