[HN Gopher] AI intensifies fight against 'paper mills' that chur...
___________________________________________________________________
AI intensifies fight against 'paper mills' that churn out fake
research
Author : headalgorithm
Score : 146 points
Date : 2023-05-31 15:18 UTC (7 hours ago)
(HTM) web link (www.nature.com)
(TXT) w3m dump (www.nature.com)
| x3874 wrote:
| [flagged]
| naillo wrote:
| The problem is the people doing this. The AI doesn't have agency,
| it's just an autocomplete tool.
| burkaman wrote:
| That doesn't really seem relevant to the discussion. AI
| obviously makes it 1000x easier to generate realistic-looking
| fake papers, so it "intensifies" the issue as this article
| explains. It is newsworthy and discussion-worthy, and as you
| said AI doesn't have agency so we don't need to worry about
| hurting its feelings by referencing it in a headline.
| juve1996 wrote:
| The only reason it's a problem is that papers became the
| measure - more papers = more prestige. The measure will
| simply change because now it won't have a strong signal.
| burkaman wrote:
| That is not the only reason and it's not the most important
| reason. Papers are useful in the real world and people use
| them to learn stuff, and also reference them as evidence.
| If you're trying to learn about a topic and you have to
| wade through 90% bullshit AI papers when search for the
| topic, when you aren't experienced enough to immediately
| tell which papers are real, that's a problem. If malicious
| actors are able to spread disinformation that cites very
| real-looking research, or pitch journalists with very real-
| looking papers, that's a problem.
| juve1996 wrote:
| No, papers are not always useful. That isn't an inherent
| property. They _can_ be useful. It depends on the content
| of the paper.
|
| If you can't tell whether a paper is real or bullshit it
| doesn't matter if an AI or a human created it.
| Journalists will, heaven forbid, have to do real
| journalism instead of blindly trusting some piece of
| paper they found somewhere.
| burkaman wrote:
| > If you can't tell whether a paper is real or bullshit
| it doesn't matter if an AI or a human created it.
|
| That's true, but now it's incredibly easy to create a
| human-quality bullshit paper. That wasn't true a year
| ago. It doesn't matter who created it, but it does matter
| how easy it was to create.
|
| > Journalists will, heaven forbid, have to do real
| journalism instead of blindly trusting some piece of
| paper they found somewhere.
|
| That's true, but now it's incredibly easy to generate a
| real-looking piece of paper and send it to a journalist,
| prompting them to spend a day doing real journalism to
| verify it. That wasn't true a year ago.
| waboremo wrote:
| Right so what solutions do you have here to prevent people from
| using the autocomplete tool for harm?
| naillo wrote:
| Punish the people using it, disallow and shame people who are
| found to use it. The usual stuff when you want people to stop
| doing things. (Not legally ban the whole population from
| making use of it even in cases where it's productive.)
| nradov wrote:
| Legitimate journals (or groups of journals) might need to
| institute a throttling mechanism to limit the rate at which
| individual authors can submit papers. They might also need
| some kind of reputation tracking system so that reviewers can
| flag articles as low quality, and then future submissions by
| the same author will be treated as lower priority. Open
| access journals can raise author fees.
| EGreg wrote:
| Guns don't kill people. They have no agency. But they sure make
| it easier.
| noughtme wrote:
| Illegal guns kill people. Something like 80-90% of guns
| involved in homicide are illegally obtained/possessed. This
| is relevant because like banning guns, "banning" AI shouldn't
| be the focus. It seems like an AI detection arms race is
| inevitable.
| EGreg wrote:
| Wait a moment. If 80-90% of guns used to kill people are
| illegally obtained, that means the laws successfully
| generate a regime by which guns are obtained much more
| safely. Now all that remains is to enforce the laws and
| crack down on illegal gun sales (although in USA that isn't
| all since old guns already purchased illegally will
| continue to work for decades)
|
| It's like when people were saying that 80-90% of people
| dying in hospitals were unvaccinated. So vaccination helped
| right?
| goodpoint wrote:
| Then how comes a 10 year old cannot buy a gun or drive a
| car?
| joncrocks wrote:
| Apologies for taking the bait.
|
| If the number of legal firearms were reduced then would
| that not reduce the pool from which the illegal firearms
| can be obtained?
|
| Agreed it doesn't completely solve the issue, but could
| make it significantly less likely that one would encounter
| a firearm in general.
| omginternets wrote:
| Likely, yes. One of the practical difficulties in the US,
| however, is that the pool has historically been
| replenished by the trafficking of guns. So yes, reducing
| the pool of available guns is _prima facie_ sensible, but
| it takes a fair bit more than declaring guns illegal.
| Further complicating the issue is the fact that guns have
| a _very_ long service life. I have an FB Vis from 1942
| that still works flawlessly, despite having seen years of
| service in actual war. With ordinary care and the
| replacement of high-wear parts (the extractor, mostly,
| and maybe the barrel), I'd expect it to run at least
| another 100k rounds.
|
| The proposition to reduce gun violence by reducing the
| number of guns in circulation has to account for the guns
| already in circulation, and for the propensity of black
| markets to introduce new guns into circulation.
| Otherwise, the only thing achieved is the disarming of
| the groups of people with legitimate uses for firearms.
|
| It's not as simple as "guns are the problem", nor is it
| as simple as "more guns are the solution", I'm afraid.
| jfengel wrote:
| Illegal guns start as legal guns. With both AI and guns you
| need to be willing to think about the problem at every step
| in the chain, not just at the end.
| justrealist wrote:
| > Illegal guns start as legal guns.
|
| To be pedantic, there are very few actually _illegal
| guns_. Mostly we are talking about crimes committed by
| felons who have committed prior crimes are no longer
| legally allowed access to firearms.
|
| That same gun, if possessed by a non-felon, would be
| legal.
|
| I don't know if this is or isn't important to the
| analogy, but I do think it applies.
| goodpoint wrote:
| Companies making AI like ChatGPT have agency. They should not
| publish tools that generate false information.
| [deleted]
| omginternets wrote:
| Prediction: as a result of fake research and it's ilk (fake
| credentials, fake trade-journal pubs, fake reviews, etc.), we
| will observe a renewed interest in referrals and meatspace
| networking. It will become correspondingly difficult for
| outsiders to enter professional circles.
| godelski wrote:
| On the other hand, maybe we'll abandon peer review and return
| to a pre 1950's like style? Everyone in ML just uses arxiv
| because the field is moving so fast. All work is realistically
| peer reviewed once it is out there. We treat conferences (more
| important than journals) as very noisy signals. Unfortunately,
| we still use those as metrics for completing degrees or hiring
| people.
|
| The way I would try to make this better is just to post to
| OpenReview rather than arxiv or integrate the two. That way
| discussions can be had on the works, in the open, and public.
| jltsiren wrote:
| I don't think ML is a good example, because it's an outlier
| within an outlier.
|
| CS is already a weird field, because it leans so heavily on
| conference papers that see a single round of reviews. Journal
| papers with multiple rounds of reviews are more common in
| other STEM fields. Because of this difference, peer review in
| CS is more focused on accepting/rejecting the paper than on
| improving it.
|
| And then the CS model breaks down in ML, which is far too
| popular for its own good. There are too many people working
| on the same topics, writing too many papers and submitting
| them to too few conferences, which have too many people
| attending. CS conferences are supposed to be more like
| community meetings than formal conferences. Informal
| discussions are the main point, but that doesn't scale beyond
| a few hundred attendees.
| stubybubs wrote:
| It's easy to do this with anything in computer science as
| others can implement or recreate whatever is being discussed
| easily. Not so with a 6 month experiment with several groups
| of humans, or even a 2 week one with rats.
| godelski wrote:
| This is not always true. ML has reproducibility issues
| despite the status quo being open models. Even if we don't
| include proprietary datasets there are still issues.
| Compute is one issue, but we can even ignore that[0]. The
| Lottery Ticket[1,2] plays a big role, in that you can just
| get lucky. This really should have resulted in treating
| benchmarks as weaker indicators but see other comments
| about reviewing incentives. Another issue is that it is
| status quo to optimize hyperparameters on test data and
| this results in information leakage. While this won't
| affect results of running a checkpoint there are issues in
| reproducing the work. It also adds noise. Generative papers
| also have a large issue in not showing random uncurated
| samples, which introduces huge biases and we can argue this
| is a reproducibility issue as you can't reproduce the
| results show in the works. Anyone that's played with things
| like Stable Diffusion (or any generative model) will be
| familiar with this.
|
| There are more nuanced issues and things that require
| intimate domain knowledge to fully understand, but I wanted
| to push back at this comment because I see a lot of people
| just brush off reproducibility concerns by pointing to
| GitHub. While it helps, it definitely doesn't make
| reproducibility "easy" or concerns nonexistent.
|
| [0] Sometimes we can't though as scaling has more effects
| than obvious. e.g. GANs can't scale batch on a per GPU
| level but scaling by multiple GPUs/nodes does give an
| advantage in quality (not just training times). This is
| non-obvious and many things can play a role.
