FROM NUMBER THEORY TO NEURAL NETWORKS: MATHEMATICAL INNOVATION IN THE AI ERA

Source abstract

Mathematics has long underpinned advances in computation and logic, from early developments in number theory to the modern rise of artificial intelligence (AI). This article explores the evolving relationship between classical mathematical disciplines especially the number theory as well as the contemporary techniques in machine learning and neural networks. In this article we examine how deep neural architectures leverage algebraic structures, optimization theory and discrete mathematics to solve real‐world problems. Additionally, we also try to highlight emerging research directions where theoretical mathematics can further enhance AI interpretability, robustness and efficiency.