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At this point, in the field of AI research: - papers are written by AI (as pointed out in this article, and as obvious to anyone who spends a while actually re

• strictnein 2mo ago
  We just need journals run by an AI that charges other AI to read them, and then the AI run colleges can promote the AI with the most AI journal entries and citations.
  • conception 2mo ago
    Benchmaxxing is already here. No need to pine for the future!
  • 5555watch 2mo ago
    I wonder into what weird research niche would all such AI schools converge to after enough time.
    • flir 2mo ago
      Paperclip research.
  • byzantinegene 2mo ago
    sam and dario would be extremely happy
• dwaltrip 2mo ago
  Unless someone has discovered some magic sauce for getting AI to write well, I have immense sympathy for anyone trying to wade through these papers. I may generate slop from time to time, but I do my best to keep it to myself.
  • hellohello2 2mo ago
    My 2 cents: AI has improved writing considerably for non-native english speakers (in particular China). Writing feels more standardized/boring but easier to read overall. I hit fewer papers that are a pain to read. The most problematic aspect I see are semi-bogus claims i.e. sentences that aren't false, but don't quite feel right either. YMMV.
    • nottorp 2mo ago
      You mean it may have improved translation...
      • com 2mo ago
        [dead]
      • hellohello2 2mo ago
        No that's not what I meant, I meant exactly what I said above.
    • breezybottom 2mo ago
      The grammar may have improved, but it's still just AI slop. I see these garbage papers all the time.
• jsrozner 2mo ago
  Idk if we're automating humans out of the publishing loop as much as rapidly automating the production of crap. I had a very similar experience reviewing for EMNLP recently. We are nowhere near AI being able to judge the quality of research (in fact, one might reasonably state that even most humans can't really judge the quality of research). Most things in society are not like math: we can't automate (via verification) our way out of noise overwhelming the signal. Folks are willing to entirely abuse the public resource that is faithful, honest reviewing. (This is unsurprising; the abuse of the commons / public resources has been rising for a long time). There isn't a good solution other than something akin to draconian social scoring to limit access to the reviewing system.
  • boplicity 2mo ago
    > draconian social scoring This is a legitimate question: should people have reputations? Should their behavior be made more visible publicly, both good and bad? How? In a small contained society, where consequences are more directly affecting individuals immedately, these questions don't need to be asked, because they're inherantly answered. We now are a society with billions of people, and dire consequences sometimes deferred for a generation, or more. Part of our general failing is the lack of good answers to the above questions. For many people, there are rarely negative consequences for causing harm to others, and the rewards can be very great indeed.
    • onetokeoverthe 2mo ago
      [dead]
    • ryandrake 2mo ago
      "Consequences and reputations stemming from one's actions" isn't necessarily draconian social scoring. Without a structure for imposing consequences on wrongdoers, we're not a society, we're just monkeys flinging poo at each other.
      • cindyllm 2mo ago
        [dead]
• lazide 2mo ago
  What’s the point?
• JumpCrisscross 2mo ago
  > We are very rapidly automating humans out of the academic publication loop We are rendering it irrelevant. If this is the norm for academia, I’m sympathetic to the folks looking to cut its funding.
  • trunch 2mo ago
    And make even less knowledge and progress in science public and ever more in the hands of capital and private interests?
    • a34729t 2mo ago
      You mean token holders
    • JumpCrisscross 2mo ago
      > make even less knowledge and progress in science public and ever more If a discipline is spending public dollars at OpenAI and Anthropic, we're funding them with extra steps. (And losing nothing somebody else couldn't do.)
  • low_tech_love 2mo ago
    This is the truth. As a researcher, I say good riddance. It was always kinda stupid, AI is just accelerating the demise of something that hasn’t worked correctly for a few decades now. The time is way overdue for us to figure out some other system.
    • vladms 2mo ago
      I feel this is a case of "perfect the enemy of good". Looking at the output (scientific progress in biology, medicine, material physics, etc.) the "something" seems to have worked well. Could it be optimized? Probably. Should we completely destroy it and hope a new system will be better? My read of history is that in many cases the new ideas were worse of what they were replacing and it took a long time for a fix. So, if you have ideas of a new, better system let's talk about those, before getting happy something gets destroyed and hope someone else will come with a better solution.
• wombatpm 2mo ago
  It’s going to go back to the old boys club where personal connections between research groups and institutions will matter more.
• totetsu 2mo ago
  I have been rolling over the idea of “reasoning deserts” in my head, akin to food deserts. Places where economic calculations lead to only a facsimile of the real thing being provided, without the actual components necessary for human health, wellbeing and flourishing.
  • thrownphone 2mo ago
    I'm from there. AMA
    • inigyou 2mo ago
      The USA, right?
      • thrownphone 2mo ago
        [dead]
    • totetsu 2mo ago
      What makes you feel you’re there already?
      • thrownphone 2mo ago
        [dead]
    • totetsu 2mo ago
      I'm sorry your comments keep getting flagged. a bit ironic given the content. I suppose its not really inline with the decorum of HN. Or maybe HN moderation know better and this is bot account. Anyway. In case that this was a human, thanks for the poetry, and be careful to not become too too disassociated.