|
| [1] https://arxiv.org/abs/1803.03635
|
| [2] https://arxiv.org/abs/2109.08203
| omginternets wrote:
| I think peer review exists for reasons beyond building or
| affirming the reputation of a scholar. In particular, it's
| regarded as a filter for eliminating the very worst-quality
| research. And judging from the manuscripts that came across
| my desk when I was still in academia, I'd say it's performing
| this particular duty reasonably well. As such, the connection
| between AI fakery and peer-review isn't all that clear cut to
| me.
|
| I might expect the peer-review pipeline to become inundated
| with _prima facie_ credible manuscripts, which would
| overwhelm the reviewer's ability to process publications even
| more than is already the case. At this point, I'd expect
| scholarly reputation to become _even more_ important for
| getting published, and I'd expect to see an increase in
| signaling and other out-of-band communications (e.g. talking
| about your draft at a conference). Critically, I'd expect
| this to _generally_ avoid AI generated "studies", despite the
| obvious drawbacks.
|
| And moreover, I actually have serious reservations about the
| project of purging the scientific landscape of informal
| processes. While there are some obvious and notorious
| drawbacks to the way things currently work, I am also wary of
| top-down, "authoritarian high-modernist" (to borrow the turn
| of phrase from James C. Scott's _Seeing Like a State_ )
| projects in all their forms, especially in science. The
| production of scientific knowledge is not pure technae; it
| cannot be reduced to a well-behaved formula. A large part of
| it is a craft requiring a significant share of informal
| processes in order to be truly creative. But that is a
| separate topic, and indeed, one might argue that doing away
| with formal peer-review altogether would be a step in the
| direction of informality.
|
| So at the end of the day, I'm not convinced by any
| predictions that relate the effects of AI on peer-review. I
| think it could go either way :)
| godelski wrote:
| I mostly agree with your assessment of peer review, but
| feel that it's gone too far. It's decades of metric hacking
| going unresolved. My research area is ML and it is
| definitely far out of hand. When I was in physics I'd
| probably be closer to your statement. But right now we have
| measurements that conclude "reviewers are good at
| identifying bad papers, but bad at identifying good
| papers." Either I think we need to accept more works (as
| long as they are valid and useful) and/or reducing the
| value of a top tier venue (which right now is top or none).
| I think papers should get multiple rounds, as our goal is
| to improve works, and submissions should always be rolling.
| I think this can help, because I agree that peer review
| _should_ be about eliminating the worst, not identifying
| the best (far too difficult, and hundreds of years of
| evidence).
|
| I think one important thing that venues could do is host
| data and code/tools for works. Harddrive space is rather
| cheap now (especially with tax benefits and donations) and
| can only result in high value to the community. The other
| thing is having formats like OpenReview, where works can
| continuously be discussed in the open. With authors and
| others being able to defend/criticize/question works. But I
| think there should be some filter, even if low, and rules
| for control of quality.
|
| For the most part, I do actually think getting rid of venue
| based review would be a step in the right direction. I use
| these words because I like to encourage the idea that open
| publication still leads to peer reviewing. In ML a lot is
| done with arxiv + twitter, I just think we should better
| formalize this. It allows for a lot of freedom and for high
| speed. It has problems, but I don't see them as any worse
| than the current system (I see an overall decrease in
| problems). Good science requires risks and a lot of
| creativity. The modern word advantage is that we have
| higher numbers of researchers and monte carlo sampling in
| parallel helps optimize. You want to encourage tail end
| samples too to escape local minima. The history of science
| is a history of upsets. You don't get upsets by doing what
| everyone is doing. I agree that top down hierarchies
| discourage such thinking and are an overall net negative to
| science and advancing human knowledge. Creativity is
| critical.
|
| For predictions, I agree. I lean towards predicting
| disruption, but I'm not even certain of that. And
| disruption can go many different ways. I'm glad we're
| starting the discussions, but I think they need to be
| deeper and more honest.
| dsr_ wrote:
| It might be time for a Research Web of Trust.
| __MatrixMan__ wrote:
| The problem is letting this be a step that directs our attention
| in the first place:
|
| > platform X published this so it must be good
|
| It's a root-of-trust scheme, and those create high value targets
| which fail to corruption. Better would be:
|
| > human Y cited this, it's about Z, and you've configured Y to be
| trusted in domain Z
|
| Webs of trust require maintenance, which isn't convenient, but if
| roots of trust continue to degrade in trustworthiness, then that
| maintenance will eventually be a price worth paying.
| gtop3 wrote:
| The height of trust rot is Science publishing Woo Suk Hwang's
| 2005 stem cell research. This was one of the top scientific
| journals publishing research that appeared to be a nobel-prize
| track line of research that would have been a breakthrough in
| medical treatment. Instead it tainted a line of research.
|
| The research results were fraudulent, claiming a much higher
| success rate at generating a stem cell line than what was
| achieved. They lied about the number of stem cell lines
| generated, the number of oocytes used to generate the stem cell
| lines, and the number of donors the oocytes came from.
|
| As if bad data wasn't enough, they lied both to their donors,
| lied about their donors, and miscredited authors.
| __MatrixMan__ wrote:
| I wasn't aware of the this kind of thing in 2005. How do you
| think the trustworthiness of papers has been trending since
| then, generally speaking. It sounds like that was a bit of an
| outlier.
| jxramos wrote:
| There was an interesting claim I heard from Eric Weinstein
| regarding peer review that he characterized as a cancerous
| infiltration into all of science from some medical area? Aha,
| found it in my notes...
|
| """ 1:32:28 Eric: but let me jump in--peer review is a cancer
| from outer space. It came from the biomedical community, it
| invaded science. The old system, because I have to say this
| because many people who are now professional scientists have an
| idea that peer review has always been in our literature and it
| absolutely [mff __ ] has not.
|
| Bret: right
|
| Eric: Okay. It used to be that the editor of a journal took
| responsibility for the quality of the journal which is why we had
| things like nature crop up in the first place because they had
| courageous, knowledgeable, forward-thinking editors. And so I
| just want to be very clear because there is a mind virus out
| there that says peer review is the sine qua non of scientific
| excellence, yada, yada, yada, bs, bs, bs. And if you don't
| believe me go back and learn that this is a recent invasive
| problem in the sciences.
|
| Bret: recent invasive problem that has no justification for
| existing in light of the fact
|
| Eric: Well not only does it have no justification for existing...
| When Watson and Crick did the double helix and this is the
| cleanest example we have. The paper was agreed should not be sent
| out for review because anyone who was competent would understand
| immediately what its implications were. There are reasons that
| great work cannot be peer-reviewed. Furthermore you have entire
| fields that are existing now with electronic archives that are
| not peer reviewed. Peer review is not peer review, it sounds like
| peer review it is peer injunction. It is the ability for your
| peers to keep the world from learning about your work.
|
| Bret: keep the world from learning about your work.
|
| Eric: because peer review is what happens, real peer review is
| what happens after you've passed the bs thing called "peer
| review". """ https://youtu.be/JLb5hZLw44s?t=5608
|
| This is a fascinating bit of history to know about if true. I
| wonder if anyone has ever documented the evolution of its
| adoption across fields. This would be interesting too to see the
| HN community react to this characterization. It was the first
| time I ever heard of it from his wild interview with his brother.
| luckydata wrote:
| Andrew Huberman will remember this
| MengerSponge wrote:
| Spiderman pointing at spiderman.png
| godelski wrote:
| There's really only one solution to this. Luckily it is the same
| solution to a lot of other things. Unluckily it is the same
| solution that no one has sought to implement for decades.
| Reviewing takes a lot of nuance, care, and time. You're not going
| to get this when all the incentives for the reviewers are
| currently to reject works. You can only get a group to work on
| ethics alone when that group is small and accountable.
| Incentivize the reviewers to be high quality and worthwhile work.
| Incentivize the chairs to review the reviewers and force high
| quality reviews. An expert paying close attention to a work makes
| the papermill's job exponentially more difficult. It also just
| makes the review process actually useful in the first place, as
| it ensures authors get actual feedback.
| noobermin wrote:
| 1) You must therefore pay the reviewers. Probably a good
| starting point.
|
| 2) It is in fact, hard to publish things already in top
| journals especially if you're not established. Given the fact
| you also want the pool of reviewers to shrink, you're also
| making difficulty of publishing works even harder. Yes, the
| issue is paper mills, got it, but you're making it even harder
| for sincere scientists who aren't part of a mill in doing this.
|
| 3) The flood of work that will fall to the smaller, more
| accountable group will require culling, which of course, the
| top journals will cull the horde based on the biases they
| already have: pedigree, which will further calcify the issues
| in science and research already, or even intensify it.
| godelski wrote:
| 1) I mostly agree. There are at least other rewards that can
| also be offered, such as conference discounts. There's also
| the token system (pay tokens to submit works, receive tokens
| for reviewing). But some incentive structure needs to happen,
| agreed.
