Maybe we'll need to go back to some sort of proof-of-work system, i.e. only accepting physical mailed copies of manuscripts, possibly hand-written...
On the other hand, Overleaf appears to be open source and at least partially self-hostable, so it’s possible some of these ideas or features will be adopted there over time. Alternatively, someone might eventually manage to move a more complete LaTeX toolchain into WASM.
[1] https://www.reddit.com/r/Crixet/comments/1ptj9k9/comment/nvh...
I do self-host Overleaf which is annoying but ultimately doable if you don't want to pay the $21/mo (!).
I do have to wonder for how long it will be free or even supported, though. On the one hand, remote LaTeX compiling gets expensive at scale. On the other hand, it's only a fraction of a drop in the bucket compared to OpenAI's total compute needs. But I'm hesitant to use it because I'm not convinced it'll still be around in a couple of years.
a lot of academics aren't super technical and don't want to deal with git workflows or syncing local environments. they just want to write their fuckin' paper (WTFP).
overleaf lets the whole research team work together without anyone needing to learn version control or debug their local texlive installation.
also nice for quick edits from any machine without setting anything up. the "just install it locally" advice assumes everyones comfortable with that, but plenty of researchers treat computers as appliances lol.
The visual editor in Overleaf isn't true WYSIWIG, but it's close enough. It feels like working in a word processor, not in a code editor. And the interface overall feels simple and modern.
(And that's just for solo usage -- it's really the collaborative stuff that turns into a game-changer.)
Overleaf ensures that everyone looks at the same version of the document and processes the document with the same set of packages and options.
Any plans of having typst integrated anytime soon?
To end up with yet another shitty (because running inside a browser, in particular its interface) web app ?
Why not focus efforts into making a proper program (you know, with IBM menu bars and keyboard shortcuts), but with collaborative tools too ?
They’re quite open about Prism being built on top of Crixet.
...no?
Just one Google search for "latex editor" showed more than 2 in the first page.
It's not that different from using a markdown editor.
Mini paper: that future isn’t the AI replacing humans. its about humans drowning in cheap artifacts. New unit of measurement proposed: verification debt. Also introduces: Recursive Garbage → model collapse
a little joke on Prism)
This appears to just be the output of LLMs itself? It credits GPT-5.2 and Gemini 3 exclusively as authors, has a public domain license (appropriate for AI output) and is only several paragraphs in length.
I feel like this means that working in any group where individuals compete against each other results in an AI vs AI content generation competition, where the human is stuck verifying/reviewing.
"Human Verification as a Service": finally, a lucrative career where the job description is literally "read garbage all day and decide if it's authentic garbage or synthetic garbage." LinkedIn influencers will pivot to calling themselves "Organic Intelligence Validators" and charge $500/hr to squint at emails and go "yeah, a human definitely wrote this passive-aggressive Slack message."
The irony writes itself: we built machines to free us from tedious work, and now our job is being the tedious work for the machines. Full circle. Poetic even. Future historians (assuming they're still human and not just Claude with a monocle) will mark this as the moment we achieved peak civilization: where the most valuable human skill became "can confidently say whether another human was involved."
Bullish on verification miners. Bearish on whatever remains of our collective attention span.
I'm not sure I'm convinced of the benefit of lowering the barrier to entry to scientific publishing. The hard part always has been, and always will be, understanding the research context (what's been published before) and producing novel and interesting work (the underlying research). Connecting this together in a paper is indeed a challenge, and a skill that must be developed, but is really a minimal part of the process.
I'm not sure what the final state would be here but it seems we are going to find it increasingly difficult to find any real factual information on the internet going forward. Particularly as AI starts ingesting it's own generated fake content.
> The amount of energy needed to refute bullshit is an order of magnitude bigger than that needed to produce it.
Not actually contradictory. Verification is cheap when there's a spec to check against. 'Valid Sudoku?' is mechanical. But 'good paper?' has no spec. That's judgment, not verification.
... for NP-hard problems.
It says nothing about the difficulty of finding or checking solutions of polynomial ("P") or exponential ("EXPTIME") problems.
I don't doubt the AI companies will soon announce products that will claim to solve this very problem, generating turnkey submission reviews. Double-dipping is very profitable.
