Durk,
Absolutely right about the need for what is essentially an imaginative level of
mind. But wrong in thinking:
"Vision may be classified under "Narrow" AI"
You seem to be treating this extra "audiovisual perception layer" as a purely
passive layer. The latest psychology & philosophy recognize that this is in
fact a level of v. active thought and intelligence. And our culture is only
starting to understand imaginative thought generally.
Just to begin reorienting your thinking here, I suggest you consider how much
time people spend on audiovisual information (esp. tv) vs purely symbolic
information (books). And allow for how much and how rapidly even academic
thinking is going audiovisual.
Know of anyone trying to give computers that extra layer? I saw some vague
reference about this recently.of which I have only a confused memory.
Durk:Although I symphathize with some of Hawkin's general ideas about
unsupervised learning, his current HTM framework is unimpressive in comparison
with state-of-the-art techniques such as Hinton's RBM's, LeCun's convolutional
nets and the promising low-entropy coding variants.
But it should be quite clear that such methods could eventually be very handy
for AGI. For example, many of you would agree that a reliable, computationally
affordable solution to Vision is a crucial factor for AGI: much of the world's
information, even on the internet, is encoded in audiovisual information.
Extracting (sub)symbolic semantics from these sources would open a world of
learning data to symbolic systems.
An audiovisual perception layer generates semantic interpretation on the
(sub)symbolic level. How could a symbolic engine ever reason about the real
world without access to such information?
Vision may be classified under "Narrow" AI, but I reckon that an AGI can
never understand our physical world without a reliable perceptual system.
Therefore, perception is essential for any AGI reasoning about physical
entities!
Greets, Durk
On Sun, Mar 30, 2008 at 4:34 PM, Derek Zahn <[EMAIL PROTECTED]> wrote:
It seems like a reasonable and not uncommon idea that an AI could be built
as a mostly-hierarchical autoassiciative memory. As you point out, it's not so
different from Hawkins's ideas. Neighboring "pixels" will correlate in space
and time; "features" such as edges should become principle components given
enough data, and so on. There is a bunch of such work on self-organizing the
early visual system like this.
That overall concept doesn't get you very far though; the trick is to make
it work past the first few rather obvious feature extraction stages of sensory
data, and to account for things like episodic memory, language use,
goal-directed behavior, and all other cognitive activity that is not just
statistical categorization.
I sympathize with your approach and wish you luck. If you think you have
something that produce more than Hawkins has with his HTM, please explain it
with enough precision that we can understand the details.
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