>> Also, I don't see how you can call a model "semantic" when it makes
>> no reference to the world.

Mark> Ah, but this is where it gets tricky.  While the model makes no
Mark> reference to the world, it is certainly influenced by the fact
Mark> that 100% of it's data comes from the world -- which then forces
Mark> the model to build itself based upon the world
Mark> (i.e. effectively, it is building a world model) -- and I would
Mark> certainly call that semantics.

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?

>> natural or highly unlikely, but unless I misunderstand something,
>> there is no possibility it could tell me whether a sentence
>> describes a scene.

Mark> Do you mean that it couldn't perform sensory fusion or that it
Mark> can't recognize "meaning"?  I would agree with the former but
Mark> (as an opinion -- because I can't definitively prove it)
Mark> disagree with the latter.

If adequately trained (a big if) it could perhaps distinguish a meaningful
sentence from an unlikely one. The situation might be analagous to 
unsupervised learning. 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. 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.

Mark>         Mark



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