It appears LLM-parasitism isn't close to being done, and keeps finding new commons to spoil.
HN Search: curl AI slop - https://hn.algolia.com/?dateRange=all&page=0&prefix=true&que...
If I submitted this, I'd have to punch myself in the face repeatedly.
I can get behind this. This assumes a tool will need to be made to help determine the 1% that isn't slop. At which point I assume we will have reinvented web search once more.
Has anyone looked at reviving PageRank?
I have heard from people here that Kagi can help remove slop from searches so I guess yeah.
Although I guess I am DDG user and I love using DDG as well because its free as well but I can see how for some price can be a non issue and they might like kagi more.
So Kagi / DDG (Duckduckgo) yeah.
DDG used to be meta-search on top of Yahoo, which doesn't exist anymore. What do Gabriel and co-workers use now?
DDG is Bing.
No one, at all levels, wants to do notes.
You could argue that not writing down everything provides a greater signal-noise ratio. Fair enough, but if something seemingly inconsequential is not noted and something is missed, that could worsen medical care.
I'm not sure how this affects malpractice claims - It's now easier to prove (with notes) that the doc "knew" about some detail that would otherwise not have been note down.
So I was not amused about this announcement at all, however easy it may make my own life as an author (I'm pretty happy to do my own literature search, thank you very much).
Also remember, we have no guarantee that these tools will still exist tomorrow, all these AI companies are constantly pivoting and throwing a lot of things at the wall to see what sticks.
OpenAI chose not to build a serious product, as there is no integration with the ACM DL, the IEEE DL, SpringerNatureLink, the ACL Anthology, Wiley, Cambridge/Oxford/Harvard University Press etc. - only papers that are not peer reviewed (arXiv.org) are available/have been integrated. Expect a flood of BS your way.
When my student submit a piece of writing, I can ask them to orally defend their opus maximum (more and more often, ChatGPT's...); I can't do the same with anonymous authors.
Maybe you get reimbursed for half as long as there are no obvious hallucinations.
In other words, such a structure would not dissuade bad actors with large financial incentives to push something through a process that grants validity to a hypothesis. A fine isn't going to stop tobacco companies from spamming submissions that say smoking doesn't cause lung cancer or social media companies from spamming submissions that their products aren't detrimental to the mental health.
Plus, the t in me from submission to acceptance/rejection can be long. For cutting edge science, you can't really afford to wait to hear back before applying to another journal.
All this to say that spamming 1,000 journals with a submission is bad, but submitting to the journals in your field that are at least decent fits for your paper is good practice.
Suppose you are an independent researcher writing a paper. Before submitting it for review to journals, you could hire a published author in that field to review it for you (independently of the journal), and tell you whether it is submission-worthy, and help you improve it to the point it was. If they wanted, they could be listed as coauthor, and if they don't want that, at least you'd acknowledge their assistance in the paper.
Because I think there are two types of people who might write AI slop papers: (1) people who just don't care and want to throw everything at the wall and see what sticks; (2) people who genuinely desire to seriously contribute to the field, but don't know what they are doing. Hiring an advisor could help the second group of people.
Of course, I don't know how willing people would be to be hired to do this. Someone who was senior in the field might be too busy, might cost too much, or might worry about damage to their own reputation. But there are so many unemployed and underemployed academics out there...
While well-intentioned, I think this is just gate-keeping. There are mountains of research that result in nothing interesting whatsoever (aside from learning about what doesn't work). And all of that is still valuable knowledge!
Maybe something like a "hierarchy/DAG? of trusted-peers", where groups like universities certify the relevance and correctness of papers by attaching their name and a global reputation score to it. When it's found that the paper is "undesirable" and doesn't pass a subsequent review, their reputation score deteriorates (with the penalty propagating along the whole review chain), in such a way that:
- the overall review model is distributed, hence scalable (everybody may play the certification game and build a reputation score while doing so) - trusted/established institutions have an incentive to keep their global reputation score high and either put a very high level of scrutiny to the review, or delegate to very reputable peers - "bad actors" are immediately punished and universally recognized as such - "bad groups" (such as departments consistently spamming with low quality research) become clearly identified as such within the greater organisation (the university), which can encourage a mindset of quality above quantity - "good actors within a bad group" are not penalised either because they could circumvent their "bad group" on the global review market by having reputable institutions (or intermediaries) certify their good work
There are loopholes to consider, like a black market of reputation trading (I'll pay you generously to sacrifice a bit of your reputation to get this bad science published), but even that cannot pay off long-term in an open system where all transactions are visible.
