Making AI Meaningful Again

Synthese 198 (March):2061-2081 (2021)
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Abstract

Artificial intelligence (AI) research enjoyed an initial period of enthusiasm in the 1970s and 80s. But this enthusiasm was tempered by a long interlude of frustration when genuinely useful AI applications failed to be forthcoming. Today, we are experiencing once again a period of enthusiasm, fired above all by the successes of the technology of deep neural networks or deep machine learning. In this paper we draw attention to what we take to be serious problems underlying current views of artificial intelligence encouraged by these successes, especially in the domain of language processing. We then show an alternative approach to language-centric AI, in which we identify a role for philosophy.

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Author Profiles

Jobst Landgrebe
State University of New York (SUNY)
Barry Smith
University at Buffalo

Citations of this work

On the Opacity of Deep Neural Networks.Anders Søgaard - 2023 - Canadian Journal of Philosophy:1-16.
Biomedical Ontologies.Barry Smith - 2023 - In Peter L. Elkin, Terminology, Ontology and their Implementations. Cham, Switzerland: Springer Nature. pp. 125-169.
Ontology and Cognitive Outcomes.David Limbaugh, Jobst Landgrebe, David Kasmier, Ronald Rudnicki, James Llinas & Barry Smith - 2020 - Journal of Knowledge Structures and Systems 1 (1): 3-22.

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References found in this work

Some philosophical problems from the standpoint of artificial intelligence.John McCarthy & Patrick Hayes - 1969 - In B. Meltzer & Donald Michie, Machine Intelligence 4. Edinburgh University Press. pp. 463--502.
Origins of analytical philosophy.Michael Dummett - 1993 - Cambridge: Harvard University Press.
On Clear and Confused Ideas.R. Millikan - 2001 - Cambridge Studies in Philosophy.

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