So a professor at a major institution wrote 200 papers last year. Unlike other commenters I’ve observed so far, I think this is neither true research nor pure AI fraud. It is likely AI “slop” to varying degrees, but unlike a lot of slop there is probably real value within it. What I have not yet seen ascertained is whether any of it has been vetted, investigated, or curated by the author in a meaningful way. The real question is: what is the actual ambition here? I think the tell is the lack of submission to peer review.
I think this is a form of intellectual squatting. The nice version is it’s putting out a series of half-baked papers in the hopes of establishing a property right to the underlying ideas at an earlier stage of the research process than previously possible. The less generous interpretation is it’s dumping a series of haystacks on the plains and laying claim to the needles probabilistically within each. Imagine you are a person who has highly esoteric, potentially important ideas every day. Many of those ideas you suspect, based on some combination of experience and ego, are new in at least one dimension. You would like to get credit for that newness. For being first. What’s the problem?
The problem is that scholarship remains more perspiration than inspiration. Having a new idea is great, but it takes years to work through the nuance in sufficient detail that you can convince your peers of the coherence and originality of the contribution. During the minutes each day you are not working on this singular project you have the inspiration for other ideas, sometimes multiple within a single day. How frustrating is the proposition that someone else gets credit for the originality of contribution just because they had time to reveal it to the world while you were embroiled in your investigation of what is only one of your many score ideas!?
Ah, but meta-level inspiration has struck you! What if you took each one of those ideas, spent an hour curating a series of prompts around it, and then let Chat GPT (or another LLM) fabricate an entire research paper around it? It might not be good, correct, or even coherent, but it does somethine far more important. It establishes an intellectual property right to the claim of being first. Now, to be clear, you are fully aware of the deficiciency of your paper as an actual scholarly contribution, but if somone else writes a full paper you at least have something to point to and say “I was here first. Cite me. Hell, if I’m close enough you might even have to name it after me. Well, sure, us. But definitely include me. Glory shared via hypenhnation is better than no glory at all.”
Is it a contribution? That’s something that will vary on a case-by-case basis, but I expect far more misses than hits. The work isn’t there. It’s like plopping down a block of marble with a dramatic-ish sketch of a man on an adhered post-it note and claiming that Michaelangelo needs to share credit with you on any subsequent sculptures. It’s like asking people to cite that one cool tweet you did about how DNA is cool but maybe RNA could be useful in vaccines one day. Intellectual property rights trolling via AI blunderbuss.
BTW, I’m not 100% sure this isn’t an AI take on a modern Sokal hoax. An attempt to show how much AI slop is introducing a whole new version of Gresham’s Law to scholarship. But if we treat it as earnest, it’s proof that a very smart person can potentially disrupt the market for scholarship, patents, or any other intellectual property by laying claim to ideas in much the same way that the printing press undermined the market for plenary indulges. Flood the market, leave it to someone else to sort through the ecumenical consequences.
I have a little campaign to contribute to open source. I use AI. Getting something good enough to convince an already busy maintainer to review and accept is the real challenge: you don’t want to burn their time or your reputation with bad AI slop.
However I feel this has become easier at least for certain tasks. For example reviewing submissions with a different model than the one that originally wrote it helps a lot. (I’m also doing a human review before anything goes out. But that’s a scarce resource.)
What also helps me and what the academic in the article probably can’t do: I concentrate on contribution I can largely judge mechanically. So for example, I look for already reported bugs, ie bugs that at least one other user found annoying enough to bother to report. I almost always start by producing a mechanical reproduction of the circumstances that trigger the bug. Then it’s easy to tell mechanically whether the proposed fix fixes the bug.
Many projects are actually even more keen on the reliable reproducer than the bug fix themselves: these days the latter is easy to produce these days once you have the former. But the former is often seen as drudgery.
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