On Sun, Jul 9, 2023 at 3:21 PM John Rose <[email protected]> wrote:

> On Sunday, July 09, 2023, at 2:19 PM, James Bowery wrote:
>
>
>
> Good predictors (including AIXI, other AI, and lossless compression)
> are necessarily complex...
> Two examples:
> 1. SINDy, mentioned earlier, predicts a time series of real numbers by
> testing against a library of different functions and choosing the
> simplest combination. The bigger the library, the better it works.
>
>
> Hide quoted text
>
> Predictors deduce from previously compressed, or induced, models of
> observations or data.
>
> The dynamical models _produced_ by SINDy do not contain the library of
> different functions contained in the SINDy program.
>
> It is the dynamical models produced by AIT that do the predicting when
> called upon by the SDT aspect of AIXI.
>
>
> A "mathematical compression" would represent the libraries in a
> mathematically dense form so you can produce more libraries in a
> constrained compressor.
>

Perhaps I should have said: "The dynamical models _produced_ by SINDy *contain
a sparse selection of* the library of different functions contained in the
SINDy program."

A Solomonoff Induction engine is not subject to any constraints on size.
That is the point I was trying to get across in response to Matt.  Indeed,
a Solomonoff Induction engine is not subject to any resource constraints
whatsoever -- including that it be computable.

The only thing constrained in size is the model being induced by the
Solomonoff Induction engine -- and *boy is it constrained in size!*

Constraining the *model* in size is the whole point of scientific induction
as proved by Solomonoff.

So when you mix the phrase "mathematical compression" with "constrained
compressor", you're conflating the engine doing the "mathematical
compression" with the only thing that is constrained in size:  The
compressed model produced by the resource profligate compressor.

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