Grounding Symbols in the Analog World with Neural Nets

Harnad, Stevan (1993). « Grounding Symbols in the Analog World with Neural Nets ». Think, 2(1), pp. 12-78.

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Harnad's main argument can be roughly summarised as follows: due to Searle's Chinese Room argument, symbol systems by themselves are insufficient to exhibit cognition, because the symbols are not grounded in the real world, hence without meaning. However, a symbol system that is connected to the real world through transducers receiving sensory data, with neural nets translating these data into sensory categories, would not be subject to the Chinese Room argument. Harnad's article is not only the starting point for the present debate, but is also a contribution to a longlasting discussion about such questions as: Can a computer think? If yes, would this be solely by virtue of its program? Is the Turing Test appropriate for deciding whether a computer thinks?

Type: Article de revue scientifique
Mots-clés ou Sujets: catégorisation, computation, apprentissage, langage, ancrage symbolique, évolution, intelligence artificielle, cognition, réseaux neuronaux, perception categorielle, Searle, Turing, sciences cognitives
Unité d'appartenance: Faculté des sciences humaines > Département de psychologie
Instituts > Institut des sciences cognitives (ISC)
Déposé par: Stevan Harnad
Date de dépôt: 26 sept. 2007
Dernière modification: 20 avr. 2009 14:27
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