[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:
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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