OK, but in general terms, that's what DL is doing anyway. You want to the
same thing without its flaws, but how?
You know what I think, these flaws are produced by coarse statistical
operations of ANN, & the solution is to start with fine-grain one-to-one
cross-comparison.

On Sun, May 10, 2015 at 12:33 PM, Ben Goertzel <[email protected]> wrote:

>
>
> On Sat, May 9, 2015 at 9:45 PM, Boris Kazachenko <[email protected]>
> wrote:
>
>> Ben,
>>
>> a) Episodic memory requires transition from hierarchical to sequential
>> processing, which is problematic for ANN. Conventional solution is to model
>> dendrites: they do all the heavy sequential processing, while neuron itself
>> is more like networking node. Jeff Hawkins does that.
>> In my model, this dilemma does not exist: every level of a hierarchy is
>> already a sequence: www.cognitivealgorithm.info
>>
>> b) Your solution to pathologies is basically supervision, by some image
>> grammar. It's a hack, general intelligence must be able to learn without
>> supervision. I think real solution is in proper node design.
>>
>
>
> No, my suggested solution is NOT supervision by an image grammar.  Rather,
> my preferred approach involves creation of unsupervised learning algorithms
> that implicitly form image grammar type structures within their networks.
> Sorry if my write-up was not clear on that point.  I agree that most
> learning in an AGI must occur without supervision, though supervision can
> also play a supporting role
>
> -- Ben
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