Miller, Ryan (2021) Does Artificial Intelligence Use Private Language? [Preprint]

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Abstract

Wittgenstein’s Private Language Argument holds that language requires rule-following, rule following requires the possibility of error, error is precluded in pure introspection, and inner mental life is known only by pure introspection, thus language cannot exist entirely within inner mental life. Fodor defends his Language of Thought program against the Private Language Argument with a dilemma: either privacy is so narrow that internal mental life can be known outside of introspection, or so broad that computer language serves as a counter-example. I suggest that the developing field of artificial intelligence (deep learning neural networks) tends to vitiate Fodor’s defense and hence vindicate the Private Language Argument. The first horn of Fodor’s dilemma requires language to encompass genuinely internal mental life, i.e. non-projected intentional states, which are not exhibited in classical machine learning but only by deep learning neural networks (artificial intelligence). Such networks act as black boxes, however, whose state cannot be understood by tracking the changes in their supervenience bases without shared context, and that shared context introduces the possibility of error. The language of artificial intelligence is not private.

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Item Type:	Preprint
Creators:	Creators Email ORCID Miller, Ryan 66millerr@cua.edu 0000-0003-0268-2570
Keywords:	private language, artificial intelligence, language of thought, computational theory of mind, Fodor, Wittgenstein
Subjects:	Specific Sciences > Cognitive Science > Computation Specific Sciences > Artificial Intelligence Specific Sciences > Cognitive Science > Concepts and Representations Specific Sciences > Artificial Intelligence > Machine Learning
Depositing User:	Ryan Miller
Date Deposited:	07 Nov 2022 15:34
Last Modified:	07 Nov 2022 15:34
Item ID:	21369
Subjects:	Specific Sciences > Cognitive Science > Computation Specific Sciences > Artificial Intelligence Specific Sciences > Cognitive Science > Concepts and Representations Specific Sciences > Artificial Intelligence > Machine Learning
Date:	2021
URI:	https://philsci-archive.pitt.edu/id/eprint/21369

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