Sorry to barge into the conversation in this way, but YKY mentioned something I needed clarification with.
You said:
With logic I can write down a rule for recognizing this pretty easily, mainly due to the use of symbolic variables. So you see the compressive power of logic.
But wouldn't this mean you'd end up "hand-coding" a lot of logic rules? I don't really follow how this could achieve generic AI. Am I missing something?
M. Riad
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>
> Nice to hear your views, Durk. =)
>
> I think the essense of Hawkins' theory (his HTM [hierarchical temporal memory] model) is the compression of sensory experience via pattern recognition. Sensory experience goes in; condensed episodic memory comes out. He does this with neural networks.
>
> I worked with NNs for a while along exactly the same line of thought. After a while I just decided that NN is too difficult to work with, so I switched to (predicate) logic as the substrate for pattern recognition.
>
> Take an example:
>
> John hits Mary.
> Mary kicks John.
> Mary kicks John again.
> John hits Mary again.
> etc, etc.
>
> The point is to recognize that John and Mary are "fighting", thus achieving compression. The fighting pattern can be irregular consisting of X hit Y, Y kick X, etc. With logic I can write down a rule for recognizing this pretty easily, mainly due to the use of symbolic variables. So you see the compressive power of logic. NN is just too clumsy to work with. Although we know that the brain somehow must perform this information compression with neurons, we just don't understand the mechanisms yet.
>
> Let's say the goal is to compress visual inputs to the "John hits Mary" level. I think it can be done using my vision scheme plus a logical knowledge representation. But with NN, this still seems very very remote....
>
> YKY
>
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"Er, five," said the mattress.
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- Life, the Universe, and Everything
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