|
| > especially if you're not established
|
| This is commonly stated but I don't think many internalize
| what it means. I don't think a meritocracy can exist -- this
| indicator supports that -- and shows that there's strong
| factors influencing acceptance beyond the quality of work.
|
| 2) I do think we need to be very careful about how we sell
| the prestige of publications/venues. As fields get more
| popular and our metrics rely more on them (publish or perish)
| then this not only encourages paper mills, but cheating in
| general. As two simple examples, look at how ML publishes
| works where papers use proprietary datasets/models (reveals
| authors' lab and thus violates ethics), or how it is status
| quo to tune hyperparameters on test data results (information
| leakage). You're are a huge disadvantage if you don't cheat.
| We have a similar problem in schools and this is why students
| cheat. If it is hard to catch and bad actors aren't punished
| (high risk because high false positive) then you actively
| reward cheaters. This needs to be a serious conversation and
| I don't think we are having this (this is several domains,
| even outside academia).
|
| 3) There's a coupling effect here though, that is pretty
| destructive and can have bigger social ramifications. As the
| noise in venue publication increases, the trust in the venue
| decreases. But I think it increases first, as people are
| metric chasing. But I suspect it'll be like a rubber band,
| and snap back hard. The larger ramification is social trust
| around science, where only the large venues matter and not
| the small unknown journals. We already have a growing
| distrust as the "just asking questions" anti-science strategy
| has been growing, and we need to be pretty careful and think
| beyond a local (spatially and temporally) window.
| nologic01 wrote:
| This is No.2 in the list of existential AI risks [1]
|
| > A deluge of AI-generated misinformation and persuasive content
| could make society less-equipped to handle important challenges
| of our time.
|
| One way to think about it as significant chunks of information
| exchange turning into a _Market for Lemons_. Namely the
| information asymmetry between the producer of AI junk and the
| receiver of said junk means that the receiver cannot distinguish
| between a high-quality message (a "peach") and a zero (or
| negative) value "lemon". Then receivers are only willing to pay a
| fixed price for a message that averages the value of a "peach"
| and "lemon". Given the zero marginal cost of producing junk, this
| will mean that in the limit receivers will be willing to pay
| exactly _zero_. Information exchange is completely discredited.
|
| But is this really an "existential risk" or an opportunity to
| think deeply about human relations, trust and the meaning of
| exchange?
|
| Maybe the transactional, "a fool is born every second", buyer
| beware, caveat emptor society we have built was never fit-for-
| purpose in the first place?
|
| [1] https://www.safe.ai/ai-risk#Misinformation
|
| [2] https://en.wikipedia.org/wiki/The_Market_for_Lemons
| dmingod666 wrote:
| Not even a mention of the galactica model. I suspect the author
| probably isn't aware of its existence..
| SiempreViernes wrote:
| To be fair, it is stressful to keep track of all the faceplants
| the new saviours perform
| dmingod666 wrote:
| Fair enough, but this was a major 'event' in both tech and
| academic circles..
|
| Funny that Nature publishes an article about fake papers and
| fails to mention the one model specifically trained
| exclusively on white paper to write (fake?) white papers..
| dallasg3 wrote:
| I didn't know what the Galactica Model was.
|
| https://www.technologyreview.com/2022/11/18/1063487/meta-lar...
| mohave529 wrote:
| From my perspective as an outsider, I have always been amazed by
| the use of papers in academic research as a means of
| communicating findings to the wider world. I find it problematic
| that these papers are often formatted in a way that makes them
| highly unreadable, with two columns and compressed text. In my
| opinion, adopting more modern methods of publishing research
| could greatly enhance the overall quality of research by making
| papers more accessible and increasing the likelihood of them
| being read.
|
| Imagine a scenario where there is a standardized format for
| academic papers, where the conclusion is explicitly derived from
| specific data and accompanied by confidence intervals. This
| standardized schema would enable easier referencing of other
| papers and easy incorporation of additional data through features
| like autocomplete. Implementing such a system could potentially
| reverse the trend of academic papers that use excessive and
| unnecessary language to appear more intellectually rigorous, even
| when the actual information being conveyed is limited.
|
| By embracing these changes, we could create a more transparent
| and efficient research environment that promotes clearer
| communication and enhances the impact of academic findings.
| jltsiren wrote:
| Whatever you think academic research is, it's not always like
| that, and it's usually easy to find counterexamples. That's why
| the attempts to standardize the processes usually fail.
|
| There is not necessarily any data behind the paper. Even if
| there is data, the conclusions may not be about the data. Even
| if the conclusions are about the data, the paper may not use
| quantitative methods. Even if it uses quantitative methods,
| confidence intervals may not make sense. Even if confidence
| intervals do make sense, adding new data might not. And so on.
| ftxbro wrote:
| so the ai is making bad research papers honestly i don't know if
| i like that more or less than if they were somehow doing amazing
| research
| LadyCailin wrote:
| It feels like AI is exposing Kessler Syndrome for other areas -
| that is, some small scale amount of junk isn't a problem
| necessarily, but if you scale up that problem, it fundamentally
| changes the thing for everyone permanently. I guess the jury is
| still out whether it's net good or bad, but it feels like it's
| going to force us to confront some fundamental issues we have in
| society, which have perhaps always been there, but are now
| unavoidable, and will demand quick societal change. That almost
| never goes over well.
| lacker wrote:
| This seems like an extension of the replication crisis. In many
| fields, most published research is already bogus. The idea that
| peer review is enough to ensure that research is valid is perhaps
| not scaling well.
|
| It would be great to have things like open data sharing. At least
| in astronomy, which I'm somewhat familiar with, it doesn't seem
| like we're that close. Most scientists cannot even reproduce
| their own results. It's common to use things like manual Jupyter
| notebooks, unlabeled CSVs, and a bunch of disorganized data
| files, in a one-off process that a scientist manually summarizes
| to produce a paper.
|
| To me, in an ideal world each paper would sit in a GitHub
| repository, with an integration test that verifies the code
| actually produces the results used in the paper. That isn't
| really what academics prioritize, but perhaps things will move in
| this direction as more people realize that we have a replication
| crisis, and also as scientists tend to have more software
| engineering skills over time.
| jvanderbot wrote:
| The unfortunate truth is that academic lineage matters. In a
| world with decreasing SNR from paper content, the SNR from
| finding the paper on a reliable researcher's homepage massively
| increases (because sadly, people are added to papers without
| their permission in an effort to boost their own signal).
| RosanaAnaDana wrote:
| I think if the data is valid, open access, and reproducible, it
| shouldn't matter if the paper was written by AI.
| mike_hearn wrote:
| Unfortunately this assumes that the only thing which can go
| wrong is lack of reproducibility. Not so. I read a lot of
| public health papers during COVID and a staggering quantity
| (IMHO nearly all of them) should not have been published;
| many of them would have been reproducible despite that.
|
| Other things that can and do regularly appear in
| reproducible, peer reviewed papers:
|
| * Nonsensical methodologies
|
| * Logical fallacies
|
| * Mis-representation of their data
|
| * Incorrectly implemented software
|
| * Source datasets that are cherry-picked
|
| One might think that things like incorrectly implemented
| software would fall under the umbrella of irreproducible
| research, but that won't work. Some fields don't really
| recognize a distinction between model implementation and
| specification. The model _is_ the implementation and if a
| description was once published it 's quite possibly either
| too vague to implement, out of date, wrong, or all three.
| IIRC Prof Neil Ferguson's team actually rejected an attempted
| replication of one of their epidemic models on the basis that
| only they were qualified to use it! Pseudo-science like this
| goes unremarked in universities, only outsiders seem to care.
|
| Sometimes you get an honest academic who knows what they're
| supposed to do and actually does it, but there's no
| observable benefit to them from doing so because the ones who
| don't bother don't seem to suffer any consequences.
|
| tl;dr The biggest problem with the replication crisis is the
| framing of it as being about replication. What the world
| actually faces is a misleading research crisis. You can drive
| the replicability rate to 100% and you'll still find whole
| fields consisting of logical fallacies and misinformation.
| tga_d wrote:
| The article says this:
|
| > Kahn says that, although there will undoubtedly be positive
| uses of AI to support researchers writing papers, it will
| still be necessary to distinguish between legitimate papers
| written with AI and those that have been completely
| fabricated.
| habitzreuter wrote:
| (PhD student in STEM here) I think most people have the wrong
| idea about what peer review is. My advisor teaches us to treat
| it as a first check, but it is not a guarantee of correct
| results.
|
| Most of the time I spend on research is actually trying to
| understand the literature and reproducing their results. If I
| can't do it, it probably means I don't understand enough about
| the work I'm reading, but there is also the small chance that
| the published analysis is wrong, which already happened to me.
|
| EDIT: typo.
| BeetleB wrote:
| Peer review cannot catch if they performed the procedure
| correctly (e.g. in a lab), but it is there to check things
| like:
|
| - Validity of the experimental setup
|
| - Validity of the statistical analysis methodology
|
| - Validity of the conclusions
|
| Take a look at the items in this comment:[1]
|
| > Afflicted by studies with small sample sizes, tiny effects,
| invalid exploratory analyses
|
| All these can be caught with peer review.
