[Rasch] Fan

commons commons at tiac.net
Wed Oct 31 11:14:04 EST 2007

The problem with neural networks is that they are part of AI.  They take on
way to complicated problems and therefore have lots of problems.  They are
unstable, they have to be fooled with to get them to converge.  In animals,
evolution has solved these problems.  Also, stacked neural networks are much
closer to nature and can solve much more complicated problems.

My best,

Michael Lamport Commons, Ph.D.
Assistant Clinical Professor
Department of Psychiatry
Harvard Medical School
Beth Israel Deaconess Medical Center
234 Huron Avenue
Cambridge, MA 02138-1328
commons at tiac.net
617-497-5270 Telephone
617-320-0896  Cellular
617-491-5270  Facsimile

-----Original Message-----
From: rasch-bounces at acer.edu.au [mailto:rasch-bounces at acer.edu.au] On Behalf
Of Paul Barrett
Sent: Tuesday, October 30, 2007 7:40 PM
To: rasch at acer.edu.au
Subject: RE: [Rasch] Fan

----Original Message-----
> From: rasch-bounces at acer.edu.au 
> [mailto:rasch-bounces at acer.edu.au] On Behalf Of Moritz Heene
> Sent: Wednesday, October 31, 2007 8:47 AM
> To: rasch at acer.edu.au
> Subject: Re:[Rasch] Fan
> Hello to all and hello especially to Paul,
> I am not expert in neural networks, I am just a beginner, but 
> as far as I can see, neural networks, as efficient as they 
> are, have still their problems. 

Hello Moritz

That was some response!

I'm only replying to this bit quickly to note you missed my point!
Neural nets, like all Machine Learning algorithms, are prone to many
"threats to their validity" such as overlearning, lack of
cross-validation, and sometimes it's really difficult to track back as
to how they achieved their outputs (!) .. 

But - I wanted to stress the impact this one innovation had in many
areas - unlike the impact of modern test theory which was developed
around the same time - and note why it was not taken up so quickly by
those who utilize test scores for explanatory or predictive purposes.

As to Andrew's point about back-propogation - again, it was the concept
of a network and how such a concept might be causal for human neural
development and activity which took several areas by storm. 

Whether or not neural nets survive beyond the next developments in AI is
of course a moot point.

And thanks for the detailed response on the Lexile system Andrew - very

Regards .. Paul

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