[ExI] The upper limit on brain complexity
Anders Sandberg
anders at aleph.se
Tue May 10 13:52:37 UTC 2016
Yup, agree with John here. Of course, a simple genetic program can still
"cheat" by getting existing physics and biochemistry to do complex
things like assemble structures, but the truth remains that the recipe
for a mind can be surprisingly small.
On a mildly related note, Scott Aaronson has a new cool result:
http://www.scottaaronson.com/blog/?p=2725
There exist a one-tape, two-symbol Turing machine with 7,918 states,
whose behavior (when run on a blank tape) can never be proven from the
usual axioms of set theory. This is a constructive upper bound on how
small TMs can be and yet produce profoundly nontrivial behavior - there
are surely simpler ones, but this is a machine that fits into the
appendix of a paper.
On 2016-05-10 04:27, John Clark wrote:
> We don't yet know what the brain's master learning algorithm is but we
> can put upper limits on how complex that algorithm can be, and we know
> for a fact it can't be all that complex. In the entire human genome
> there are only 3 billion base pairs. There are 4 bases so each base
> can represent 2bits, there are 8 bits per byte so that comes out to
> 750 meg. Just 750 meg! And all that 750 meg certainly can be used
> just for the master learning software algorithm, you've got to leave
> room for instructions on how to build a human body as well as the
> brain hardware. So the instructions MUST contain wiring instructions
> such as "wire a neuron up this way and then repeat that procedure
> exactly the same way 917 billion times". And the 750 meg isn't even
> efficiently coded, there is a ridiculous amount of redundancy in the
> human genome. So there is no way, absolutely no way, the algorithm can
> be very complex, and if Evolution could find it then it's just a
> matter of time before we do too.
>
>
>
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--
Anders Sandberg
Future of Humanity Institute
Oxford Martin School
Oxford University
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