There is an enormous amount of redundancy in the abstract thalamocortical
architecture evincing small Kolmogorov Complexity in description.  While I
understand "the devil is in the details" of this evolved structure (not the
least of which is the fact that it elides that the cerebellum's neuron
count is a super-majority of the brain's), there seems to be a vast
theoretic vacuum of the requisite simplicity.  It's the dog that didn't
bark.

That's why I take Hecht-Nielsen's Confabulation Theory seriously:  Not
because I believe, as he did, that he "solved the problem of cognition",
but rather that he has a first order approximation of the neocortex (indeed
thalamcortical) structure -- at least one barking dog -- an _approach_.
It's rather like a framework for compression like mixture of models, rather
than the models themselves.

On Sun, Oct 13, 2019 at 12:07 PM Matt Mahoney <[email protected]>
wrote:

>
>
> On Sun, Oct 13, 2019, 10:09 AM <[email protected]> wrote:
>
>> Isn't that massively inefficient? It'd take 100 times more
>> storage/computation to do the same thing as a weighted net no?
>>
>
> The neural models I use in the top ranked text compressors use a lot less
> than 12-24 petaflops and a petabyte of RAM. But the language modeling is
> rather rudamentary, nowhere near AGI. But I would be happy for you to prove
> my estimate wrong.
>
> And one more thing. That's one human brain. To automate all labor, you
> need several billion times that. Current technology uses about 1 megawatt
> per petaflop. Maybe neuromorphic computing could get it down to 100 kW per
> brain. Maybe economy of specialization could reduce it to 1 kW, which is
> 50% of global energy production. But shrinking transistors alone won't do
> it. If we can't do the optimization, it's going to take nanotechnology,
> moving atoms instead of electrons. The brain uses 20 watts. It can be done.
>
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