Neural nets deriveable from middle school algebra: high-order interactions implicit via layers
cephaloform · x · 2026-08-22
The author argues that "ban AI tech" advocates get uncomfortable when shown how simple training neural networks is, claiming it's derivable from middle school algebra. The core technical point is that while polynomial fits work, high-order interaction terms are computationally expensive; hence, deep learning achieves this implicitly by stacking low-order interactions multiple times.
Related event: Neural Networks Are Just High-School Algebra, Making AI Bans Impractical(3 posts)→
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