Mike, you seem to have misinterpreted my statement. Perception is certainly
not 'passive', as it can be described as active inference using a (mostly
actively) learned world model. Inference is done on many levels, and could
integrate information from various abstraction levels, so I don't see it as
an isolated layer.

On Sun, Mar 30, 2008 at 6:27 PM, Mike Tintner <[EMAIL PROTECTED]>
wrote:

>  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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