Path: utzoo!utgpu!news-server.csri.toronto.edu!cs.utexas.edu!uunet!tdatirv!sarima From: sarima@tdatirv.UUCP (Stanley Friesen) Newsgroups: comp.ai.philosophy Subject: Re: Reasoning Paradigms Message-ID: <27@tdatirv.UUCP> Date: 13 Oct 90 00:05:09 GMT References: <3586@media-lab.MEDIA.MIT.EDU> <69347@lll-winken.LLNL.GOV> <3593@media-lab.MEDIA.MIT.EDU> <69377@lll-winken.LLNL.GOV> <11@tdatirv.UUCP> Reply-To: sarima@tdatirv.UUCP (Stanley Friesen) Organization: Teradata Corp., Irvine Lines: 37 In article gessel@cs.swarthmore.edu (Daniel Mark Gessel) writes: >You seem to be starting from the point of view that NNs embody the way the >human brain works. Unless there have been some huge jumps in the understanding >of the human brain and it's functioning, no one knows if NNs are even close. I was operating on the assumption that NN's were originally concieved as a sort of crude analog to the type of processing done by living things. I also assumed that the limitations of current NN's are due to limitations of technology and knowledge, not fundamental limitations of circuits. >How the exact chemical composition of the brain affects the functioning of >the brain is unknown. Todays artificial NN cannot be assumed to capture this >accurately. To assume that future NN's will be able to is to assume that we >will be able to recreate the human brain via well defined functions (in the >Mathematical sense) which is not necessarily possible. It is true there are many unknowns in neurobiology, but I do not see that there is any reason to believe that neurons do anything that is not at least amenable to aproximation using electronics. Indeed, I suspect that they do not even do anything that cannot be exactly duplicated. But even more important, I suspect that the exact details of biological neurobiology are irrelevant to making a general purpose responsive network [such as NN's]. That is, the redundancy and intrinsic variability in neuron response patterns make exact duplication unnecessary. In basic operation a neuron appears to be a complex combinatorial circuit. It performs some sorted of a generalized weighted 'sum' of an enormous number of inputs (often several thousand), and distributes the resulting signal to a large number destinations (again often in the thousands). The summation is probably quite different than the simple arithmetic sum used by current NN's, and the weighting and threshold functions are probably more complex than we currently imagine, but this is just a matter of technology and knowledge, not metaphysics. -- --------------- uunet!tdatirv!sarima (Stanley Friesen)