> What I said in my previous reply was that something very like neural nets
> (with all the beneficial features for which people got interested in NNs in
> the first place) *can* do syntax, and all forms of abstract representation.
>
> I do not think it is fair to say that they can't, only that the
> particularly restrictive interpretation of NN that prevails in the
> literature can't.
> (with all the beneficial features for which people got interested in NNs in
> the first place) *can* do syntax, and all forms of abstract representation.
>
> I do not think it is fair to say that they can't, only that the
> particularly restrictive interpretation of NN that prevails in the
> literature can't.
Hi Richard
I have to agree that NN can represent all forms of knowledge, since our brains are NNs. But figuring out how to do that in artificial systems must be pretty difficult. I should also mention Ron Sun's work, he has long tried to reconcile neural and symbolic processing. I studied NNs/ANNs for some time, but I recently switched camp to the more symbolic side.
One question is whether there is some definite advantage to using NNs instead of say, predicate logic. Can you give an example of a thought, or a line of inference, etc, that the NN-type representation is particularly suited? And that has a advantage over the predicate logic representation? John McCarthy proposed that predicate logic can represent 'almost' everything.
If NN-type representation is not necessarily required, then we should naturally use symbolic/logic representations since they are so much more convenient to program and to run on von Neumann hardware.
YKY
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