the deep neural networks that are dominating today’s efforts in AI—with billions, even hundreds of billions of neurons and tens, even hundreds of hidden layers—are challenging the theoretical foundations of machine learning. For one, these networks aren’t as susceptible to the curse of dimensionality as was expected, for reasons that aren’t entirely clear.
