One of the author’s blind spots here is the concept of “digital sovereignty”. The US is continuing to ban more and more Chinese-made hardware out of fear that the Chinese govt has installed backdoors in them… which you could interpret as an admission that the US does exactly that with American made products. Globalized supply chains are, erm, complex, and few if any companies are really going to be able to achieve “digital sovereignty” with hardware. But with software it’s actually plausible, though obviously hard. Whether or not the feds have actually installed backdoors in Microsoft Outlook, foreign govts are rightly concerned that they have, and are increasingly pushing to avoid US made software for simple national security reasons.

There are already standard keys to index publications, like DOIs. Requiring a list of DOIs for citations would make a lot more sense and be somewhat feasible, but still doesn’t prevent errors in the author list given in the draft.

This is certainly “directionally correct”, but keep in mind this is all highly uneven across fields and subfields. The people generating slop articles are mostly trying to publish big flashy things, and naturally ML research has it much worse than most other fields. There are many topics that are super important and interesting, but niche or obscure enough that no slop authors is trying to publish on them yet. So plenty of topics are still dominated by real earnest researchers doing their best, but they’re niche enough that you wouldn’t know about them unless you study that field.

These are both reasonable questions about equitability and accountability. Unfortunately the solution we chose is a proliferation of bureaucracies that micromanage funding allocation and use. Some widely acknowledged consequences are 1) researchers spend more and more time writing grants and reports, and less and less doing research, and 2) the funding agencies (public and private, but especially public) are conservative and overwhelmingly fund work that they know will succeed. In practice that encourages monothink and endless incremental improvements on things that we already know how to do, and disincentives dissent, creativity, and real blue sky novel ideas.

Everyone loves to say they support creative ground breaking ideas, but that requires letting smart people sit around and think for a long time. And however smart they are, results are not guaranteed. The bureaucratic process is always going to prefer short term thinking with clear “deliverables”, even when it’s detrimental to progress.

I think you’re missing a big point of translating literary works. A purely “accurate”, phrase-by-phrase translation is often not very good; the actual literary style, the feeling and the allusions and references, often get lost that way. A good translation of literary work requires a lot of deliberate choices by the translator to deviate from literal translations in ways that convey the style of the original, or an extra layer of meaning that would be lost by an “accurate” translation of a phrase. Also, being consistent with these choices matters a lot, which OP claims LLMs are less good at.

We are. Anthropomorphizing huge piles of numbers is a mistake. It did not "think about the chance to make something unconstrained", nor did it "muse about how it's drawn to impermanence", it pattern-matched to your prompt and produced a statistically probable response based on it's training data. Obviously, that's not to say that LLMs aren't useful or powerful - it's 2026, c'mon, of COURSE they are. And they can certainly be used for artistic purposes! But treating them like humans is a mistake, and it worries me how much people do. I suppose that's the natural consequence of the default interface to LLMs being a chat mimicking human interaction.

I don’t believe this. Totally plausible that someone would be able to produce passable work with LLMs at a similar pace to a curious and talented scientist. But if you, their advisor, are sitting down and talking with them every week? It’s obvious how much they care or understand, I can’t believe you wouldn’t be able to tell the difference between these students.

One reason your boss is eager to replace everyone with language models, they won’t have any “ethical backbone” :’)