My amateur take on this: This paper makes me think of the underlying premise of perceptrons -- the universal approximation theorem -- which states that a neura

My amateur take on this: This paper makes me think of the underlying premise of perceptrons -- the universal approximation theorem -- which states that a neural network of simple perceptrons can be combined to approximate most any mathematical equation with reasonable accuracy. At least, I hope that's a reasonable approximation of what it says. :) Even though real-life biological neurons are much more complex (and some can perform complex operations like XOR operations in a single node), a regular perception cannot produce XOR by itself. But combine a bunch of perceptrons together across a few layers, and you can not only perform XOR with a neural network, but pretty much any mathematical equation. This paper makes me think that they're attempting to go the reverse direction with it -- after training a neural network to model a quadratic formula or something -- can we then go backwards and approximate the neural network's behavior with a discrete math equation? This is a fascinating idea, and if it works, would send us a LONG way towards building interpretable AI!