Look at Juergen Schmidhuber's work, as a good example of RNNs pushed to the
limit of current understanding...

Recurrent NNs, like other purely subsymbolic methods, have not proven
convenient (or maximally effective) for carrying out tasks involving logic,
language, reasoning, etc. ...

For tasks involving sensory perception, motor control or data
classification or nonlinear regression, NNs are powerful and demonstrably
applicable -- but, tend to take more computing resources and (importantly)
require more case-by-case tuning and fiddling, than other methods....

For instance,

-- to do supervised data classification, I'd choose SVMs over NNs, on
grounds of speed and auto-tunability

-- to do vision processing, I'd choose a non-NN hierarchical architecture
like DeSTIN over an NN, on similar grounds

... ben


On Tue, Jul 10, 2012 at 5:54 PM, Jarrad Hope <[email protected]> wrote:

> Hi,
>
> I was wondering if anyone could point me to some resources on developing
> AGI with neural networks?
> (variants of ann and learning algorithms)
>
> So far I'm aware that it should be a form of RNN - I have been looking at
> BlueBrain alot - but I feel that modeling a mammalians brain in that much
> detail is better suited for WBE rather than a strictly AGI goal.
>
> Also why aren't ANN's used almost exclusively in developing AGI - is it a
> memory/computational power hurdle? or is there some other limiting factor I
> am not aware of.
>
> Regards,
> Jarrad
>
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-- 
Ben Goertzel, PhD
http://goertzel.org

"My humanity is a constant self-overcoming" -- Friedrich Nietzsche



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