A working hypothesis of computationalism is that Mind arises, not from the intrinsic nature of the causal properties of particular forms of matter, but from the organization of matter. If this hypothesis is correct, then a wide range of physical systems (e.g. optical, chemical, various hybrids, etc.) should support Mind, especially computers, since they have the capability to create/manipulate organizations of bits of arbitrarily complexity and dynamics. In any particular computer, these bit patterns are quite physical, but their particular physicality (...) is considered irrelevant (since they could be replaced by other physical substrata). (shrink)

Goswami proposes to replace the current scientific paradigm of physical realism with that of a quantum-based monistic idealism and, in the process, accomplish the following goals: establish a basis for explaining consciousness, reintegrate spirituality, mysticism, morality, a sense that the universe is meaningful, etc., with scientific discoveries and the scientific enterprise, and support the assumption that humans possess free will - i.e., that they are not controlled by the apparently inexorable causality of the physical laws that govern the functioning of (...) their brains. Here, we critically examine this approach, from an artificial intelligence and neural network perspective, and point out what appear to be some inherent weaknesses in Goswami's arguments. (shrink)

The neural blackboard architecture is a localist structured connectionist model that employs a novel connection matrix to implement dynamic bindings without requiring propagation of temporal synchrony. Here I note the apparent need for many distinct matrices and the effect this might have for scale-up to semantic processing. I also comment on the authors' initial foray into the symbol grounding problem.

Shastri & Ajjanagadde argue convincingly that both structured connectionist networks and parallel dynamic inferencing are necessary for reflexive reasoning - a kind of inferencing and reasoning that occurs rapidly, spontaneously, and without conscious effort, and which seems necessary for everyday tasks such as natural language understanding. As S&A describe, reflexive reasoning requires a solution to thedynamic binding problem, that is, how to encode systematic and abstract knowledge and instantiate it in specific situations to draw appropriate inferences. Although symbolic artificial intelligence (...) systems trivially solve the dynamic binding problem using computers' registers and pointers, it has remained a difficult problem for connectionist systems. S&A's temporal synchrony solution to the dynamic binding problem using synchronous firing of argument units and the entities that are bound to them illustrates one way in which connectionist networks can do thisusing a constrained but important class of long-term knowledge rules. Their structured connectionist solution allows dynamic inferencing to proceed in parallel and therefore has a number of advantages for reflexive reasoning over most other connectionist and symbolic systems. (shrink)