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Symbol Grounding is an Empirical Problem: Neural Nets are Just a Candidate Component

Harnad, Stevan (1993) Symbol Grounding is an Empirical Problem: Neural Nets are Just a Candidate Component.

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Abstract

"Symbol Grounding" is beginning to mean too many things to too many people. My own construal has always been simple: Cognition cannot be just computation, because computation is just the systematically interpretable manipulation of meaningless symbols, whereas the meanings of my thoughts don't depend on their interpretability or interpretation by someone else. On pain of infinite regress, then, symbol meanings must be grounded in something other than just their interpretability if they are to be candidates for what is going on in our heads. Neural nets may be one way to ground the names of concrete objects and events in the capacity to categorize them (by learning the invariants in their sensorimotor projections). These grounded elementary symbols could then be combined into symbol strings expressing propositions about more abstract categories. Grounding does not equal meaning, however, and does not solve any philosophical problems.

Item Type:Other
Keywords:symbol grounding, neural nets, Turing test
Subjects:Computer Science > Artificial Intelligence
Computer Science > Dynamical Systems
Computer Science > Neural Nets
Psychology > Perceptual Cognitive Psychology
Philosophy > Philosophy of Mind
ID Code:1588
Deposited By: Harnad, Stevan
Deposited On:18 Jun 2001
Last Modified:11 Mar 2011 08:54

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