Symbol grounding and its implications for artificial intelligence Export

@inproceedings{citeulike:907465, abstract = {In response to Searle's well-known Chinese room argument against Strong AI (and more generally, computationalism), Harnad proposed that if the symbols manipulated by a robot were sufficiently grounded in the real world, then the robot could be said to literally understand. In this article, I expand on the notion of symbol groundedness in three ways. Firstly, I show how a robot might select the best set of categories describing the world, given that fundamentally continuous sensory data can be categorised in an almost infinite number of ways. Secondly, I discuss the notion of grounded abstract (as opposed to concrete) concepts. Thirdly, I give an objective criterion for deciding when a robot's symbols become sufficiently grounded for "understanding" to be attributed to it. This deeper analysis of what symbol groundedness actually is weakens Searle's position in significant ways; in particular, whilst Searle may be able to refute Strong AI in the specific context of present-day digital computers, he cannot refute computationalism in general.}, address = {Darlinghurst, Australia, Australia}, author = {Mayo, Michael J.}, booktitle = {ACSC '03: Proceedings of the twenty-sixth Australasian conference on Computer science}, citeulike-article-id = {907465}, citeulike-linkout-0 = {http://portal.acm.org/citation.cfm?id=783113}, keywords = {symbol-grounding}, pages = {55--60}, posted-at = {2006-10-20 06:36:24}, priority = {4}, publisher = {Australian Computer Society, Inc.}, title = {Symbol grounding and its implications for artificial intelligence}, url = {http://portal.acm.org/citation.cfm?id=783113}, year = {2003} }

TY - CONF ID - citeulike:907465 L3 - citeulike-article-id:907465 N2 - In response to Searle's well-known Chinese room argument against Strong AI (and more generally, computationalism), Harnad proposed that if the symbols manipulated by a robot were sufficiently grounded in the real world, then the robot could be said to literally understand. In this article, I expand on the notion of symbol groundedness in three ways. Firstly, I show how a robot might select the best set of categories describing the world, given that fundamentally continuous sensory data can be categorised in an almost infinite number of ways. Secondly, I discuss the notion of grounded abstract (as opposed to concrete) concepts. Thirdly, I give an objective criterion for deciding when a robot's symbols become sufficiently grounded for "understanding" to be attributed to it. This deeper analysis of what symbol groundedness actually is weakens Searle's position in significant ways; in particular, whilst Searle may be able to refute Strong AI in the specific context of present-day digital computers, he cannot refute computationalism in general. AD - Darlinghurst, Australia, Australia EP - 60 TI - Symbol grounding and its implications for artificial intelligence PB - Australian Computer Society, Inc. T2 - ACSC '03: Proceedings of the twenty-sixth Australasian conference on Computer science SP - 55 KW - symbol-grounding AU - Mayo, Michael PY - 2003/// UR - http://portal.acm.org/citation.cfm?id=783113 ER -