This is...rather exaggerated. Scores based on residue distance-matrices have been around for a long time and don't perform especially well, so it was surprising

This is...rather exaggerated. Scores based on residue distance-matrices have been around for a long time and don't perform especially well, so it was surprising to me that the method described on the webpage would trounce the competition. Then I read the abstract.... While the description of method (page 11 from this paper: http://predictioncenter.org/casp13/doc/CASP13_Abstracts.pdf http://predictioncenter.org/casp13/doc/CASP13_Abstracts.pdf) is pretty vague, they make clear that much of their scoring and structure refinement uses the scoring function from Rosetta. That's a tell that the neural-network part of the method probably isn't sufficient to pick out good structures. The AI, in this case, is generating fragments (which is not exceptionally different from what Rosetta already does), and doing a beta-carbon-distance score. Basically, the machine-learning part is generating protein fragments and quickly stack-ranking structures that are created by monte carlo search. Everything else is done by a much more complicated physical model that has little/nothing to do with AI.