This sounds misguided. In the little experience I had, I've seen that models get basic knowledge so absolutely wrong that giving them any sort of independence will not result in publications that positively impact a professor's reputation, or contribute to science. Or at least the reviews and papers I read that had AI content did not give me the impression that we should have more of this. And they require much more supervision, with the added issue that they cannot learn in the long term through your interactions, and without the enjoyment of teaching something to someone. They're really good at finding papers though. Perhaps because navigating search engines has become a pain. Perhaps this will be the case in the future, but saying you're tempted right now is like saying you're being tempted to replace your HPC with quantum computers. It's a bit early.
Upon reading this:
> The issue is not whether my students are valuable. In the long run, they are invaluable. The issue is that their value emerges slowly, whereas AI delivers immediate returns.
I had the thought that it's more like hiring only autistic/on-the-spectrum employees that will on whims do exactly what their interpretation was, or possibly worse literally what you said without considering further consequences.
Sounds a bit like externalising the learning cost (of AI models) is preferred to investing the time into training the students.
You think? I will get banned from HN if I bring up that these models are fundamentally theft but we just don't put them in jail because they had the foresight to bribe the trump admin like everyone else who wants favor did.
Also 90% of citations generated by AI are wrong or straight up don’t even exist. It’s got such a long way to go to be able to reliably write credible papers.
[Source: https://www.reddit.com/r/AskReddit/comments/o6hlry/statistic... ]
Your source is a 5 year AMA post that it itself claims is made it.
While funny, it does nothing to prove your assertion.
>While funny, it does nothing to prove your assertion.
Unless that citation was generated by AI.
I think you missed the point. Yes it was meant to be humorous, and also to emphasise one of the reasons AI-generated citations are completely untrustworthy, especially with the growing number of AI-generated (junk) papers being published.
No, I had no intention of trying to offer a real source for the accuracy of AI generated citations. It is not hard to Google, search HN or even (ironically) use AI to search, to find numerous relatively recent studies discussing the problem or highlighting specific cases of respected journals/conferences publishing papers with junk citations.
It feels like allowing fake citations in the output from the AI means that you didn't do even the barest minimum of verification (i.e. tell the AI to verify it by sending a new AI to download the pdf that matches that DOI and verifying that it matches what the citation says).
Yeah I tried building such a tool. The problem was two fold:
1) Automated fetching of papers is difficult. API approaches are limited, and often requires per-journal development, scraping approaches are largely blocked, and AI- approaches require web fetch tools which are often blocked and when not, they consume a lot of credits/tokens very quickly.
2) AI generates so many hallucinated citations it’s very hard to know what a given citation was even supposed to be. Sure you can verify one link, but when you start trying to verify and correct 20 to 40 citations, you end up having to deal with hundreds or thousands of citations just to get to a small number of accurate and relevant ones, which rapidly runs you out of credits/tokens on Claude, and API pricing is insane for this use-case. It’s not possible to just verify the link, as “200 Status” isn’t enough to be confident the paper actually exists and actually contains the content the AI was trying to cite. And if it requires human review anyway, then the whole thing is pointless because a human could more quickly search, read and create citations than the AI tool approach (bearing in mind most researchers aren’t starting from scratch - they build up a personal ‘database’ of useful papers relevant to their work, and having an AI search it isn’t optimising any meaningful amount of work; so the focus has to be on discovering new citations).
All in all, AI is a very poor tool for this part of the problem, and the pricing for AI tools and/or APIs is high enough that it’s a barrier to this use case (partly due to tokens, and partly because the web search and web fetch tools are so relatively expensive).
Interesting, tools like Zotero seem to have sorted out the pdf fetching (and metadata + abstract fetching even without institutional access to the pdf). Did you try building the fetching on top of that?
AFAICT Zotero relies on scanning what you browse, not on suggesting citations based on a draft paper. It’s not solving the same problem.
I meant for point 1. Zotero will accept a doi/arxiv link (among other) and download the public metadata (authors, journal, abstract) for you so you don't need to build something for that end. AI cites a paper, copy DOI into Zotero, analyze info Zotero returns.