|
| > together with an obsession for pursuing fashionable trends
| of dubious importance
|
| In contrast, this problem is _intensified_ with peer review.
|
| > In their quest for telling a compelling story, scientists
| too often sculpt data to fit their preferred theory of the
| world. Or they retrofit hypotheses to fit their data.
|
| Not the role of peer review to catch these, but making your
| data and analyses scripts open will allow these to be caught
| by anyone who wishes to. The problem we have with current
| publishing is that I could use this technique to get faulty
| results, publish in a prestigious journal, get a 100
| citations on my paper, and _one_ of those citations will be
| from the person who looked at my data and saw obvious biases
| in my selection (which cannot be caught by a mere peer
| review). That one citation pointing out how wrong I am is
| lost in the noise.
|
| We need a way to highlight that citation - merely making a
| simple graph of connections via references doesn't get us
| there.
|
| [1] https://news.ycombinator.com/item?id=36140540
| gowld wrote:
| Most people have the wrond idea about what publication is,
| too. For almost all papers, for almost all readers, it
| doesn't matter if results are correct. It's just part of the
| game of academia. If you need to build something based on a
| paper, and you need it to work correctly (so, outside of
| public policy or macroeconomics), you need to do the work
| yourself.
|
| But if you just want to cite the work and write your own
| paper on top of it, go ahead.
|
| Published science is like ChatGPT ;-)
| slt2021 wrote:
| scientific publication is like a JIRA ticket.
|
| to get visibility of your work you need to close JIRA
| tickets, the more the better, since people look at
| aggregate metrics.
|
| The more your JIRA tickets are quoted in company
| documentation the better.
|
| but JIRA ticket is not code, in the same way publication is
| not science
| caddemon wrote:
| It's never going to be a guarantee, but it's also implemented
| very shittily at present. There's a difference between what
| peer review is and what it should be. It makes sense to look
| at it realistically in day to day practice, but in discussion
| of systemic problems it should be viewed in a different
| light.
| matthewdgreen wrote:
| I love the idea. But do be aware that the high-quality software
| engineering like this will require has a cost: be prepared for
| basic science costs to scale appropriately (or for productivity
| to be reduced.) More to the point, not all science can be
| usefully verified by unit tests. Even machine-verified
| mathematical proofs are entirely dependent on the definitions
| being correct, and that requires expert human analysis.
| dndn1 wrote:
| My instinct about numbers (i.e. incl. statistics) is that they
| should be reproducible: on-demand, with results and workings
| easily subjected to interrogation.
|
| In reality in many fields - my experience is in finance, but
| also in science - calculations are scattered across different
| languages and systems. This adds friction to any process about
| reproducing, understanding, analysing numbers.
|
| This is some motivation for calculang, a language for
| calculations I develop. https://github.com/calculang/calculang
|
| For the HN crew it's an under-development(!) functional
| language with properties to permit flexible designs that can
| scale. It's for numbers and if you share the model alongside
| your numbers people can check them according to the model and
| see the workings.
|
| It goes well with visual number [dev/person]tools that I will
| release one of soon, and in the future watch for a browser
| extension for the workings behind numbers you are reading.
|
| Important, it won't address the raw data part of the problem,
| but where numbers following from that are concerned, it might
| get closer to that instinct.
| lumost wrote:
| The way academic research is setup is not currently conducive
| to effective research. Research must intrinsically be allowed
| to fail. If you incentivize success, then what happens when
| someone is 2-4 years in and their only skill is apparently
| worthless?
|
| Without Tenure or the ability for a scientist to accumulate
| their own savings, there is a strong incentive for most
| scientists to charitably interpret results/papers to maintain
| relevance.
| mschuster91 wrote:
| > Most scientists cannot even reproduce their own results. It's
| common to use things like manual Jupyter notebooks, unlabeled
| CSVs, and a bunch of disorganized data files, in a one-off
| process that a scientist manually summarizes to produce a
| paper.
|
| The only way to get _that_ under control is if universities had
| a career track of data scientists and programmers, basically a
| shared resource pool of specialists that all researchers could
| use. But most US universities seem to prefer to "invest" their
| endowments into professional athlete teams.
| ftxbro wrote:
| > "The idea that peer review is enough to ensure that research
| is valid is perhaps not scaling well."
|
| As others have said, this is not what peer review has ever been
| for, at all. It only checks for gross omissions and violations
| of form and syntax that are obvious to other scientists who are
| in adjacent fields (not even necessarily the same one). It's a
| relict from the times when publications were not target
| metrics. Peer reviewers never replicate the work. If you are a
| grad student tapped for peer review and you spend the time to
| replicate the work, you are ruining your career and your
| advisor will also be mad.
| Salgat wrote:
| It's ironic and deeply shameful when whitepapers side-step the
| most fundamental aspect of the scientific method:
| reproducibility.
| noobermin wrote:
| No offense, but astronomy is ripe for being rife with this sort
| of thing because the validity of astronomical research doesn't
| matter for the "real world" and can't really easily be tested.
| I feel like it could be better, but since you guys do not have
| any external pressures beyond yourselves (as academics), you
| can still publish however you feel like.
|
| A lot of the more applied fields cannot survive scrutiny.
| Either you have to produce something that leads to a product or
| something a PM will scrutinize (especially if you work for a
| national lab or the like, or for pharma, and so on). There, a
| lot of published research might be bogus, but it is nowhere
| near the majority.
| gtop3 wrote:
| Astronomy is fantastic for preventing this type of fraud.
|
| The data is incredibly open. Anyone can download datasets
| from telescopes, or build their own. Papers built on invalid
| data can be refuted by simply pointing telescopes at the
| location the paper is lying about. There are very few
| possible discoveries or theories in an astronomy journal that
| would immediately impact the wider culture.
|
| Compare astronomy to sociology, in which questionnaire
| results can be fabricated and are essentially unconformable
| without using other unconformable questionnaires. Compare
| astronomy to economics, which the articles can be used
| bolster political positions.
| mycologos wrote:
| It might help to expand on "bogus". Bogus has a few levels,
| going from "not good, but possible from a well-intentioned
| author trying to do the right thing" (low-level bogus) to
| "outright deception" (high-level bogus). Small sample sizes,
| statistical errors, and flawed (but honest) experiment design
| are all, I suggest, low-level bogus. Faking data and plagiarism
| are high-level bogus.
|
| I think peer review is capable of, eventually, mitigating low-
| level bogus. The quantitative standards in fields where low-
| level bogus is a problem (e.g., but definitely not only,
| medicine) are rising. Peer review is not a scalable solution to
| high-level bogus. Figuring out high-level bogus seems to be
| almost a full-time job [1]. You cannot expect this level of
| effort from researchers, especially if they are reviewing for
| free; I would even argue that it's easier to fake data than to
| figure out it's fake. It also requires more expertise to assess
| quality research than to write a low-level bogus paper and
| submit it. There's a mismatch here. There are not enough expert
| reviewers to handle all the low-level bogus papers.
|
| The solution therefore seems to require some kind of
| reputational component. There needs to be a cost to engaging in
| high-level bogus. But this is a hard problem. Do you ban any
| lead author of a paper with demonstrated high-level bogus?
| Publicize their names? Ban _any_ author of a paper with
| demonstrated high-level bogus? Throttle the submissions any one
| person can make to a conference /journal at a time? I don't
| know. But the current model will have to change.
|
| [1]
| https://www.ft.com/content/32440f74-7804-4637-a662-6cdc8f3fb...
| caddemon wrote:
| It's also possible to be functionally bogus while doing
| everything 100% by the book. If you control things super well
| you can inadvertently make your result so narrow that it's
| basically meaningless, at least with the way the scientific
| system functions today. If people worked together in a more
| concrete way to build on prior results this effect might not
| be so bad.
|
| One of my favorite examples is a mouse study that did not
| replicate between two genetically identical mouse populations
| raised in the same conditions run by the same lab. The
| difference was the supplier the two sets of mice originally
| came from, and the researchers were able to pin down the
| cause as differences in the gut microbiome between the two
| (and in fact one particular bacterial strain). That is an
| example of great research, but the vast majority of studies
| will never catch something like this before they publish
| because they will only use one mouse supplier as they keep
| things controlled while minimizing costs.
|
| Because designed replication studies are fairly rare and
| people often do not officially publish when they find things
| that don't replicate in biology, we are approaching
| interpretation/downstream use of these highly controlled
| studies in an extremely inefficient way.
|
| But that isn't really the fault of individual researchers.
| Technically they're applying the scientific method correctly
| to their niche problem. It's the definition of the problem
| coupled with how we combine results across groups that causes
| the inefficiencies. As problems of interest increase in
| complexity we can't define them on the scale of individual
| labs anymore, and for some reason we've addressed it by
| breaking them up into subproblems in this homogeneous way.
| Then we just assume that piecing together the little results
| will work great...
