Why are neural networks and cryptographic ciphers so similar?
Summary
An exploration of the surprising similarities between neural networks and cryptographic ciphers, highlighting shared design patterns such as sequential and parallel processing, alternating linear/nonlinear layers, and row/column mixing. The piece argues that weak correctness requirements, emphasis on mixing, and performance drive convergent solutions, with notes on cross-field ideas like reversible layers and potential AI–crypto parallels.