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!)
“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.”
The difference comes in developing a theory for generalizing the study of protein folding as a scientific pursuit.
It looks like you'd like a grokable solution, but the problem might be just too complex to grasp for the human brain. "Solved" means they solved the protein puzzles on the official benchmark.
> but you would expect this with any suitable universal function approximator
Yeah, it's just that easy. Function approximator, engage! It took a team of Deep Mind researchers, two years and God knows how much compute. The universal function approximation theorem doesn't also say how to find that network.
Maybe all at once, but having a self-consistent, unified theory is very important.
We can't understand the full brain, but we can understand the essential components and how they work together. This still constitutes "interpretable".
> The universal function approximation theorem doesn't also say how to find that network.
Correct, and irrelevant.
Then launches into what can only be recognized as an exercise in pedantry.
This is true if you're only paying attention to how this system can be utilized to answer questions posed to it.
This achievement by itself, however, does not do much to push the science of protein folding much further. Those advances will come when people poke, prod, and break the model to develop a unified theory for protein folding.
This is what alphaFold does, and it's been verified to produce results at an apparent accuracy at or above something like X-ray protein crystallography. The advances will come, after these results are validated and accepted by the scientific community as whole, simply when groups start using this technique to immediately access the structure of proteins that in the past would be prohibitively expensive and time consuming or down right impossible to access before, and then use that knowledge to do their work.
You seem to think the first thought a researcher will have after this becomes widely available is, "Oh hey, I can now accurately predict the shape of an arbitrary protein which unlocks untold potential scientific progress on numerous scientific fronts, but the thing I want to spend my time on is trying to replicate the results of the network myself, so I can do it manually thousands of times slower...", which is patently inane.
The two are different concepts -- this isn't the typical HN pedantry.
"Solving" the problem would entail developing an interpretable algorithm for taking a string of amino acids and determining the 3D structure once folded.
Approximating a solution would entail simulating that algorithm, which is what their neural network is doing. It is of course usually accurate, but you would expect this with any suitable universal function approximator.
Props to DeepMind and congrats to CASP but is it not obvious that this is more hype-rhetoric for public consumption?