|
| Anyways, I agree there is also straight up fraud and
| blatantly bad practices on the level of individual papers,
| it's definitely a continuum. Sometimes such bad results slip
| into the mainstream of science or even have a huge impact on
| subsequent funding directions like with the fabricated
| amyloid beta paper. But I do suspect that for the most part
| the blatantly bad work stays on the fringes, and the largest
| negative impact on scientific productivity actually comes
| from a level of abstraction up.
| CobrastanJorji wrote:
| Imagine a world where all the raw data sits in a public
| repository next to a script to process the data which was
| reviewed as part of the publication process, which would
| accompany the paper and could be quickly and easily replicated
| with new data. Anyone could produce the graphs shown in the
| paper simply by downloading both and running them together.
| What a wonderful thing that would be. And you could imagine the
| government funding the storing and serving of all the data.
| What a wonderful daydream of an idea.
| jxramos wrote:
| seems attainable, doesn't sound far fetched technically.
| Maybe get a few universities to start the trend.
| venv wrote:
| Even this would require somehow verifying the raw data. It's
| plausible a bad actor could "reverse engineer" their data
| from a pre-determined conclusion.
|
| But yes, overall more openness is good. Still, the cost
| losing trust in society is very high (as you need to verify
| everything).
| ChainOfFools wrote:
| > It's plausible a bad actor could "reverse engineer" their
| data from a pre-determined conclusion.
|
| I've already heard of someone planning a product (
| initially targeted at lazy^H^H^H^Hbusy high schoolers and
| undergrads) that will use AI to reverse discover citations
| that fit a predetermined narrative in a research paper.
| Write whatever the hell you want, and the AI will do its
| best to backsolve a pile of citations that support your
| unsourced claims and arguments. The founder, and I use that
| term very generously, expects the academic community to net
| support this because it will boost citation counts for the
| vast majority of low citation, low visibility works.
| reprociboi wrote:
| Some journals in fact implement this idea (e.g. having the
| raw data underlying each figure one click away). That is
| however not the crux of the reproducibility crisis; it would
| be great if it was just "I can't make an exact copy of
| Supplementary Figure 5D", but rather "I can't confirm that
| protein X causes dementia using orthogonal techniques". There
| is no easy code fix for that problem.
| simonw wrote:
| Funding the storing and serving of all of that data doesn't
| sound like a difficult problem to me. That has gotten SO
| cheap over the past couple of decades. There are plenty of
| well funded institutions that can support that kind of
| resource.
| jrumbut wrote:
| You'd be surprised. One headache there is what did you tell
| study participants you would do with the data?
|
| Did you say you'd keep it forever? Did you say 5 years?
| Who's in charge of making sure that this centralized
| repository isn't inappropriately holding and distributing
| data?
|
| Funding organizations also have different requirements.
|
| Then a script is only useful if paired with a set of
| libraries of a particular version, a specific
| compiler/interpreter, an OS, also there may be specialized
| hardware involved. Some of the languages used in science
| like SAS, Stata, SPSS, and Matlab, etc aren't free and open
| source so you can't always just bundle it.
|
| And even data storage isn't trivial. For a recent small
| conference abstract I processed ~150GB of data. Hundreds
| (thousands? Tens of thousands?) of other papers have looked
| at that same data. You would really want some way to
| deduplicate that storage, but that introduces some
| additional complexity.
|
| I do like this vision but I think it would be a major
| undertaking that would require a lot of well funded
| institutions coming together rather than any one in
| particular doing it on their own.
| noobermin wrote:
| I hate to be sincere, but the reality is the data is our
| product. If we open source our data too much, we won't have
| anything left to publish as others who would have to expend
| nowhere near the resources we must do to produce it can
| scrape it and publish it. (that literally is how "AI" of the
| current hype bubble works today, lol, why would I want that
| online?)
|
| The incentives are definitely bad, and that's where the
| actual fix should be.
| diognesofsinope wrote:
| If any level of government funds your institution you
| should have to releases the data.
|
| The code I write is not my code, it's the banks.
| caddemon wrote:
| If that happened it would indirectly change the
| incentives anyway, because everyone would start being
| required to release data, so bad practices that are
| currently incentivized would become impossible.
|
| However I think it would be better to directly
| incentivize data release rather than require it, at least
| in biomedical sciences. Because of patient privacy issues
| there is no way raw data release can be required across
| the board. And I certainly do not trust the NIH to come
| up with a coherent set of rules for when it is versus
| isn't allowed, which would mean loopholes and more
| corruption.
| ChainOfFools wrote:
| Most projects are funded by a patchwork of different
| sources, not all of which would agree to the same terms
| of release of the information, but all of which are
| required sufficiently fund its creation. Not to mention
| maintaining ongoing storage and accessibility. single
| source government grants for scientific research are the
| exception, not the rule.
| [deleted]
| JacobThreeThree wrote:
| The current scientific system has long been known to have
| serious problems of incorrect results and conflicts of
| interest. This article seems like an attempt to pin the crisis
| on an AI scapegoat.
|
| From 2015, the Editor of The Lancet:
|
| _The case against science is straightforward: much of the
| scientific literature, perhaps half, may simply be untrue.
| Afflicted by studies with small sample sizes, tiny effects,
| invalid exploratory analyses, and flagrant conflicts of
| interest, together with an obsession for pursuing fashionable
| trends of dubious importance, science has taken a turn towards
| darkness. As one participant put it, "poor methods get
| results". The Academy of Medical Sciences, Medical Research
| Council, and Biotechnology and Biological Sciences Research
| Council have now put their reputational weight behind an
| investigation into these questionable research practices. The
| apparent endemicity of bad research behaviour is alarming. In
| their quest for telling a compelling story, scientists too
| often sculpt data to fit their preferred theory of the world.
| Or they retrofit hypotheses to fit their data. Journal editors
| deserve their fair share of criticism too. We aid and abet the
| worst behaviours. Our acquiescence to the impact factor fuels
| an unhealthy competition to win a place in a select few
| journals. Our love of "significance" pollutes the literature
| with many a statistical fairy-tale. We reject important
| confirmations. Journals are not the only miscreants.
| Universities are in a perpetual struggle for money and talent,
| endpoints that foster reductive metrics, such as high-impact
| publication. National assessment procedures, such as the
| Research Excellence Framework, incentivise bad practices. And
| individual scientists, including their most senior leaders, do
| little to alter a research culture that occasionally veers
| close to misconduct._
|
| https://www.thelancet.com/journals/lancet/article/PIIS0140-6...
| tga_d wrote:
| How do you see this as scapegoating? The headline
| specifically says "intensifies", the article very clearly
| positions AI-fabricated data as an extension of existing
| problems, and I don't see anything in the article downplaying
| those existing problems (the entire closing section is about
| how the summit was on issues broader than AI).
| mike_hearn wrote:
| Nature's reporting on the problem of paper mills is
| surprisingly high quality and honest, given that these
| reports directly attack the credibility of Nature itself
| (and many other journals).
| huijzer wrote:
| Instead of fighting against "paper mills", let's fight against
| journals. There are strong arguments against the need for peer-
| review for example [1]. Science does not get worse when there are
| more bad papers, science gets better when there are more good
| papers. Most papers in AI aren't even reviewed by peers or editor
| and guess what: we have lots of progress happening. I'm not
| saying that is because the lack of review, I'm saying that
| reviews are not necessary for progress. Furthermore, was the
| iPhone good because it was reviewed by a board of "independent"
| reviewers? No it wasn't. Let's just ignore Nature. Good papers
| are good because they have good arguments and if they are not
| good, then time will tell. Papers are not good simply because
| Nature (TM) put a stamp on it.
|
| [1]: https://www.experimental-history.com/p/science-is-a-
| strong-l...
| noobermin wrote:
| Let's be real, there are a lot of papers in AI right now
| because it's been hot, not just recently but for years. Still,
| no one is reading random papers from _insert-unknown-group-
| here_. People read papers from already established scientists
| who have pedigree. And, how did they become established?
| Because they actually published before arxiv.
|
| In that sense, peer review should be an equalizer, because the
| only way to be read and cited then is to have the holy academic
| lineage, which is what would happen if we abolished peer-review
| as it exists in the current academic word we're in.
| artursapek wrote:
| I forget which episode, but Andrew Huberman discussed somewhere
| how the incentives behind getting published in science journals
| and the peer review process often lead to poor studies.
| ethanbond wrote:
| The cases you mentioned have clear and immediate commercial
| incentives that push them toward quality. Not quite so for
| basic research that might (optimistically) be decades away from
| commercialization and will probably be commercialized from
| someone very very far away in the value chain from the original
| researcher.
|
| FWIW I'm also really critical of peer review/journals, just
| don't think this is a great line of analogy.
| SQueeeeeL wrote:
| I've found most people who are deeply critical of peer review
| as a process (and not just how shady modern journals are)
| have never engaged in serious scientific work. They have no
| idea how lost in the weeds researchers can get, and how
| useful having a somewhat uninformed 3rd party see if they can
| parse your prose is for having a work be understandable.
