If such neural systems can actually spit out sensible
analyses of natural language, it would obviously be a
huge discovery and could probably be sold to a good
number of people as a commercial product. So why
aren't more people investing in this, if you've
already got working software that just needs a
suitable supercomputer?

 - Tom

--- Matt Mahoney <[EMAIL PROTECTED]> wrote:

> --- Tom McCabe <[EMAIL PROTECTED]> wrote:
> > --- Matt Mahoney <[EMAIL PROTECTED]> wrote:
> > > Personally, I would experiment with
> > > neural language models that I can't currently
> > > implement because I lack the
> > > computing power.
> > 
> > Could you please describe these models?
> 
> Essentially models in which neurons (with time
> delays) respond to increasingly
> abstract language concepts: letters, syllables,
> words, grammatical roles,
> phrases, and sentence structures.  This is not
> really new.  Models like these
> have been proposed in the 1980's but were never
> fully implemented due to lack
> of computing power.  These constraints resulted in
> connectionist systems in
> which each concept mapped to a single neuron.  Such
> models can't learn well. 
> There is no mechanism for adding to the vocabulary,
> for instance.  I believe
> you need at least hundreds of neurons per concept,
> where each neuron may
> correlate weakly with hundreds of different
> concepts.  Exactly how many, I
> don't know.  That is why I need to experiment.
> 
> One problem that bothers me is the disconnect
> between the information
> theoretic estimates of the size of a language model,
> about 10^9 bits, and
> models based on neuroanatomy, perhaps 10^14 bits. 
> Experiments might tell us
> what's wrong with our neural models.  But how to do
> such experiments?  A fully
> connected network of 10^9 connections trained on
> 10^9 bits of data would
> require about 10^18 operations, about a year on a
> PC.  There are optimizations
> I could do, such as activating only a small fraction
> of the neurons at one
> time, but if the model fails, is it because of these
> optimizations or because
> you really do need 10^14 connections, or the
> training data is bad, or
> something else?
> 
> 
> -- Matt Mahoney, [EMAIL PROTECTED]
> 
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