I’m very excited to share this piece by Henry Kvinge about the overlap between machine learning research and pure mathematical domains like topology, algebra, and geometry. Henry, who is an AI researcher and mathematician at Pacific Northwest National Laboratory, makes a very compelling case that simply scaling existing methods is not all we need and gives a fascinating tour of some recent work applying these domains to ML. He concludes that instead of seeing scale-driven progress and empirical breakthroughs as a challenge to mathematical theory, mathematicians should embrace these developments as opportunities to develop new tools that can deepen our theoretical understanding. — Cole
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