In this talk, I will first give an overview perspective and taxonomy of major work the field, as motivated by our recent survey paper on meta-learning in neural networks. I hope that this will be informative for newcomers, as well as reveal some interesting connections and differences between the methods that will be thought-provoking for experts. I will then give a brief overview of recent meta-learning work from my group, which covers some broad issues in machine learning where meta-learning can be applied, including dealing with domain-shift, data augmentation, learning with label noise, and accelerating single task RL. Along the way, I will point out some of the many open questions that remain to be studied in the field.