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The CEO of deepmind is an author on the paper, his PhD is in biology (but a totally different field, cog neuroscience). The rest of the authors include all the ingredients you'd expect from a modern successful quantitative scientific collaboration: a university professor of Bioinformatics who has a huge prior knowledge of computer-aided protein folding (http://www0.cs.ucl.ac.uk/staff/D.Jones/), several postdoc or post-postdoc level bio/protein experts with knowledge in physical simulation (the method they used ultimately works as distance and angle constraints on the protein structure), as well as a bunch of world-class machine learning/computer science folks. They're not gaming things. DeepMind is good at games, and CASP is a competition, but everybody who does well at CASP is already doing the same sorts of things that DeepMind did to score well. And they really did come up with a good system that was demonstrably better (I want to give them credit, I just don't think 'breakthrough' is really correct). But one thing I know about CASP (I competed one year) is that after 2 years, whatever the previous winning team did is duplicated by the other top teams, and 2 years after that, everybody can do it. I think ML is moving protein folding competitions like CASP to be faster now, because you can put your code, training data generator (much of the hard work in protein folding is coming up with good training data), a materialized copy of the training data the generator generates, and a trained model checkpoint on github, so after 2 years, everybody will be able to do what DM did at the previous competition. I think this has been one of the really important improvements in the last few years in protein folding- the computational infrastructure, both the training data, the systems to train on, and the tools to do training, have all gotten much better, and lots of people have gotten good at using them. That's a really promising sign and I hope it takes over more quantitative science. |