I don't think AlQuraishi really hits the mark in his critique. The mere fact that hundreds or thousands of people working on a problem for decades doesn't account for the fact that the field of machine learning has been growing extremely rapidly over the last decade, the compute power available has grown exponentially, and the people working on the problem simply weren't looking at the problem in the way that the deepmind people were looking at it.
If you were trying to get across the Atlantic, this would be like getting upset at a group of bridgebuilders for trying to solve the problem by building a bridge across instead of by inventing the airplane. The approaches are that different.
> and the people working on the problem simply weren't looking at the problem in the way that the deepmind people were looking at it.
>The approaches are that different.
I'm not sure if that analogy applies here. DeepMind wasn't the first group tackling structure prediction with machine learning. Their success lies in the innovations that they implemented (predicting interresidue distances as opposed to contacts, for example).
If you were trying to get across the Atlantic, this would be like getting upset at a group of bridgebuilders for trying to solve the problem by building a bridge across instead of by inventing the airplane. The approaches are that different.