Durk, I am indebted to you for bringing this very interesting Hinton lecture to the attention of this list.
It is highly relevant to AGI, since, if it is to be believed, it provides a
general architecture for learning invariant hierarchical representations
(which are currently in vogue--for good reason), from presumably any type of
data. It can perform both unsupervised and supervised learning. Hinton
claims this architecture scales well. He does not mention how his system
would learn temporal patterns, but presumably it could be expanded to do so,
such as by the use of temporal buffers to store sequences of inputs over
time. If it could learn temporal patterns it would seem to be able to
generate behaviors as well as recognizing and generating patterns.
Of course it would require considerably more to become a full AGI, such as
motivational, reinforcement-learning-like, mental behavior, goal selecting,
goal pursuing, and novel pattern formation features. But it would seem to
provide a system for automatically learning and generating a significant
percent of the patterns and behaviors an AGI would need.
I think the AGI community should be open to adopting such a potentially
powerful idea from machine learning, if it is shown to be as powerful as
Hinton says, because, if so, it would add credence to the possibility of AGI
by making the task of building an AGI seem considerably less complex.
Ed Porter
-----Original Message-----
From: Kingma, D.P. [mailto:[EMAIL PROTECTED]
Sent: Sunday, March 02, 2008 12:08 PM
To: agi@v2.listbox.com
Subject: [agi] interesting Google Tech Talk about Neural
Nets
Gentlemen,
For guys interested in vision, neural nets and the like,
there's a very interesting talk by Geoffrey Hinton about unsupervised
learning of low-dimensional codes:
It's been on Youtube since December, but somehow it escaped
my attention for some months.
http://www.youtube.com/watch?v=AyzOUbkUf3M
BTW, the back of Peter Norvig's head makes a guest
appearance throughout most of the video ;)
As an academic I'm quite excited about this technique
because it has the potential of solving non-trivial parts of problems in
perception in a clean, practical, understandable way.
Greets from Utrecht, Netherlands,
Durk
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