> As I think about it, one problem is, depending on how its
> parametrized, its not going to build much of a world model.
> Say for example it uses trigrams. The average hs grad knows
> something like 50,000 words. So there are something like 10^17
> trigrams. It will never see enough data to build a model capturing
> much semantics, unless it builds an incredibly compact model,
> in which case-- what is the underlying structure and how
> (computationally) are you going to learn it?
Absolutely correct. That's why I said "My belief is that if you had the proper
structure-building learning algorithms that your operator grammar system would
simply (re-)discover the
basic parts of speech and would then successfully proceed from there." and why
I slammed it for ""reinventing the wheel" in terms of it's unnecessary
generalization of dependency"
> In unsupervised learning, you can learn a lot,
> say you can cluster the world into two clusters. But until you get
> supervision, you can't learn the final few bits to distinguish good
> from bad, or whatever.
I'm afraid that I disagree completely with the latter sentence.
> Operator grammar might be very useful for
> getting a structure that could then be rapidly trained to produce
> meaning, but I don't think you can finish the job until you interact
> with sensation.
It seems as if you're now talking sensory fusion (which is a whole 'nother can
o' worms).
Mark
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