Mike,

None of the Hawkin's papers I have read have given any results as impressive
as the Hinton papers I cited.  If you know of some that have, please send me
references to the most impressive among them.

Hinton says he believe his system could scale efficiently to much larger
nets.  If that is true, a system having multiples of his modules would
appear possibly able to learn how to handle a good chunk of sensory
perception. 

Like, Ben I am not wed to a totally connectionist approach, but rather one
that has attributes of both connectionist and symbolic approaches.  I
personally like to think in terms of systems where I have some idea what
things represent, so I can think in terms of what I want them to do.

But still I am impressed with what Hinton has shown, particularly if it can
be made to scale well to much larger systems.  

Ed Porter

-----Original Message-----
From: Mike Tintner [mailto:[EMAIL PROTECTED] 
Sent: Sunday, June 29, 2008 2:48 PM
To: [email protected]
Cc: [EMAIL PROTECTED]
Subject: Re: [agi] WHAT SORT OF HARDWARE $33K AND $850K BUYS TODAY FOR USE
IN AGI

Ed:Another reason for optimism is Hintons new work described in papers such 
as
"Modeling image patches with a directed hierarchy of Markov random fields"
by Simon Osindero and Geoffrey Hinton and the Google Tech Talk at
http://www.youtube.com/watch?v=AyzOUbkUf3M.  Hinton has shown how to
automatically learn hierarchical neural nets that have 2000 hidden nodes in
one layer, 500 in the next, and 1000 in the top layer

Comment from a pal on Hinton who was similarly recommended on slashdot:(I'm 
ignorant here):

"I also took a closer look at the Hinton stuff that the slashdot poster made

reference to. To call this DBN stuff highly advanced over Hawkins is 
ridiculous. I looked at it already a couple of months ago. It took Hinton 
***17-years*** - by his own admission - to figure out how to build a 
connectionist net that could reliably identify variations of handwritten 
numbers 1-9. And it's gonna take him about a MILLION more years to do 
general AI with this approach. Gakk.
To me, the biggest problem with connectionist networks is all they ever 
solve are toy problems - and it's 20 years after connectionism become 
popular again."




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