On Tue, Jan 27, 2015 at 1:46 PM, Steve Richfield via AGI
<[email protected]> wrote:

> We had a discussion here a couple of years back, to the effect that if you 
> differentiate signals leading into a NN, and integrate the results from a NN, 
> that you then get the same as if you didn't do the integration and 
> differentiation.

Almost, but neurons have a nonlinear response.

> However, "learning" in such a system would then become temporal learning. Of 
> course, differentiation, which is widely utilized in living systems, is only 
> possible in CONTINUOUS systems.

Neurons are not continuous. The relevant signal is approximated by the
spiking rate. But I agree that differentiation is an important
component of perception.

>  This would also support bidirectional computing, that I believe is also a 
> necessary requirement.

Reversible computing is only required for a quantum computer, which
the brain is not.

> 4.  Move to a programmable analog platform. There ARE ways past the usual 
> objections to this approach, but no one seems to be interested in investing 
> the ~$100M to launch in this direction. Done right, this could also support 
> bidirectional computing.

How much did IBM invest in the TrueNorth neuromorphic processor?
Analog computing (to the extent possible at the molecular level) might
help solve the power efficiency problem. The TrueNorth processor
performs 1000 times as many synapse operations per watt as a
conventional computer, because it is encoded as a single bit operation
rather than 1000 bit operations normally needed for a 32 bit
multiply-accumulate. But this is still 100 times more power than
required by the brain.

> Your observation that continuous operation is probably necessary is good, but 
> still not entirely sufficient to get AGI working. Besides continuous and 
> bidirectional operation, I wonder what ELSE is needed to close this gap?!!!

Just a vast amount of computing power, training data, and programming
effort. If it was a simple answer then we would have solved it 50
years ago.

-- 
-- Matt Mahoney, [email protected]


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