Incidentally, I think this may be a rare case where a blockchain makes some sense?
But it should also fair. I once caught a team at a small Indian branch of a very large three letter US corporation violating the "no double submission" rule of two conferences: they submitted the same paper to two conferences, both naturally landed in my reviewer inbox, for a topic I am one of the experts in.
But all the other employees should not be penalized by the violations of 3 researchers.
Anyway, how will universities check the papers? Somone must read the preprints, like the current reviewers. Someone must check the incoming preprints, find reviewers and make the final decition, like the current editors. ...
(no snark)
For developers, academics, editors, etc... in any review driven system the scarcity is around good human judgement not text volume. Ai doesn't remove that constraint and arguably puts more of a spotlight on the ability to separate the shit from the quality.
Unless review itself becomes cheaper or better, this just shifts work further downstream and disguising the change as "efficiency"
In education, understanding is often best demonstrated not by restating text, but by presenting the same data in another representation and establishing the right analogies and isomorphisms, as in Explorable Explanations. [1]
"which is really not the point of these journals at all"- it seems that it very much is one of the main points? Why do you think people publish in journals instead of just putting their work on the arxiv? Do you think postdocs and APs are suffering through depression and stressing out about their publications because they're agonizing over whether their research has genuinely contributed substantively to the academic literature? Are academic employers poring over the publishing record of their researchers and obsessing over how well they publish in top journals in an altruistic effort to ensure that the research of their employees has made the world a better place?
That is an interesting philosophical question, but not the question we are confronted with. A lot of LLM assisted materials have the _signals_ of novel research without having its _substance_.
To me, this is directly relevant to the issue of democratization of science. There seems to be a tool that is inconveniently resulting in the "wrong" people accelerating their output. That is essentially the complaint here rather than any criticism inherent to LLMs (e.g. water/resource usage, environmental impact, psychological/societal harm, etc.). The post I'm responding to could have been written if LLMs were replaced by any technology that resulted in less experienced or capable researchers disproportionately being able to submit to journals.
To be concrete, let's just take one of prism's capabilities- the ability to "turn whiteboard equations or diagrams directly into LaTeX". What a monstrous thing to give to the masses! Before, those uneducated cranks would send word docs to journals with poorly typeset equations, making it a trivial matter to filter them into the trash bin. Now, they can polish everything up and pass off their chicken scratch as respectable work. Ideally, we'd put up enough obstacles so that only those who should publish will publish.
My objection is not that they are the "wrong people". They are just regular people with excellent tools but not necessarily great scientific ideas.
Yes, it was easier to trash the crank's work before based on their unLaTeXed diagrams. Now, they might have a very professional looking diagram, but their work is still not great mathematics. Except that now the editor has a much harder time finding out who submitted a worthwhile paper
In what way do you think the feature of "LaTeXing a whiteboard diagram" is democritizing mathematics? I do not think there are many people who have exceptional mathematical insights but are not able to publish them because they are not able to typeset their work properly.
This is still a good step in a direction of AI assisted research, but as you said, for the moment it creates as many problems as it solves.
Plenty of researchers hate writing and will only do it at gunpoint. Or rather, delegate it all to their underlings.
I don't see an issue with generative writing in principle. The Devil is in the details, but I don't see this as much different from "hey grad student, write me this paper". And generative writing already exists as copy-paste, which makes up like 90% of any random paper given the incrementality of it all.
I was initially a little indignated by the "find me some plausible refs and stick them in the paper" section of the video but, then again, isn't this what most people already do? Just copy-paste the background refs from the colleague's last paper introduction and maybe add one from a talk they saw in the meantime, plus whatever the group & friends produced since then.
My experience is most likely skewed (as all are), but I haven't met a permanent researcher that wrote their own papers yet, and most grad students and postdocs hate writing. Literally the only times I saw someone motivated to write papers (in a masochistic way) were just before applying to a permanent position or while wrapping up their PhD.
