Particle physics involves an intense data science challenge: signals of interest, like for the Higgs boson are often buried in backgrounds trillions of times as large. Over the last decade or so, there has been a revolution in machine learning, and over  the last year or two, the revolution has started to influence collider physics. For example, image recognition technology based on convolutional neural networks can be used to find patterns in the distribution of energy around a detector. Or recurrent neural networks designed for natural language processing can be applied to parse the substructure of jets. This talk will review some of these developments and discuss some exciting possibilities for the future.