AI in 2018: More Deep Learning Extensions and Crazy Rich Bayesians

This is the second installment of a three-part piece on the advances made in artificial intelligence in 2018, by Yves Bergquist, founder and CEO of AI company Corto, and director of the AI and Neuroscience in Media Project at the Entertainment Technology Center at the University of Southern California (ETC@USC). Part one can be read here. With one new academic paper publisher every half hour or so in 2018, machine learning is still -- and by far -- the most vigorous domain of AI. And within machine learning, Deep Learning (also called Deep Neural Networks) still dominates the field. This year saw a lot of extensions of DL to new areas, especially natural language. ULMFiT, the Allen Institute's ELMo and of course Google's BERT, all used new DL architectures to deliver breakthrough accuracy performances in all areas of Natural Language Processing (NLP), ensuring that 2019 and 2020 will see a massive improvement in text analysis and chatbot deployment.