Adversarial machine learning has progressed rapidly over the past few years, with currently over 1,000 papers on this topic and growing at a rate of over a paper a day. In this talk, I survey some of the most interesting recent results, ranging from practical applications of adversarial machine learning to fundamental research investigating why adversarial examples exist in the first place. I conclude with a selection of future research directions that would advance the body of knowledge in this important field.