May 19, 2015 03:37 PM
http://what-when-how.com/artificial-inte...elligence/
EXCERPT: The symbol grounding problem aroused from the notice that symbol systems manipulated structures that could be associated with things in the world by an observer operating the system, but not by the system itself. The quest for symbol grounding processes is concerned with understanding processes which could enable the connection of these purely symbolic representations with what they represent in fact, which could be directly, or by means of other grounded representations....
http://www.scholarpedia.org/article/Symb...ng_problem
EXCERPT: The Symbol Grounding Problem is related to the problem of how words (symbols) get their meanings, and hence to the problem of what meaning itself really is. The problem of meaning is in turn related to the problem of consciousness, or how it is that mental states are meaningful. According to a widely held theory of cognition, "computationalism," cognition (i.e., thinking) is just a form of computation. But computation in turn is just formal symbol manipulation: symbols are manipulated according to rules that are based on the symbols' shapes, not their meanings. How are those symbols (e.g., the words in our heads) connected to the things they refer to? It cannot be through the mediation of an external interpreter's head, because that would lead to an infinite regress, just as my looking up the meanings of words in a (unilingual) dictionary of a language that I do not understand would lead to an infinite regress. The symbols in an autonomous hybrid symbolic+sensorimotor system -- a Turing-scale robot consisting of both a symbol system and a sensorimotor system that reliably connects its internal symbols to the external objects they refer to, so it can interact with them Turing-indistinguishably from the way a person does -- would be grounded. But whether its symbols would have meaning rather than just grounding is something that even the robotic Turing Test -- hence cognitive science itself -- cannot determine, or explain....
http://users.ecs.soton.ac.uk/harnad/Pape...oblem.html
EXCERPT: There has been much discussion recently about the scope and limits of purely symbolic models of the mind and about the proper role of connectionism in cognitive modeling. This paper describes the "symbol grounding problem": How can the semantic interpretation of a formal symbol system be made intrinsic to the system, rather than just parasitic on the meanings in our heads? How can the meanings of the meaningless symbol tokens, manipulated solely on the basis of their (arbitrary) shapes, be grounded in anything but other meaningless symbols? The problem is analogous to trying to learn Chinese from a Chinese/Chinese dictionary alone. A candidate solution is sketched: Symbolic representations must be grounded bottom-up in nonsymbolic representations of two kinds: (1) "iconic representations" , which are analogs of the proximal sensory projections of distal objects and events, and (2) "categorical representations" , which are learned and innate feature-detectors that pick out the invariant features of object and event categories from their sensory projections. Elementary symbols are the names of these object and event categories, assigned on the basis of their (nonsymbolic) categorical representations. Higher-order (3) "symbolic representations" , grounded in these elementary symbols, consist of symbol strings describing category membership relations (e.g., "An X is a Y that is Z"). Connectionism is one natural candidate for the mechanism that learns the invariant features underlying categorical representations, thereby connecting names to the proximal projections of the distal objects they stand for. In this way connectionism can be seen as a complementary component in a hybrid nonsymbolic/symbolic model of the mind, rather than a rival to purely symbolic modeling. Such a hybrid model would not have an autonomous symbolic "module," however; the symbolic functions would emerge as an intrinsically "dedicated" symbol system as a consequence of the bottom-up grounding of categories' names in their sensory representations. Symbol manipulation would be governed not just by the arbitrary shapes of the symbol tokens, but by the nonarbitrary shapes of the icons and category invariants in which they are grounded....
EXCERPT: The symbol grounding problem aroused from the notice that symbol systems manipulated structures that could be associated with things in the world by an observer operating the system, but not by the system itself. The quest for symbol grounding processes is concerned with understanding processes which could enable the connection of these purely symbolic representations with what they represent in fact, which could be directly, or by means of other grounded representations....
http://www.scholarpedia.org/article/Symb...ng_problem
EXCERPT: The Symbol Grounding Problem is related to the problem of how words (symbols) get their meanings, and hence to the problem of what meaning itself really is. The problem of meaning is in turn related to the problem of consciousness, or how it is that mental states are meaningful. According to a widely held theory of cognition, "computationalism," cognition (i.e., thinking) is just a form of computation. But computation in turn is just formal symbol manipulation: symbols are manipulated according to rules that are based on the symbols' shapes, not their meanings. How are those symbols (e.g., the words in our heads) connected to the things they refer to? It cannot be through the mediation of an external interpreter's head, because that would lead to an infinite regress, just as my looking up the meanings of words in a (unilingual) dictionary of a language that I do not understand would lead to an infinite regress. The symbols in an autonomous hybrid symbolic+sensorimotor system -- a Turing-scale robot consisting of both a symbol system and a sensorimotor system that reliably connects its internal symbols to the external objects they refer to, so it can interact with them Turing-indistinguishably from the way a person does -- would be grounded. But whether its symbols would have meaning rather than just grounding is something that even the robotic Turing Test -- hence cognitive science itself -- cannot determine, or explain....
http://users.ecs.soton.ac.uk/harnad/Pape...oblem.html
EXCERPT: There has been much discussion recently about the scope and limits of purely symbolic models of the mind and about the proper role of connectionism in cognitive modeling. This paper describes the "symbol grounding problem": How can the semantic interpretation of a formal symbol system be made intrinsic to the system, rather than just parasitic on the meanings in our heads? How can the meanings of the meaningless symbol tokens, manipulated solely on the basis of their (arbitrary) shapes, be grounded in anything but other meaningless symbols? The problem is analogous to trying to learn Chinese from a Chinese/Chinese dictionary alone. A candidate solution is sketched: Symbolic representations must be grounded bottom-up in nonsymbolic representations of two kinds: (1) "iconic representations" , which are analogs of the proximal sensory projections of distal objects and events, and (2) "categorical representations" , which are learned and innate feature-detectors that pick out the invariant features of object and event categories from their sensory projections. Elementary symbols are the names of these object and event categories, assigned on the basis of their (nonsymbolic) categorical representations. Higher-order (3) "symbolic representations" , grounded in these elementary symbols, consist of symbol strings describing category membership relations (e.g., "An X is a Y that is Z"). Connectionism is one natural candidate for the mechanism that learns the invariant features underlying categorical representations, thereby connecting names to the proximal projections of the distal objects they stand for. In this way connectionism can be seen as a complementary component in a hybrid nonsymbolic/symbolic model of the mind, rather than a rival to purely symbolic modeling. Such a hybrid model would not have an autonomous symbolic "module," however; the symbolic functions would emerge as an intrinsically "dedicated" symbol system as a consequence of the bottom-up grounding of categories' names in their sensory representations. Symbol manipulation would be governed not just by the arbitrary shapes of the symbol tokens, but by the nonarbitrary shapes of the icons and category invariants in which they are grounded....
