Let the algorithm that represents the brain of a typical new-born baby
be denoted as B1.

Now surely we can agree that the brain of a new-born baby does not
have sophisticated Bayesian machinary built into it?  Yes, there must
be *some* intrinsic built-in reasoning structure, but everything we
know suggests that the intrinsic reasoning mechanisms of the human
brain must be quite weak and simple.

Let the algorithm which represents the brain of the baby B1 which grew
up into a 20-year old with a PhD in Bayesian math be denoted as B2.

Now somehow, the algorithm B1 was able to 'optimze' its original
reasoning mechanisms by a smooth transformation into B2. (assume there
was 'brain surgery', no 'hand coding').

The environment! you may shout.  The baby got all its information from
human culture (Reading math books, learning from math professors), you
might try to argue, that's how B1 (baby) was able to transform into B2
(PhD in Bayes)

But this cant be correct.  Since, humans existed long before Bayesian
math was developed.  Every single Bayesian technique had to be
developed by a human in the past, without being told.  So in theory,
B1 could have grown into B2 entirely on its own, without being told
anything by anyone  about Bayesian math.

The conclusion:

*There exists a very simple algorithm which is only a very weak
approximation to PhD Bayesian reasoning, which is perfectly capable of
recursive self-improvement to the PhD level!  No hand coding of
advanced Bayesian math is needed.

Or to simply rephrase:

Humans could reason before they discovered Bayes.




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