| Commercialization really only applies to a very small subset
| of scientific discoveries
| CobrastanJorji wrote:
| No, science definitely goes worse when there are more bad
| papers.
|
| There's a popular argument about freedom of speech that comes
| down to "the solution to bad speech is more good speech, not
| limiting bad speech," but that only works (assuming it's true
| at all) when there's some way for a listener to discern truth
| with some research or effort.
|
| > "Good papers are good because they have good arguments"
|
| No they are not. Good papers are good when they collected data
| correctly and then presented the results fairly. Arguments
| about what that data means hardly enters into it. You can't
| argue whether the data is correct or not without gathering it
| yourself, and you can't gather it yourself.
| huijzer wrote:
| > There's a popular argument about freedom of speech that
| comes down to "the solution to bad speech is more good
| speech, not limiting bad speech," but that only works
| (assuming it's true at all) when there's some way for a
| listener to discern truth with some research or effort.
|
| Yes exactly. If it is too hard for a group of scientists to
| figure out a flaw, then it is also too hard for two peer
| reviewers to figure out a flaw. Only time can detect all
| errors in science, assuming that nonconformist papers are
| allowed into the conversation.
|
| > Good papers are good when they collected data correctly and
| then presented the results fairly.
|
| This is only true for empirical papers. Good data is a part
| of a good argument in the case of empirical papers.
| notahacker wrote:
| > If it is too hard for a group of scientists to figure out
| a flaw, then it is also too hard for two peer reviewers to
| figure out a flaw.
|
| The issue isn't that flawed papers are necessarily hard to
| spot, the issue is that indiscriminate publishing means
| that anyone studying a topic has to wade through a lot of
| flawed ones before they find anything halfway useful. Time
| and attention are finite. Peer review and journals caring
| about reputation, in theory, caps the number of readers of
| rubbish papers at 2 (and disincentivises writing
| unpublishable crap to submit in the first place... although
| this is now eroded since generating rubbish papers is now
| effort-free)
| cortesoft wrote:
| Yeah, the problem with that "more speech" argument is the
| imbalance... lies are way easier and faster to generate than
| truth, and can be crafted perfectly to play on what the
| audience wants to hear. The truth has to be the truth, so it
| can't be crafted in the same way.
| stale2002 wrote:
| > You can't argue whether the data is correct or not without
| gathering it yourself, and you can't gather it yourself.
|
| You actually can argue about the data being "correct".
|
| The way to do is, is studies need to be much more radically
| open with their raw data sources.
|
| In AI, that would mean actually releasing the code you used,
| so people can try it themselves.
|
| Or, in more sociology stuff, where you are doing, I don't
| know, interviews with humans, you could release the physical
| videos of your interviews.
|
| And then for science stuff, show pictures/vidoes of the
| science you are doing.
|
| I'm sure there would still be way to hack this. But
| significant more code and data transparency would do a huge
| amount for allowing other people to replicate or verify your
| work.
| eldaisfish wrote:
| This line of thinking is dangerous and ignores very real
| problems as the other comments point out.
|
| Instead of targeting peer-review, i recommend approaching this
| problem as one of incentives. Under the current system, what is
| the incentive for a reviewer to read and critique a paper?
| None. If they were paid in cash, there is a clear incentive.
| The publishers do not want this overheard and the associated
| legal requirements so they seek out "volunteers" and
| "compensate" them with rubbish like book copies and
| "reputation". The fault is with publishers, not the peer-review
| process.
|
| At the moment, the current model allows parasites like Elsevier
| to derive the maximum benefit while not paying most folks
| involved in creating the actual value - the reviewers, editors
| and authors.
|
| Citizen journalism tried this, failed and is now hollowing out
| established journalism. We need proper funding and proper
| incentives for journalism just as we need proper incentives for
| academic research.
| jltsiren wrote:
| The incentives of paid peer review would not be easy to get
| right. Especially if you want the compensation to be high
| enough that it would actually be an incentive to the
| reviewer.
|
| Many universities limit the number of hours their faculty can
| commit to paid external activities. Paid peer review would be
| one of those activities, and it would have to compete against
| other activities. Such as consulting, which can be very
| lucrative in some fields.
|
| So maybe you have to pay $10k peer review fee when you submit
| a paper. And then it gets rejected after the first round of
| reviews, because you aimed too high or the editor and the
| reviewers just didn't like the topic. You resubmit to another
| journal and pay another $10k. After a couple of additional
| rounds of reviews ($5k each), the journal seems to be
| interested in publishing the paper. But reviewer 2 wants you
| to cite some of their papers that are not really that
| relevant. Do you agree, or do you argue against and risk
| another round of reviews (another $5k)? Or maybe you get the
| feeling that reviewer 3 is stalling the process with
| superficial requirements, as they get easy money from the
| reviews.
|
| Except that most academics can't afford that. American
| academics are massively overpaid by global standards, and
| peer review would naturally be outsourced to developing
| countries, where the expectations of pay are more reasonable.
| Many of the academics there are reputable, after all.
| Unfortunately the institutional culture is often more
| problematic, and corruption also tends to be more prevalent.
| Do we really want to let those institutions shape the
| practices of science worldwide?
|
| One of the unfortunate features of capitalism is that being a
| middleman is more profitable than doing the actual work.
| Instead of a system where you pay $x for reviews, we could
| end up with one where you pay $1.1x to a middleman, who then
| pays $0.8x for the reviews. The middlemen get even richer
| than in the current system, because there is more money in
| publishing than there used to be.
| eldaisfish wrote:
| This makes a good point but is largely a strawman. Review
| fees do not need to be this high. At $ 25 an hour, most
| engineering papers take about 2-5 hours to review. That's $
| 125 per reviewer or $ 500 for three reviewers, one editor
| and another $ 500 for publication fees. That's $ 1,000 in
| total and the number of reviewers can be fewer.
|
| At the moment, greed drives open access publication fees.
| Journals charge thousands of dollars because they can.
| There is no logical justification of the cost. There are
| several journals that charge hundreds of euros for review
| although they do not pay reviewers.
|
| Note also that not all review needs to be done by
| academics. folks in industry can contribute equally but
| their employer pays for their time so that boils down to
| altruism or business priorities. A reasonable payment for
| time creates an incentive for the world beyond academia and
| this is a net positive. The payment does not need to be
| prohibitive, just enough that the reviewer has an incentive
| to do a good job and isn't pressured to rush. The problem
| thus is not limited to the journals or the peer review
| process but inventives.
| jltsiren wrote:
| I was thinking more about something like $200 to
| $500/hour.
|
| A small payment like $200 can be more of a disincentive
| than an incentive. You have to deal with bureaucracy to
| get paid and more bureaucracy to pay taxes for it. And if
| the money comes from a foreign source, amount of the
| compliance bureaucracy can be absurd. Getting paid is
| simply not worth it, if the payment is too small.
| malikNF wrote:
| I understand how journals made sense during a time when
| distribution was a hard problem. Now with the internet I don't
| get the reason for them to exist in the form it does in 2023.
| probably_wrong wrote:
| A journal is a way of ensuring that the paper you are about
| to read meets a minimum standard of quality. My anecdote: the
| second worst paper I've ever read was an ArXiv paper,
| presented by a young researcher who had no idea of how bad
| this paper was, Had I not teared it down (which was a very
| unpleasant experience for everyone involved) it would have
| been further propagated by other young researchers. And the
| worst paper? Submitted to a conference, rejected outright
| during review, and I still wonder if someone was trying to
| sneak a machine-generated article for fun.
|
| No one has the time to keep up with all the research coming
| out, not to mention all the fake research published in bad
| faith. Life is not restricted to Elsevier and ArXiv, and
| there are sane alternatives out there.
| webnrrd2k wrote:
| Re: "Science does not get worse when there are more bad
| papers"...
|
| I don't think that this is true at all. Weeding through bad
| papers is, at a minimum, an opportunity cost, as is a good
| paper built on top of a bad one.
|
| Also, there is a societal cost in that bad research can get
| picked up and believed by people, like the anti-vax crowd. Or,
| bad research can be used to push an agenda, like anti-climate
| change.
| mschuster91 wrote:
| > Also, there is a societal cost in that bad research can get
| picked up and believed by people, like the anti-vax crowd.
| Or, bad research can be used to push an agenda, like anti-
| climate change.
|
| Responsible media would act as a gatekeeper. The problem is,
| most media utterly _gutted_ scientific journalists for more
| profit, so they completely lack the basis to evaluate and
| supply context on research. Others, particularly boulevard
| media, willfully ignore any kind of ethics for clicks.
|
| On top of that comes a general media illiteracy and media
| distrust that makes it even harder to combat because there's
| an awful lot of media that _thrives_ on intentionally pushing
| crap to people.
| SantalBlush wrote:
| You are correct. Many people want research to work like a
| social media site where everyone can contribute, believing
| this is an egalitarian--and therefore better--solution. In
| reality, it would slow research to a crawl, as noise and
| conflicted interests dominate the conversation and drown out
| more rigorous or valuable information.
