What are the most significant machine learning advances?
So much has happened in the world of AI and machine learning that it is hard to fit in a single answer. Here is my attempt. 2017 saw the continuation (escalation?) of social issues around AI. Elon Musk continues to fuel the idea that we are getting closer and closer to killer AIs, to many people’s dismay. There has also been a lot of discussion about how AI will affect jobs in the next few years. Finally, we have seen a lot more focus being put on transparency and bias of AI algorithms. For the past few months I have been working on AI for medicine and healthcare. I am also happy to see that the rate of innovation in less “traditional” domains like healthcare is quickly picking up. AI and ML have been applied to medicine with years, starting with expert and Bayesian systems in the 60s and 70s. However, I often find myself citing papers that are only a few months old. Some of the recent innovations presented this year include the use of Deep RL, GANs, or Autoencoders to represent patient phenotypes. A lot of recent AI advances have also focused on Precision Medicine (highly personalized medical diagnosis and treatment) and genomics. All the big players are investing in AI in healthcare. Google has several teams, including Deepmind Healthcare who have presented several very interesting advances in AI for medicine, especially in automating medical imaging or the work that Fei Fei’s group is doing between Google and Stanford. Apple is also finding healthcare applications for their Apple Watch, and Amazon is “secretly” investing in healthcare. It is clear the space is ripe for innovation.