Right but we also can't generalize from 2 out 3, and without knowing this figure it's really hard to say how useful this is, no?
> Knowing how ML works, I'm surprised it didn't give any indication of low confidence.
This is actually a hard problem in ML, for example in NLP, many people assume a high log prob score means a high confidence but it is not true at all.
Thank you for the clarification. I wasn't aware of this, as I'm most familiar with super-basic/old NLP techniques like BOW/RNN/LSTM/GRU techniques, where log prob scores seemed to me to be roughly correlated with result quality. I'm aware the landscape has changed recently with insights about high dimensional searches...
But after training you can recalibrate the temperature of the softmax on the test set and still get meaningful confidence scores (temperature calibration). Or you can use a variation of cross entropy called Focal Loss that will leave your logits un-squashed.
Not to give DeepMind too much credit, but it's also entirely possible that "both are correct". By the nature of how targets are selected for CASP, year over year there is higher likelihood that "pathologically difficult" proteins are presented for the competition, for example - a protein that exhibits polymorphic structure where the technique (say Xtal vs NMR) biases the "solved" fold in a huge way. I believe CASP tries to weed out proteins that we know are polymorphic, but you can never be sure, and again, as time marches on, those are the proteins that fundamentally are harder to solve, so it's likely they will be enriched in the test pool.
An extreme example is insulin. The structure of insulin is has been solved for 60? I think years, but when it's in the environment of its receptor, it looks TOTALLY different (solved 5? years ago). Having said that, doubtful that DeepMind could ascertain that structure, since the environment is super-super different.
I think that the modestly high error rate is an indication that deepmind is mostly interpolating and has solved the broad protein folding heuristic. It probably will get better.