One day, mathematicians started thinking really hard about moving piles of dirt around, and the Wasserstein distance was born. It measures the difference between two probability distributions, in a way that is different (and sometimes better) than entropy and the L^p metrics.  In this talk, I will introduce the field known as optimal transport, and talk about some applications, mainly to machine learning.