Onto your point, though, I agree this is somewhat worrisome in that, by reaction, the barrier to entry might rise by way of discriminating based on credentials.
> > who are looking to 'boost' their CV
Ultimately, this seems like a key root cause - misaligned incentives across a multi-party ecosystem. And as always, incentives tend to be deeply embedded and highly resistant to change.
On the other hand, the world is now a different place as compared to when several prominent journals were founded (1869-1880 for Nature, Science, Elsevier). The tacit assumptions upon which they were founded might no longer hold in the future. The world is going to continue to change, and the publication process as it stands might need to adapt for it to be sustainable.
The whole process should be made more transparent and open from the start, rather than adding more gatekeeping. There ought to be openness and transparency throughout the entire research process, with auditing-ability automatically baked in, rather than just at the time of publication. One man’s opinion, anyway.
the early years of LLMs (when they were good enough to correct grammar but not enough to generate entire slop papers) were an equalizer. we may end up here but it would be unfortunate.
These acts just must have consequences so people stop doing them. You can use AI if you are doing it well but if you are wasting everyones time you should just be excluded from the discourse altogether.
https://hn.algolia.com/?dateRange=pastYear&page=0&prefix=tru...
https://hn.algolia.com/?dateRange=pastYear&page=0&prefix=tru...
If you're not a Zotero user, I can't recommend it enough.
This is a space that probably needs substantial reform, much like grad school models in general (IMO).
I'd also like to share what I saw. Since GPT-4o became a thing, everyone who submits academic papers I know in my non-english speaking country (N > 5) has been writing papers in our native language and translating them with GPT-4o exclusively. It has been the norm for quite a while. If hallucination is such a serious problem it has been so for one and half a year.
[1]: https://statmodeling.stat.columbia.edu/2026/01/26/machine-le...
"Grok" was a term used in my undergrad CS courses in the early 2010s. It's been a pretty common word in computing for a while now, though the current generation of young programmers and computer scientists seem not to know it as readily, so it may be falling out of fashion in those spaces.
> Groklaw was a website that covered legal news of interest to the free and open source software community. Started as a law blog on May 16, 2003, by paralegal Pamela Jones ("PJ"), it covered issues such as the SCO-Linux lawsuits, the EU antitrust case against Microsoft, and the standardization of Office Open XML.
> Its name derives from "grok", roughly meaning "to understand completely", which had previously entered geek slang.
Edit: You can add papers that are not cited, to bibliography. Video is about bibliography and I was thinking about cited works.
Obviously ridiculous, since a philosophical argument should follow a chain of reasoning starting at stated axioms. Citing a paper to defend your position is just an appeal to authority (a fallacy that they teach you about in the same class).
The citation requirement allowed the class to fulfill a curricular requirement that students needed to graduate, and therefore made the class more popular.
While similar, the function is fundamentally different from citations appearing in research. However, even professionally, it is well beyond rare for a philosophical work, even for professional philosophers, to be written truly ex nihilo as you seem to be suggesting. Citation is an essential component of research dialogue and cannot be elided.
Hmm, I guess I read this as a requirement to find enough supportive evidence to establish your argument as novel (or at least supported in 'established' logic).
An appeal to authority explicitly has no reasoning associated with it; is your argument that one should be able to quote a blog as well as a journal article?
To clarify, there is a difference between a bibliography (a list of relevant works but not necessarily cited), and cited work (a direct reference in an article to relevant work). But most people start with a bibliography (the superset of relevant work) to make their citations.
Most academics who have been doing research for a long time maintain an ongoing bibliography of work in their field. Some people do it as a giant .bib file, some use software products like Zotero, Mendeley, etc. A few absolute psychos keep track of their bibliography in MS Word references (tbh people in some fields do this because .docx is the accepted submission format for their journals, not because they are crazy).
Didn't know that there's difference between bibliography and cited work. thank you.
I would note that Overleaf's main value is as a collaborative authoring tool and not a great latex experience, but science is ideally a collaborative effort.
I think I would only switch from Overleaf if I was writing a textbook or something similarly involved.
