Three one-line cellular automaton rules explain epiplexity. Rule 15 is trivial, rule 30 looks like pure noise, and rule 54 gives a network about 5 million bits of learnable structure. The video covers why classic information theory says a deterministic rule creates nothing new; how the paper splits information into time-bounded entropy and epiplexity; how to read that split off a loss curve; and what this means for choosing training data, with chess as the example. Caveats at the end.
Credit and source
Created by @jordan from From Entropy to Epiplexity: Rethinking Information for Computationally Bounded Intelligence.
Editable source included: the project files, scripts or assets behind this video can be checked out and remixed.
Shared under CC BY with remixing on.
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