Terrence Tao recently published an interesting take that zooms out from the details of the Navier Stokes drama.
Its an interesting observation he makes, because it is not dissimilar from the relatively common phenomenon of one academic lab getting scooped by another lab (usually by coincidence).
one academic lab getting scooped by another lab (usually by coincidence).
I was with you up to “usually by coincidence”.
There’s a long and sordid history in areas of chemistry and areas of biology of holding up a competing paper in review so you can scoop them. I’m sure it exists in physics as well. Certainly biophysics, but probably most subfields.
Often it’s a famous labs that can steamroll review or even just dump the work into PNAS as a “member contribution.”
At least one author of a famous inorganic chemistry textbook was rumored to do this routinely.
And I know of at least one National Academy member who swore off arxiv prepublication after getting scooped.
None of this makes it all right. But plagiarism and academic theft is old and definitely not always accidental.
I know this happens, but my impression during my phd was that many labs use popular methods to test popular questions, leading to a lot of simultaneous work. You see this in history as well.
This is interesting. He seems to imply that the AI will just provide the solution. He's concerned that the process of getting to that solution is the important bit. But I can see two versions of "process".
A.) The actual steps of the proof, which I assume the AI would provide.
B.) People, while working toward a solution, finding novel properties/methods along the way that open new avenues of research + new open problems.
Does B.) actually happen? Would knowing the solution to a problem stymie the process of finding new open questions? I would assume finding a solution may unlock other problems too. So maybe on balance it's not really bad?
I guess in the end, I'm just making the obvious case "the future is uncertain in the face of AI".
Well the argument is that B is no longer sustainable because of the scenario you describe in A. If you do a bunch of work on Navier Stokes but then OpenAI gets all the press, then what was the point?
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https://mathstodon.xyz/@tao/117237320796901560
Terrence Tao recently published an interesting take that zooms out from the details of the Navier Stokes drama.
Its an interesting observation he makes, because it is not dissimilar from the relatively common phenomenon of one academic lab getting scooped by another lab (usually by coincidence).
I was with you up to “usually by coincidence”.
There’s a long and sordid history in areas of chemistry and areas of biology of holding up a competing paper in review so you can scoop them. I’m sure it exists in physics as well. Certainly biophysics, but probably most subfields.
Often it’s a famous labs that can steamroll review or even just dump the work into PNAS as a “member contribution.”
At least one author of a famous inorganic chemistry textbook was rumored to do this routinely.
And I know of at least one National Academy member who swore off arxiv prepublication after getting scooped.
None of this makes it all right. But plagiarism and academic theft is old and definitely not always accidental.
I know this happens, but my impression during my phd was that many labs use popular methods to test popular questions, leading to a lot of simultaneous work. You see this in history as well.
This is interesting. He seems to imply that the AI will just provide the solution. He's concerned that the process of getting to that solution is the important bit. But I can see two versions of "process".
A.) The actual steps of the proof, which I assume the AI would provide. B.) People, while working toward a solution, finding novel properties/methods along the way that open new avenues of research + new open problems.
Does B.) actually happen? Would knowing the solution to a problem stymie the process of finding new open questions? I would assume finding a solution may unlock other problems too. So maybe on balance it's not really bad?
I guess in the end, I'm just making the obvious case "the future is uncertain in the face of AI".
Well the argument is that B is no longer sustainable because of the scenario you describe in A. If you do a bunch of work on Navier Stokes but then OpenAI gets all the press, then what was the point?