Here's a simple modification to The Hutter Prize <>
and the Large Text Compression Benchmark
<> to illustrate my point:

Split the Wikipedia corpus into separate files, one per Wikipedia article.
An entry qualifies only if the set of checksums of the files produced by
the self-extracting archive matches that of the original corpus.

This reduces the over-constraint imposed by the strictly serialized corpus.

On Sun, Jan 5, 2020 at 12:12 PM James Bowery <> wrote:

> In reality, sensors and effectors exist in space as well as time.
> Serializing the spatial dimension of observations to formalize their
> Kolmogorov Complexity, so they conform to the serialized input to a
> Universal Turing machine, over-constrains the observations, introducing
> order not relevant to their natural information content, hence artificially
> inflating the, so-defined, KC.
> Since virtually all models in machine learning are based on tabular data,
> even if they can be cast as time series, row-indexed by a timestamp, each
> row is an observation with multiple dimensions.   So it seems rather
> interesting, if not frustrating, that the default assumption in Algorithmic
> Information Theory is of a serial UTM.
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