> #Mark A Mandel <mam at theworld.com> writes: > #> And let's not forget that despite their name, the software constructs > #> that their creators optimistically named "neural nets" probably have > #> very little in common with wetware. This is not true. The reason they are called "neural networks" is that the are modelled after the biological structures for which they are named. In their implementation, of course, they are quite different, but in what they do and how they do it they are very similar. > On 24 Aug 2002, David Dyer-Bennet wrote: > #I'm not so sure. They certainly exhibit startlingly wetware-like > #properties. I'm thinking especially of some of the artificial insects > #that have been built. > # > #And after all, we *do* understand the neuronal biochemistry pretty > #decently. What we understand of it, we understand pretty well. There's still a lot of stuff (mostly relating to how the connections and feedback systems work) that we still don't understand. But our understanding is getting better all the time. Mark A Mandel wrote: > That's simulation of behavior at an insect level. ELIZA can simulate > conversation convincingly at a certain level, too. How similar > (homeomorphic?) are the innards? How far does it scale toward human > cognition? Considering that the most complex software neural network ever developed has at most the complexity of an insect, comparing it to the complexity underlying human cognition doesn't make much sense. On the other hand, though, I am becoming more and more convinced that the difference between the complexity of an insect and that of a human is a matter of amount, not kind. It's the same set of processes, with some modifications and much, much increased complexity. ELIZA was a keyword-based recogizer and can't be thought of in the same context as a neural network. For a brief description of artificial neural networks, I suggest http://www.emsl.pnl.gov:2080/proj/neuron/neural/what.html Particularly: "Artificial neural networks are collections of mathematical models that emulate some of the observed properties of biological nervous systems and draw on the analogies of adaptive biological learning." (Ibid) (I'm somewhat pleased at the vehemence of some of the reactions I've seen to this discussion. It can be difficult to consider that we are not what we think we are; the reactions suggest that some preconceptions may be being challenged.) -- Frank Mayhar frank at exit.com http://www.exit.com/ Exit Consulting http://www.gpsclock.com/