With respect, we should not confuse randomness with effective complexity.

Rob
________________________________
From: John Rose <[email protected]>
Sent: Friday, 12 October 2018 11:25 AM
To: 'AGI'
Subject: RE: [agi] Compressed Algorithms that can work on compressed data.

> -----Original Message-----
> From: Matt Mahoney via AGI <[email protected]>
>
> Encrypted data appears random if you don't know the key. But it is not
> random because it has a short description (compressed plaintext + key).
> Kolmogorov proved that there is no general algorithm to tell the difference. 
> If
> there was, then I could describe "the first string (in lexicographical order 
> by
> increasing length) that can't be described using a million bits" even though I
> just did.
>

By "shades of randomness" or "more random" I mean something that would be more 
difficult to find a shorter description comparably. If there are two strings 
one generated from low quality plastic dice and one from beta decay the beta 
decay source would be "more random". Just terminology I suppose.

But I would say again "computational distance from agent perspective". Makes 
sense to me perhaps not to others. K-randomness or "perfect randomness" then 
would often have infinite computational distance. Though for K-randomness I 
think it is in relation to a specific UTM...


> This is why there is no simple, general algorithm for intelligence. We predict
> sequences by compressing them into short programs, then run the program to
> predict the next symbol. By Occam's Razor, the shortest program gives the
> best prediction. But there is no way to know whether you found it or if there
> is a better theory.
>

I would also pursue due to separatedness this being a reason for consciousness. 
There are better theories out there in general for separated agents. The 
environment is not infinitely dense, information is spread out. It takes energy 
to retrieve bits thus virtualization and thus communication.

John



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