Neural Networks Are Cryptography in Reverse - Reiner Pope

This segment delves into the fundamental inverse relationship between cryptographic protocols and neural networks, illustrating how one aims to obscure structure while the other seeks to extract it. Reiner Pope explains how a randomly initialized neural network could function as a cipher, and draws parallels between the interpretability of neural networks via gradient descent and the concept of differential cryptanalysis.