The surprising structural similarities between neural networks and cryptographic ciphers

Summary

This article explores the structural and algorithmic similarities between neural network architectures (like recurrent neural networks) and cryptographic cipher designs (like the Sponge construction). It argues that both fields rely on similar principles of sequence processing, state manipulation, and information transformation, despite serving vastly different purposes—one for learning patterns and generating text, the other for encrypting data.