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cousin_it comments on An inflection point for probability estimates of the AI takeoff? - Less Wrong
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Here's an example from the paper that helps illustrate the difference: if the sequence is a gigabyte of random data repeated forever, it can be predicted with finitely many errors by the simple program "memorize the first gigabyte of data and then repeat it forever", though the sequence itself has high K-complexity.
No it has not. The algorithm for copying the first GB forever is small and the Kolmogorov's complexity is just over 1GB.
For the entire sequence.
Yes, but the predictor's complexity is much lower than 1GB.
The paper also gives an example of a single predictor that can learn to predict any eventually periodic sequence, no matter how long the period.
Predictor should remember what happened. It has learned. Now it's 1 GB heavy.
It looks like you just dislike the definitions in the paper and want to replace them with your own. I'm not sure there's any point in arguing about that.