Highlights of 2018

We end 2018 with a round-up of some of the research, talks, sci-fi, visualizations/art, and a grab bag of other stuff we found particularly interesting, enjoyable, or influential this year (and we're going to be a bit fuzzy about the definition of "this year")! In addition to our own research, on recommendation engines, multi-task learning, and federated learning, we found three other themes particularly interesting. At NIPS in December 2017, Ali Rahimi (and Ben Recht) delivered an address that asserted that modern deep learning is more like alchemy than science. We won't attempt to paraphrase their short talk, but many of us found it compelling, and it's certainly worth watching or reading. This lead to much discussion in the deep learning community, and the appearance of a subdiscipline that treats deep learning as an observational science (see e.g.