Symbol grounding is an empirical problem: Neural nets are just a candidate component
| 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 | |||||||||
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Similar books and articlesStevan Harnad (2002). Symbol Grounding and the Origin of Language. In Matthias Scheutz (ed.), Computationalism: New Directions. MIT Press.
Stevan Harnad (1995). Does Mind Piggyback on Robotic and Symbolic Capacity? In H. Morowitz & J. Singer (eds.), The Mind, the Brain, and Complex Adaptive Systems. Addison Wesley.
Stevan Harnad (1992). Connecting Object to Symbol in Modeling Cognition. In A. Clark & Ronald Lutz (eds.), Connectionism in Context. Springer-Verlag.
Stevan Harnad (1994). Computation is Just Interpretable Symbol Manipulation; Cognition Isn't. Minds and Machines 4 (4):379-90.
Stevan Harnad (1995). Grounding Symbols in Sensorimotor Categories with Neural Networks. Institute of Electrical Engineers Colloquium on "Grounding Representations.
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