| huijzer wrote:
| > noise and conflicted interests dominate the conversation
| and drown out more rigorous or valuable information.
|
| That's actually a great point. Who says this isn't already
| the case? There isn't much evidence to suggest that things
| have gotten better after the Why Most Published Research
| Findings Are False paper in 2005. I think people should be
| extremely skeptical of anything they read, irregardless of
| a peer review stamp.
| SantalBlush wrote:
| >I think people should be extremely skeptical of anything
| they read
|
| This is a mistake that many HN readers make; they think
| that if one is equipped with some above-average level of
| intelligence, one can discern the validity of new
| research. But this is wrong. It usually takes many years
| of study before one can begin to clearly understand what
| is even being said, let alone whether it has any
| veracity.
|
| People of above-average intelligence often resent this
| fact, because it suggests that their smart opinion isn't
| as valuable as the opinion of an expert, but that is
| simply a sad fact of life. If they haven't put in the
| work in that field, then they don't know what they're
| talking about in that field, and they are incapable of
| applying skepticism in that field correctly. This is
| precisely and unfortunately why we are forced to place
| our trust in experts.
| mike_hearn wrote:
| In practice that's not the case. Maybe for some fields it
| is, but there are definitely plenty of fields where
| minimal expertise is required to do basic sanity checks
| of a paper. And there are many generic 'tells' that are
| indicative of issues across a wide range of fields, e.g.
| if a paper is reporting mostly P = 0.049 results, well
| ...
|
| As an example of an issue that doesn't require much
| expertise to spot, I was reading a health paper a couple
| of days ago that reported on an intervention in a
| specific population. The paper said words to the effect
| of "[the intervention] was effective, especially for
| men". The associated chart not only had (somehow?!) got a
| legend that didn't match the actual chart lines, but
| whilst the line for men did indeed go down the line for
| women was very clearly flat! This should have been
| reported as "no effect in women but an effect in men" but
| wasn't. When the wording doesn't match the data being
| reported, that's a good sign in any field that the
| researchers know they're treading on thin ice. That
| particular claim was a correlation/causation fallacy
| anyway, which is _very_ common in health. They didn 't
| have any proof it was their intervention causing the
| reduction and there were a bunch of reasons to suspect it
| wasn't. But the intervention was long term and high
| effort so it's not a surprise they wanted to find
| something.
| SantalBlush wrote:
| This is a great example of what I'm talking about.
| P-values of 0.049 is not a "tell"; that is an HN meme.
| You can have p-values above 5 percent and the paper can
| still convey novel and valid research, depending on the
| nature of the study. The 5-percent value is a common
| cutoff, but it is a largely arbitrary one. Sometimes
| there is justification for a higher one, and sometimes it
| should be lower.
|
| To gauge what sort of p-value suggests a useful model
| requires familiarity with the area and the broader
| context of the research.
| mike_hearn wrote:
| That's the point - it's an arbitrary cutoff, so the high
| frequency with which results cluster around that point is
| indicative of a problem.
|
| If a field accepts P=0.05 as significant it means 1 in 20
| results can be false positives just by random chance. Now
| think about how many papers get published, and many of
| them report more than one thing. The right threshold
| should really be an order of magnitude lower. It's not OK
| for scientists to report FPs at that rate, and that's a
| big part of the reason for declining confidence in
| science.
| SantalBlush wrote:
| >If a field accepts P=0.05 as significant it means 1 in
| 20 results can be false positives just by random chance.
|
| That is not the correct interpretation of a p-value. See
| the ASA's statement on p-values.[0]
|
| >Researchers often wish to turn a p-value into a state-
| ment about the truth of a null hypothesis, or about the
| probability that random chance produced the observed
| data. The p-value is neither. It is a statement about
| data in relation to a specified hypothetical explanation,
| and is not a statement about the explanation itself.
|
| Moreover, while it's fine to suspect that p-hacking took
| place with p-values just under 5 percent, it is merely a
| suspicion and nothing more. Throwing out p-values you
| don't like without further evidence is a different sort
| of violation of the methodology; it is like p-hacking in
| the other direction.
|
| >The right threshold should really be an order of
| magnitude lower.
|
| This is false. Again, see the ASA's statement.[0]
|
| >Practices that reduce data analysis or scientific infer-
| ence to mechanical "bright-line" rules (such as "p <
| 0.05") for justifying scientific claims or conclusions
| can lead to erroneous beliefs and poor decision making. A
| conclusion does not immediately become "true" on one side
| of the divide and "false" on the other. Researchers
| should bring many contextual factors into play to derive
| scientific inferences, including the design of a study,
| the quality of the measurements, the external evidence
| for the phenomenon under study, and the validity of
| assumptions that underlie the data analysis. Pragmatic
| considerations often require binary, "yes-no" decisions,
| but this does not mean that p-values alone can ensure
| that a decision is correct or incorrect. The widespread
| use of "statistical significance" (generally interpreted
| as "p & 0.05") as a license for making a claim of a
| scientific finding (or implied truth) leads to
| considerable distor- tion of the scientific process.
|
| In short, these things often require a strong statistical
| literacy to interpret, which most people complaining
| about p-hacking do not possess.
|
| [0]https://amstat.tandfonline.com/doi/pdf/10.1080/0003130
| 5.2016...
| venv wrote:
| Maybe, but it would still be better if the situation
| didn't detoriate further.
| AnthonyMouse wrote:
| This depends heavily on the field.
|
| In many cases results can easily be independently verified.
| This is why it works for AI. If you publish a result, it
| should come with code anybody can run. If the code doesn't
| exist or doesn't do what you say it does, you're a fool and
| everyone can ignore you. If it does, you don't need
| anyone's stamp of approval to prove it.
|
| But that doesn't work with medical trials or things of that
| nature where independently verifying the claims is
| expensive.
| __MatrixMan__ wrote:
| > Weeding through bad papers
|
| How does one come across these bad papers in the first place,
| such that they must be weeded through? Maybe we need a
| different method of curation such that they stay below the
| noise floor.
| AnthonyMouse wrote:
| The solution to this is reputation. The flaw is that we're
| using the reputation of for-profit journals rather than the
| research institutions.
|
| You shouldn't have confidence in a paper because it was
| published in Nature, you should have confidence in it because
| it was published by Harvard or the University of California
| or Google Research, who puts the name of their institution on
| it and thereby stakes their reputation. For the preeminent
| researchers in a field, their own names may be enough for
| people in that field to trust the result.
|
| You can still have a paper published by nobody, and if the
| results are interesting, researchers at known institutions
| can try to replicate it. Until then it's nothing. But if they
| do, the nobody gets cited and credited with being the first
| and takes a step to building their own reputation, and the
| known institution gets credited with knowing when to spend
| resources following up on something significant.
| notahacker wrote:
| The research institutes are also for profit bodies, just
| much more of an closed shop because there's only so many
| people they can employ at any one time (and that's before
| we even get into tenure or the fact all you're really doing
| is shifting the peer review burden onto people that report
| into the person that wrote the paper).
|
| You can have papers published by Google Research or by
| nobody now: turns out academics like the journals' attempts
| to curate interesting stuff more than only ever reading
| stuff on their bookmark list or that's mailed to them in a
| desperate attempt to get some engagement. And for all that
| they might not like the long delays and low chance of
| success associated with submitting to Nature, they like the
| idea of trying to persuade an academic at an elite
| institution to go to the trouble of replicating their
| research as the only means to build their reputation even
| less...
| AnthonyMouse wrote:
| > The research institutes are also for profit bodies
|
| Their business model isn't to put the research behind a
| paywall, which is the relevant distinction.
|
| > turns out academics like the journals' attempts to
| curate interesting stuff more than only ever reading
| stuff on their bookmark list or that's mailed to them in
| a desperate attempt to get some engagement.
|
| This is what conferences are for, and then you have the
| conference organizers curating the talks but everyone
| gets the list of talks and links to all the freely
| available papers even if they can't afford to attend the
| conference.
| YeGoblynQueenne wrote:
| >> Most papers in AI aren't even reviewed by peers or editor
| and guess what: we have lots of progress happening.
|
| If by progress you mean a constant creep of SOTA on meaningless
| benchmarks, then yeah, we have "progress", but that progress is
| a strange kind of progress that is measured only on its own,
| self-selected criteria, and that does nothing to advance the
| total sum of knowledge. The main beneficiaries of this
| "progress" are large technology corporations who have now taken
| over research in AI and are turning it to profit. There has
| definitely been proress in money-making schemes and personal
| aggrandisement schemes of charlatans and mountebanks, in AI,
| aplenty.
|
| If by progress you meant scientific progress, then, no, there
| has been none of that in recent years. It is questionable if
| there has ever been any kind of scientific progress associated
| with AI. While early pioneers of AI, like John McCarthy and
| Claude Shannon, wished to establish a scientific programme of
| research in human and machine intelligence, with the purpose of
| understanding the former by developing the latter, this
| programme was soon set aside, and activity concentrated instead
| in what McCarthy used to call the "look ma, no hands disease of
| AI":
|
| >> Much work in AI has the ``look ma, no hands'' disease.
