Hi Ben,

It seems strange to me that NN's aren't effective for dealing with reason,
logic and language, etc - after all our brains seem to be capable of it?
Perhaps current research is not taking the right approaches / our
understanding is too limited?

Good to see it has some applications though.



On Tue, Jul 10, 2012 at 12:39 PM, Ben Goertzel <[email protected]> wrote:

>
> 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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