[colonelcanoe]

It might be the case that the relevant, practical threshold now tightens. For example, perhaps it is easier to experimentally verify a protein shape predicted by an algorithm than it is to experimentally determine the protein shape?

[robocat]

From: https://www.sciencemag.org/news/2020/11/game-has-changed-ai-...

“The organizers even worried DeepMind may have been cheating somehow. So Lupas set a special challenge: a membrane protein from a species of archaea, an ancient group of microbes. For 10 years, his research team tried every trick in the book to get an x-ray crystal structure of the protein. ‘We couldn’t solve it.’”

“But AlphaFold had no trouble. It returned a detailed image of a three-part protein with two long helical arms in the middle. The model enabled Lupas and his colleagues to make sense of their x-ray data; within half an hour, they had fit their experimental results to AlphaFold’s predicted structure. ‘It is almost perfect,’ Lupas says.”

[intpx]

exactly. Even an incomplete map with somewhat limited resolution makes navigation a hell of a lot easier than flying blind. This effectively is a data reduction solution-- if you have a fuzzy shape of the thing you are trying to model, and you learn the mechanics better with each thing you model, your ability to quickly and accurately reach a goal improves

[uoaei]

That's true and also is not what is being challenged by my comment.