Tuesday, December 18, 2007
Solving the Symbol Grounding Problem with Neural Nets
GROUNDING SYMBOLS IN THE ANALOG WORLD WITH NEURAL NETS by Stevan Harnad
"Abstract
Searle's Chinese Room Argument (that rule-based symbol manipulation is not enough for symbol-understanding) is based on a symptom of the Symbol Grounding Problem (that rule-based symbol manipulation is circular and ungrounded). Symbols must be grounded directly in the capacity to identify and interact with the objects they designate. One candidate way to do this is to use neural nets to try to give a robot Turing-scale sensorimotor capacities congruent with its Turing-scale linguistic capacities. Such a grounded hybrid symbolic/dynamic robot would be immune to Searle's Chinese Room Argument."
"Abstract
Searle's Chinese Room Argument (that rule-based symbol manipulation is not enough for symbol-understanding) is based on a symptom of the Symbol Grounding Problem (that rule-based symbol manipulation is circular and ungrounded). Symbols must be grounded directly in the capacity to identify and interact with the objects they designate. One candidate way to do this is to use neural nets to try to give a robot Turing-scale sensorimotor capacities congruent with its Turing-scale linguistic capacities. Such a grounded hybrid symbolic/dynamic robot would be immune to Searle's Chinese Room Argument."
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