In plain words: It reviews the math used to explain deep neural networks, the layered pattern-matching systems behind most of today's AI. It lays out the main theoretical directions, a few proven results, and the questions still open.
Abstract
We currently witness the spectacular success of artificial intelligence in both science and public life. However, the development of a rigorous mathematical foundation is still at an early stage. In this survey article, which is based on an invited lecture at the International Congress of Mathematicians 2022, we will in particular focus on the current "workhorse" of artificial intelligence, namely deep neural networks. We will present the main theoretical directions along with several exemplary results and discuss key open problems.
Gitta Kutyniok
arXiv:2203.08890 · cs.LG, math.HO, stat.ML · submitted Mar 16, 2022
abstract · pdf · html · 16 pages, 7 figures