Selection for Selectability: Inductive Biases in Evolution and in Neural Networks

A LessWrong essay published Saturday argues that evolution and neural network training share deeper structural similarities than just both being optimization processes. The author, writing under the name CarolusRenniusVitellius, contends that selection reshapes genome architecture so mutations align with environmental variation, much like feature learning in neural networks. The piece introduces concepts like the G-matrix and neutral networks to bridge the two fields, and it's part of a planned series applying evolutionary insights to machine learning.

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