[Bartweiss]

I think the criticism is that it's not obvious whether success here was a function of improved performance, expanded throughput, expanded testing, or sheer luck.

Chess engines have clearly improved in both design and computing power over the years; doubling an engine's resources or pitting a new engine against an old one produces straightforwardly better play. But the drug-discovery technique in use here may not be "playing better" in terms of producing higher-quality predictions.

To extend the chess metaphor:

- Deep Fritz is a stronger player Deep Blue even with 4% as much computing power. This story does not appear to be an algorithmic breakthrough of that source.

- Deep Blue lost to Kasparov in 1996, then beat him in 1997 with double the computing power. That's a clear improvement in play, but not an improvement in efficiency. This story might represent such a change, modelling more prospective drugs to test higher-confidence candidates.

- If an AI that can only win 2% of games against humans plays 10 games, it has an 18% chance of beating someone. But over 100 games, it has an 87% chance of a win. This result might be a team with a larger testing budget claiming the 'first win' without any AI-side improvement.

- If a dozen grandmaster-level chess AIs play GMs, one of them will have to get the first win against a human. Labeling this result a 'breakthrough' in AI terms might be outright publication bias among equivalent projects.

As far as the drug, none of that really matters, except that efficiency improvements would have more potential to increase drug discovery. The drug itself is still useful, and the discovery is a proof of concept; in 1980 no possible computer would have beaten Kasparov. But this is being hailed as a breakthrough in AI in seriously questionable ways. The BBC article, for example, managed to imply that this specific project was novel and important for using neutral nets to produce a significant result.