Why are neural networks and cryptographic ciphers so similar? (reiner.org)

Recent insights reveal striking similarities between neural networks and cryptographic ciphers, particularly in their algorithmic structures. Although training language models and encrypting data appear fundamentally different, both employ sequence processing techniques that absorb inputs into a state before producing outputs. Notably, modern approaches in both fields parallel the efficient processing of data, utilizing combinations of linear and nonlinear layers, which allow for greater complexity and performance optimization, particularly in hardware. This shared architecture underscores the significance of their similar goals: ensuring thorough mixing of information while maintaining simplicity in design. The implications of these similarities extend beyond theoretical interest; they suggest that efficient, "deeply parallel repeated-layer mixers" could be a foundational structure for future algorithms in both AI and cryptography. The fields, driven by low-level performance demands and the necessity for extensive output complexity, rely heavily on simple yet effective primitives. This convergence in algorithmic design not only highlights the potential for cross-pollination of ideas—like the incorporation of cryptographic concepts into neural networks—but may also lead to innovative solutions that could address challenges in both domains. This research paves the way for further exploration of collaborative advancements between AI and cryptography, potentially redefining approaches to information processing and security.