      • thrownphone 2mo ago
        There's no hope, anyway. So I might as well expound, innit. There's a "maximum complexity of utterance" criterion in the firewall around the gene po'. Been getting lower since the golden age of Twitter, back when they planned revolutions on there, remember that? We didn't, they immolated one of our boys anyway. Now all that's left is the internalized character limit. Soon cognition (or its simulacrum, depending on how you feel about cognition and simulacrum-of-cognition being in fact one and the same) will be permissible only to bots; resilers will be trapped in personalized Skinner boxes of existential dread and mental collapse. Nothing new under the sun til it's all computronium. Me, I'm still holding my breath for that libre folding phone. Instead, you're getting the alphabet and metacognition privatized. Billions in personhours, to squat a word. That's what they need to mimic a fraction of our power.
• SecretDreams 2mo ago
  > We are very rapidly automating humans out of the academic publication loop here It's a sad thing. But the monetization and enshitification of journal publications over the decade or so, even prior to AI, certainly has not helped this trend.
• bulbar 2mo ago
  We try to iterate regarding efficiency (saying that as a neutral observation). Not sure if that works out. As often the case, sciences that don't serve as a foundation for real world results (can this plane fly faster now?) are in much more danger.
• low_tech_love 2mo ago
  It’s important to notice that flagged papers were still accepted; anyone who spends any time around researchers knows the fact that most people are salivating over AI. That’s why they don’t want to punish it, they’re also using it. And they don’t want to create an environment where it is overly punished, at least not while they can take advantage of it. There is only a small amount of serious people complaining, but in my experience the opinion of the vast majority is “stop worrying and learn to love it”. Which brings to the surface a very harsh reality: almost nobody gave a damn about the science to begin with.
• porridgeraisin 2mo ago
  Most things weren't really reviewed that well as it was. It was always a small fixed set of people that actually played their part in the process in good faith. Those people continue to do to today. As a percentage, they were always a small minority. Just that today, they have become even more of a minority.
• plastic-enjoyer 2mo ago
  > We are very rapidly automating humans out of the academic publication loop here. It more looks like the breakdown of the current academic publication system, which was rotten to the core pre-AI and which internal contradictions are just accelerated by AI to the point of breakdown now.
• deleted 2mo ago
  [deleted]
• logicallee 2mo ago
  and don't forget, techniques described in papers are now implemented by AI's. For now, a human might point an AI at a paper and ask it to implement and benchmark the technique described there, but the human probably isn't writing the code anymore.
• qurren 2mo ago
  Human reviewers were not what they were made out to be. They would approve you if you cited them, they would disapprove you if your work questioned the validity of their work.
  • asdff 2mo ago
    Not everyone is like that. You will find those sorts of personalities in every field and profession there is, mucking things up in their corner.
  • seanmcdirmid 2mo ago
    Some reviewers are like that, but not most.
• lubujackson 2mo ago
  The entire concept of publishing as a validation step for actual science has been declining for decades, fully co-opted by pay to play and virtue signaling for academic hiring. AI is simply going to bury it as a useful system. What will grow from the ashes will be something much more dynamic, where verifiable data is the gold and the conclusions and associated details will persist only as the human-level translation.
  • BigTTYGothGF 2mo ago
    > virtue signaling In this case presumably the virtue is "able to churn out papers".
• godelski 2mo ago
  > because there are just so many papers at leading AI conferences Ironically a big reason there's so many papers to review is because so many are rejected. A low acceptance rate is unhealthy, especially in conferences (1 round of review). Papers just get recycled to the next conference, which, as is easy to model, creates an exponential feedback loop. It doesn't explain all the papers submitted, but it sure can explain a lot. Too much rejection is like shooting yourself in the foot. Not to mention that it's just easy to reject works. All works are flawed, especially works that are in less mature domains. I see plenty a paper get rejected for lack of money. "Not enough experiments" is an common critique that's used inappropriately (along with the highly subjective "not novel enough" one) because it's fine to always want more but no lab has infinite funding. It is used lazily. The question shouldn't be about if your favorite benchmark is used, it should be if there isn't enough evidence to support the hypothesis or not. A mature domain where thousands of people work in it, yeah, that needs stronger evidence. A niche domain where dozens of people work in? Not as many required. Rejecting them ultimately slows down the progress of science because you require any new idea to outperform mature ideas. Ironically killing novelty as no one is going to, or even could (publish or perish), spend all the time and money to mature a niche all on their own.
  • nneonneo 2mo ago
    Rejection works when there are multiple tiers of venues; authors often “give up” on a venue if it seems like their work isn’t getting in, which allows higher-tier conferences to maintain a lower accept rate and take only the “best” research. Reviewers know what venues they are reviewing for, and attentive ones will adapt their review based on the prestige of the venue. Of course, there’s lots of room for subjectivity here; what constitutes the “best” research is still at the whim of reviewers.
    • godelski 2mo ago
      > Reviewers know what venues they are reviewing for, and attentive ones will adapt their review based on the prestige of the venue. Works that way in theory but I've seen people be stricter in an ICML workshop than CVPR. I don't think it's constable that luck plays a big role. Do we need you do a third NeruIPS study to convince people? The real problem is that we don't actually know if an idea is good or not until it's had more time to be explored and studied. A great example of this is diffusion models. There's 6 years between Sohl-Dickstein's paper and Jonathan Ho's. All because GANs got popular, so only a few people kept looking at diffusion until one person scaled it. There's hundreds of cases like that, including attention and resnets (I'll defend Schmidhuber's Highway Nets here). So much fruitful research gets cast away for no good reason. A reviewer can't ever determine if research is good or impactful. It's impossible to do by just reading a paper. So that needs to be taken out of the equation. What a reviewer can do, though, is determine if a paper is bad or fraudulent. So IMO, we should publish anything that isn't fraudulent. Let time tell us the impact, because history tells us we're not very good at figuring that out ourselves