This is a stellar post, well worth reading. I'm including the introduction in full here in the hopes that you'll take the time to read the entire post.
Deep Learning has been the core topic in the Machine Learning community the last couple of years and 2016 was not the exception. In this article, we will go through the advancements we think have contributed the most (or have the potential) to move the field forward and how organizations and the community are making sure that these powerful technologies are going to be used in a way that is beneficial for all.
One of the main challenges researchers have historically struggled with has been unsupervised learning. We think 2016 has been a great year for this area, mainly because of the vast amount of work on Generative Models.
Moreover, the ability to naturally communicate with machines has been also one of the dream goals and several approaches have been presented by giants like Google and Facebook. In this context, 2016 was all about innovation in Natural Language Processing (NLP) problems which are crucial to reach this goal.