Your closing line is appreciated.
Yet: I still cannot get it: how can you include into an algorithm
those features that had not yet been discovered? Look at it
historically: if you composed such compendium 3000 yeas ago would you
have included 'blank potential' unfilled algorithm for those aspects
that had been discovered as part of the human intelligence since then?
And forwardly: how much would you keep blank for newly addable
features in human intelligence for the next millennia?
Is B2 a closed and complete image?
B1 (IMO) includes potential so far undiscovered beyond the "knowable".
How is that part of the algorithm?
Forgive me my simplistic view at these things: I am on some common
sense basis and allow more planes for 'intelligence' than what you may
include into ongoing mathematical logic.
Or: does your algorithm encompass Bruno's "express all" long integer
(numbers) series?
Stubbornly in my primitivness
John M

On Mon, Sep 22, 2008 at 4:48 AM, <[EMAIL PROTECTED]> wrote:
> 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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