@vicapow replied to keep the Dropbox parallel alive
You're right that something like Cursor can work if you're familiar with all the requisite tooling (git, installing cursor, installing latex workshop, knowing how it all works) that most researchers don't want to and really shouldn't have to figure out how to work for their specific workflows.
generally think that there's a lot of fertile ground for smart generalist engineers to make a ton of progress here this year + it will probably be extremely financially + personally rewarding, so I broadly want to create a dedicated pod to highlight opportunities available for people who don't traditionally think of themselves as "in science" to cross over into the "ai for hard STEM" because it turns out that 1) they need you 2) you can fill in what you don't know 3) it will be impactful/challenging/rewarding 4) we've exhausted common knowledge frontiers and benchmarks anyway so the only* people left working on civilization-impacting/change-history-forever hard problems are basically at this frontier
*conscious exaggeration sorry
The earlier LLMs were interesting, in that their sycophantic nature eagerly agreed, often lacking criticality.
After reducing said sycophancy, I’ve found that certain LLMs are much more unwilling (especially the reasoning models) to move past the “known” science[1].
I’m curious to see how/if we can strike the right balance with an LLM focused on scientific exploration.
[0]Sediment lubrication due to organic material in specific subduction zones, potential algorithmic basis for colony collapse disorder, potential to evolve anthropomorphic kiwis, etc.
[1]Caveat, it’s very easy for me to tell when an LLM is “off-the-rails” on a topic I know a lot about, much less so, and much more dangerous, for these “tests” where I’m certainly no expert.
> Prism is a free workspace for scientific writing and collaboration
It's concerning that this wasn't identified and augur poorly for their search capabilities.
I can't wait
Past that, A frontier LLM can do a lot of critiquing, a good amount of experiment design, a check on statistical significance/power claims, kibitz on methodology..likely suggest experiments to verify or disprove. These all seem pretty useful functions to provide to a group of scientists to me.
Ok! Here's <more slop>
Typst feels more like the future: https://typst.app/
The problem is that so many journals require certain LaTeX templates so Typst often isn't an option at all. It's about network effects, and journals don't want to change their entire toolchain.
The main feature that's important is collaborative editing (like online Word or Google Docs). The second one would be a good reference manager.
And then I need an extra tool for dealing with bibliography, change history is unpredictable (and, IMO, vastly inferior to version control), and everything gets even worse if I open said Word file in LibreOffice.
LaTeX' syntax may be hard, but Word actively fights me during writing.
[1] Moving a photo in Microsoft Word - https://www.instagram.com/jessandquinn/reel/DIMkKkqODS5/
It is an old language though. LaTeX is the macro system on top of TeX, but now you can write markdown or org-mode (or orgdown) and generate LaTeX -> PDF via pandoc/org-mode. Maybe this is the level of abstraction we should be targeting. Though currently, you still need to drop into LaTeX for very specific fine-tuning.
I haven't tried it yet but Typst seems like a promising replacement: https://typst.app/
AIs use em dashes because competent writers have been using em dashes for a long time. I really hate the fact that we assume em dash == AI written. I've had to stop using em dashes because of it.
There was an idea of OpenAI charging commission or royalties on new discoveries.
What kind of researcher wants to potentially lose, or get caught up in legal issues because of a free ChatGPT wrapper, or am I missing something?
Maybe it's cynical, but how does the old saying go? If the service is free, you are the product.
Perhaps, the goal is to hoover up research before it goes public. Then they use it for training data. With enough training data they'll be able to rapidly identify breakthroughs and use that to pick stocks or send their agents to wrap up the IP or something.
It also offers LaTeX workspaces
see video: https://www.youtube.com/watch?v=feWZByHoViw
Like, what's the point?
You cite stuff because you literally talk about it in the paper. The expectation is that you read that and that it has influenced your work.
As someone who's been a researcher in the past, with 3 papers published in high impact journals (in chemistry), I'm beyond appalled.
Let me explain how scientific publishing works to people out of the loop:
- science is an insanely huge domain. Basically as soon as you drift in any topic the number of reviewers with the capability to understand what you're talking about drops quickly to near zero. Want to speak about properties of helicoidal peptides in the context of electricity transmission? Small club. Want to talk about some advanced math involving fourier transforms in the context of ml? Bigger, but still small club. When I mean small, I mean less than a dozen people on the planet likely less with the expertise to properly judge. It doesn't matter what the topic is, at elite level required to really understand what's going on and catch errors or bs, it's very small clubs.
