On 10/23/06, Matt Mahoney <[EMAIL PROTECTED]> wrote:
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> One aspect of NARS and many other structured or semi-structured knowledge representations that concerns me is the direct representation of concepts such as "is-a", equivalence, logic ("if-then", "and", "or", "not"), quantifiers ("all", "some"), time ("before" and "after"), etc. These things seem fundamental to knowledge but are very hard to represent in a neural network, so it seems expedient to add them directly. My concern is that the direct encoding of such knowledge greatly complicates attempts to use natural language, which is still an unsolved problem. Language is the only aspect of intelligence that separates humans from other animals. Without language, you do not have AGI (IMHO).
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> My concern is that structured knowledge is inconsistent with the development of language in children. As I mentioned earlier, natural language has a structure that allows direct training in neural networks using fast, online algorithms such as perceptron learning, rather than slow algorithms with hidden units such as back propagation. Each feature is a linear combination of previously learned features followed by a nonlinear clamping or threshold operation. Working in this fashion, we can represent arbitrarily complex concepts. In a connectionist model, we have, for example:
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> My concern is that structured knowledge is inconsistent with the development of language in children. As I mentioned earlier, natural language has a structure that allows direct training in neural networks using fast, online algorithms such as perceptron learning, rather than slow algorithms with hidden units such as back propagation. Each feature is a linear combination of previously learned features followed by a nonlinear clamping or threshold operation. Working in this fashion, we can represent arbitrarily complex concepts. In a connectionist model, we have, for example:
It is not obligatory that AGI designers should make their AGIs as ignorant as babies at the beginning. Why many people have this predilection is because they think that the AGI should be able to learn *anything*.
Therefore: the point is whether our AGI can learn/express/reason with anything and everything; but it's not whether we should equip the AI with an advanced KR structure initially. I choose the latter as a short cut. I think it is a good thing if the KR has a good design.
A belated point is that classical logic usually cannot "talk about" syncategorimatic constructs. For example in predicate logic you cannot say "AND is a very useful thingy". I think my term-logic enhanced version can do this. =)
YKY
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