Modern neural networks, with billions of parameters, are so overparametrized that they can “overfit” even random, structureless data. Yet when trained on datasets with structure, they learn the underlying features. Understanding why overparametrization does not destroy their effectiveness is a fundamental challenge in AI. Two researchers, Andra Montanari (Stanford) and Pierfrancesco Urbani (IPhT) propose that…