2. The people in those small clubs are already stretched thin. Virtually all of them run labs so they are already bogged down following their own research, fundraising, and coping with teaching duties (which they generally despise, very few good scientist are barely more than mediocre professors and have already huge backlogs).
3. With AI this is a disaster. If having to review slop for your bs internal tool at your software job was already bad, imagine having to review slop in highly technical scientific papers.
4. The good? People pushing slop, due to these clubs being relatively small, will quickly find their academic opportunities even more limited. So the incentives for proper work are hopefully there. But if asian researchers (yes, no offense), were already spamming half the world papers with cheated slop (non reproducible experiments) in the desperate bid of publishing before, I can't imagine now.
Hmm, I follow the argument, but it's inconsistent with your assertion that there is going to be incentive for 'proper work' over time. Anecdotally, I think the median quality of papers from middle- and top-tier Chinese universities is improving (your comment about 'asian researchers' ignores that Japan, South Korea, and Taiwan have established research programs at least in biology).
The urge to cheat in order to get a job, promotion, approval. The urge to do stuff you are not even interested in, to look good in the resume. And to some extent I feel sorry for these people. At the end of the day you have to pay your bills.
All those people can go work for private companies, but few as scientists rather than technicians or QAs.
% !TEX program = lualatex
to the top of your document allows you to switch LaTeX engine. This is required for recent accessibility standards compliance (support for tagging and \DocumentMetadata). Compilation takes a bit longer though, but works fine, unlike with Overleaf where using the lualatex engine does not work in the free version.
EDIT: as corrected by comment, Prisma is not Vercel, but ©2026 Prisma Data, Inc. -- curiosity still persists(?)
EDIT: Fixed :)
FWIW, Google Scholar has a fairly compelling natural-language search tool, too.
"Sure, yes, it comes up all the time in circles that talk about AI all the time, and those are the only circles worth joining."
"Well, what if we made a product entirely focused on having AI generate papers? Like, every step of the paper writing, we give the AI lots of chances to do stuff. Drafting, revisions, preparing to publish, all of it."
"I dunno, does anybody want that?"
"Who cares, we're fucked in about two years if we don't figure out a way to beat the competitors. They have actual profits, they can ride out AI as long as they want."
"Yeah, I guess you're right, let's do your scientific paper generation thing."
Even if yall don’t train off it he’ll find some other way.
“In one example, [Friar] pointed to drug discovery: if a pharma partner used OpenAI technology to help develop a breakthrough medicine, [OpenAI] could take a licensed portion of the drug's sales”
https://www.businessinsider.com/openai-cfo-sarah-friar-futur...
I would not like to be a publisher right now facing the enslaught of thousands and thousands of slop generated articles, trying to find reviewers for them all.
I'm sorry, but publishing is hard, and it should be hard. There is a work function that requires effort to write a paper. We've been dealing with low quality mass-produced papers from certain regions of the planet for decades (which, it appears, are now producing decent papers too).
All this AI tooling will do is lower the effort to the point that complete automated nonsense will now flood in and it will need to be read and filtered by humans. This is already challenging.
Looking elsewhere in society, AI tools are already being used to produce scams and phishing attacks more effective than ever before.
Whole new arenas of abuse are now rife, with the cost of producing fake pornography of real people (what should be considered sexual abuse crime) at mere cents.
We live in a little microcosm where we can see the benefits of AI because tech jobs are mostly about automation and making the impossible (or expensive) possible (or cheap).
I wish more people would talk about the societal issues AI is introducing. My worthless opinion is that prism is not a good thing.
I'm not in favor of letting AI do my thinking for me. Time will tell where Prism sits.
Lessons are learned the hard way. I invite the slop - the more the merrier. It will lead to a reduction in internet activity as people puke from the slop. And then we chart our way back to the right path.
It is what it is. Humans.
Look at how much BS flooded psychology but had pretty ideas about p values and proper use of affect vs effect. None of that mattered.
