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Comment by astroalex | Hacker News Reader

The distinction you're making between "solved" and "closely approximated" makes logical sense to me. However, if I'm interpreting the AlphaFold results correctly, this distinction isn't practically significant, right?

If you can approximate an algorithm with error that is "below the threshold that is considered acceptable in experimental measurements" (to quote another HN comment), then you have something as good as the algorithm itself for all intents and purposes.

Therefore the use of the word "solve" doesn't qualify as hype-rhetoric, and the distinction you're making does seem somewhat pedantic (even if technically true).

(I'm speaking as someone with only the tiniest amount of stats/ML experience, so I could be totally wrong!)

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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?

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.”

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

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

Practically there is little difference if all you're interested in is determining folds from protein sequences.

The difference comes in developing a theory for generalizing the study of protein folding as a scientific pursuit.

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