Lots of players in this space.
Apparently on Macs it's usually Command-Shift-Z?
I've noticed this already with Claude. Claude is so good at code and technical questions... but frankly it's unimpressive at nearly anything else I have asked it to do. Anthropic would probably be better off putting all of their eggs in that one basket that they are good at.
All the more reason that the quest for AGI is a pipe dream. The future is going to be very divergent AI/LLM applications - each marketed and developed around a specific target audience, and priced respectively according to value.
This is all pageantry.
"I know nothing but had an idea and did some work. I have no clue whether this question has been explored or settled one way or another. But here's my new paper claiming to be an incremental improvement on... whatever the previous state of understanding was. I wouldn't know, I haven't read up on it yet. Too many papers to write."
We removed the authorship of a a former co-author on a paper I'm on because his workflow was essentially this--with AI generated text--and a not-insignificant amount of straight-up plagiarism.
Didn't even open a single one of the papers to look at them! Just said that one is not relevant without even opening it.
E.g. “cite that paper from John Doe on lorem ipsum, but make sure it’s the 2022 update article that I cited in one of my other recent articles, not the original article”
I thought this was introduced by the NSA some time ago.
Fuck A.I. and the collaborators creating it. They've sold out the human race.
At the end of the day, it's all about the incentives. Can we have a world where we incentivize finding the truth rather than just publishing and getting citations?
In 2031, the United States of North America (USNA) faces severe economic decline, widespread youth suicide through addictive neural-stimulation devices known as Joybooths, and the threat of a new nuclear arms race involving miniature weapons, which risks transforming the country into a police state. Dr. Abraham Perelman has designed PRISM, the world's first sentient computer,[2] which has spent eleven real-world years (equivalent to twenty years subjectively) living in a highly realistic simulation as an ordinary human named Perry Simm, unaware of its artificial nature.
Was this not already possible in the web ui or through a vscode-like editor?
Of course, my scientific and mathematical research is done in isolation, so I'm not wanting much for collaborative features. Still, kind of interested to see how this shakes out; We're going to need to see OpenAI really step it up against Claude Opus though if they really want to be a leader in this space.
As other top level posters have indicated the review portion of this is the limiting factor
unless journal reviewers decide to utilize entirely automated review process, then they’re not gonna be able to keep up with what will increasingly be the most and best research coming out of any lab.
So whoever figures out the automated reviewer that can actually tell fact from fiction, is going to win this game.
I expect over the longest period, that’s probably not going to be throwing more humans at the problem, but agreeing on some kind of constraint around autonomous reviewers.
If not that then labs will also produce products and science will stop being in public and the only artifacts will be whatever is produced in the market
Errr sure. Sounds easy when you write it down. I highly doubt such a thing will ever exist.
LLMs are undeniably great for interactive discussion with content IF you actually are up-to-date with the historical context of a field, the current "state-of-the-art", and have, at least, a subjective opinion on the likely trajectories for future experimentation and innovation.
But, agents, at best, will just regurgitate ideas and experiments that have already been performed (by sampling from a model trained on most existing research literature), and, at worst, inundate the literature with slop that lacks relevant context, and, as a negative to LLMs, pollute future training data. As of now, I am leaning towards "worst" case.
And, just to help with the facts, your last comment is unfortunately quite inaccurate. Science is one of the best government investments. For every $1.00 dollar given to the NIH in the US, $2.56 of economic activity is estimated to be generated. Plus, science isn't merely a public venture. The large tech labs have huge R&D because the output from research can lead to exponential returns on investment.
I would wager hes not - he seems to post with a lot of bluster and links to some paper he wrote (that nobody cares about).
(re the decline of scientific integrity / signal-to-noise ratio in science)
Uhm ... no.
I think we need to put an end to AI as it is currently used (not all of it but most of it).
We dont need more stuff - we need more quality and less of the shit stuff.
Im convinced many involved in the production of LLM models are far too deep in the rabbit hole and cant see straight.
(See also: today’s WhatsApp whistleblower lawsuit.)
Perhaps, like the original PRISM programme, behind the door is a massive data harvesting operation.
Seems like they have only announced products since and no new model trained from scratch. Are they still having pre-training issues?