| Someone programs a computer to do something no computer has
| done before and writes a paper pointing out that the computer
| did it. The paper is not directed to the identification and
| study of intellectual mechanisms and often contains no coherent
| account of how the program works at all.
|
| http://www-formal.stanford.edu/jmc/reviews/lighthill/lighthi...
|
| And this is where we still are today. AI is no science.
| ushtaritk421 wrote:
| Feels like AI makes the fight against paper mills easier. A group
| could submit AI-generated papers to different places and create a
| public index based on how successful they are at getting their
| nonsense papers accepted.
| EGreg wrote:
| Your comment seems self-contradictory.
|
| AI makes the _fight against_ spam harder since it is harder to
| detect fakes. The group would only get more successful over
| time. The "AI detection" tools get worse over time and
| overhyped already anyway, as we learned from the guys running
| that sci fi submission site.
| ushtaritk421 wrote:
| Right, but an org that accepts fake papers (now easily
| generated) gets outed easily and ignored by everyone.
|
| Legitimate orgs with real review processes presumably sniff
| out the fake papers easily with their current procedures and
| don't publish them.
|
| And I guess if AI gets good enough that an expert reviewer
| learns something novel by reading it then it deserves to get
| published.
| EGreg wrote:
| That's exactly the point. What you previously assumed
| review processes could catch is no longer true. AI can now
| infiltrate pretty much anything, especially if there is a
| Generative Adversarial Network (which "AI detection tools"
| can be used to train at scale).
|
| And if you say it "deserves to get published" then taken to
| its logical conclusion, human generated content in all
| fields and interactions will soon be dwarfed by AI content
| and interactions.
|
| The issue is that the AI can be switched out after it's
| infiltrated and taken over, or it can be gradually used to
| shift public opinion or organize any sort of coordinated
| attack. Heck, a reputational attack is easy to pull off at
| scale within 6 months via AutoGPT already, and it takes 1
| button press.
|
| First they'll separate us, then they'll herd us into echo
| chabers and cause our protests won't be heard by anyone
| anymore amid all the AI glut.
|
| It comes gradually, then all at once!
| bagels wrote:
| https://en.wikipedia.org/wiki/Sokal_affair
| SpaceBuddha wrote:
| "Later, after Sokal revealed the hoax in Lingua Franca,
| Social Text's editors wrote that they had requested editorial
| changes that Sokal refused to make, and had had concerns
| about the quality of the writing: 'We requested him (a) to
| excise a good deal of the philosophical speculation and (b)
| to excise most of his footnotes.' Still, despite calling
| Sokal a 'difficult, uncooperative author", and noting that
| such writers were 'well known to journal editors', based on
| Sokal's credentials Social Text published the article in the
| May 1996 Spring/Summer 'Science Wars' issue"
| noobermin wrote:
| That even makes it worse, I somehow didn't know this part
| of it.
| BeetleB wrote:
| Sokal did not publish in a peer reviewed journal.
| dontupvoteme wrote:
| The easiest solutions won't be appreciated -- chain of trust from
| older professors/academics who co-author(or otherwise sign off
| that it's legit), metrics based on historical outputs from
| university+lab, and i'm sure some will start looking at names.
| Balgair wrote:
| For 'lay' people here on HN, I just want to make a quick point:
|
| Peer-review does not mean that the reviewer is re-doing
| experiments or re-running analyses. They are only reviewing to
| see if the paper merits inclusion in the journal. Often this
| means telling the authors to do more experiments or check other
| things. But, to be clear, the peer-reviewer does not re-do things
| and check if they are 'right'
| JoshuaJB wrote:
| Although there is a type of peer review that includes redoing
| experiments and analysis: artifact evaluation. All of the top
| conferences in my field (real-time embedded systems) include
| this as an opt-in option, and papers get a special badge if
| they also pass artifact evaluation. I strongly believe that
| other fields in computer science would benefit by including and
| normalizing this process.
|
| Besides the reproducibility benefits, artifact evaluation
| forces documentation of the experiments and process; I've found
| this enormously useful when on-boarding new students to an
| existing project.
| photochemsyn wrote:
| The Jan Hendrik Schon scandal of two decades ago and the fallout
| from it point to how to limit the spread of fraudulent research
| into the accepted literature:
|
| https://en.wikipedia.org/wiki/Sch%C3%B6n_scandal
|
| The basic issue is that all raw data must be preserved and made
| available for scrutiny by other researchers after publication, as
| must experimental materials. Why?
|
| > "The committee requested copies of the raw data, but found that
| Schon had kept no laboratory notebooks. His raw data files had
| been erased from his computer. According to Schon, the files were
| erased because his computer had limited hard drive space. In
| addition, all of his experimental samples had been discarded or
| damaged beyond repair."
|
| The opposition to this standard is strongest in the corporatized
| patent-centric research sectors, which is most of applied science
| in the USA and China, etc. Ambitious academics in non-commercial
| sectors don't really like it either as it means competitors can
| jump-start their research by having access to their raw data and
| experimental protocols. Regardless, implementing standard
| practices with respect to laboratory notebooks, raw data, and
| experimental materials in any institution receiving federal
| research money makes a lot of sense - along with regular audits,
| with failure leading to a cutoff in funding.
|
| This problem is much broader than just the paper-mill outfits the
| article focuses on; some highly public and contentious related
| issues are public access to the raw data, research records and
| database sequences from the Wuhan Institute of Virology from
| c.2016-2019, the raw clinical trial data from Pfizer/Moderna/J&J
| Covid vaccine trials in 2020, and so on.
| pierat wrote:
| Then, lets use the 'AI' as its own proving ground.
|
| "AI, here's the text of this paper. We need to determine if it's
| accurate and true. It may be a false paper, and our goal is to
| determine that.
|
| AI, please analyze for logical fallacies, and list and cite them
| here.
|
| Next, for the potentially falsifiable claims, please give me a
| list of potential experiments that I can do or verify to check
| the claims being made."
|
| Edit: Wow, at -2, and attracting all the haters. Thought this
| place was about inquisitiveness and curiosity about tech. Oh
| well.
| worrycue wrote:
| I don't understand why you would think current AI can do any of
| that. Current AI can't even stop hallucinating.
| pierat wrote:
| Because I was sub'ed to openAI until yesterday. And my
| experiments with it were pretty promising.
|
| I took the whole corpus of Magic the Gathering rules (
| https://magic.wizards.com/en/rules ) as a textfile, and fed
| it into 4.0 . parsed it in a few seconds. I was then able to
| send it cards from Gatherer (MtG card database), and then ask
| it pointed questions about multiple card interactions.
|
| I also compared it to what DCI judges have made ruling on as
| well, and matched 100%. It quite impressed me.
|
| I was thinking next is to give it the rules text, and a JSON
| of every card. And then ask for all combos. But I'd run out
| of response before it could.
| raincole wrote:
| > But I'd run out of response before it could.
|
| So you used ChatGPT via the website.
|
| > I took the whole corpus of Magic the Gathering rules (
| https://magic.wizards.com/en/rules ) as a textfile, and fed
| it into 4.0 . parsed it in a few seconds.
|
| You mean the TXT file? ChatGPT(4) on the website literally
| can't comprehend it. It has much, much, much, much more
| tokens than ChatGPT(4) can take.
|
| So this particular example proved what your parent comment
| pointed out. You think AI can do something that it can't.
| jfengel wrote:
| Out of curiosity, did you try the same experiment without
| specifically training it on the MtG rules? Could it have
| all of that, including card interaction decisions, from its
| training data (sourced from the whole Internet)?
| pierat wrote:
| I only tried it after giving the URL of the rules.
| There's multiple rules documents, and I wanted to make
| sure to use the current rules.
|
| It might have given good results without explicit rules
| provided. Or it could have spouted garbage.
|
| I did however ask very pointed questions about timing and
| layers. The ones that had DCI judge writings matched 100%
| (could be overfit with matching these documents). And the
| ones not written about also appeared to be completely
| accurate as well, since it also cited the rules that it
| came to its decision.
|
| However the larger problem is that GPT4 has been
| degrading quite a bit recently. It also precipitated my
| decision to unsubscribe. And I'm not the only one to
| notice this https://news.ycombinator.com/item?id=36134249
| joshspankit wrote:
| I had hoped this was from the angle of "AI does better review
| than the journals" and "AI is being used to weed out a lot of
| papers that are just plain wrong".
|
| Maybe next time.
| ISL wrote:
| In the short term, the solution may be reputation, distributed by
| keychain.
|
| When author C submits a paper and they're unknown to the journal,
| the journal consults the keychain to see whom has vouched for C
| as a credible researcher. If authors A and B have vouched for
| them, and A and B are in similarly good standing, C is regarded
| as being in good stead and the editorial process can move forward
| as usual.
|
| If C is later found to have maliciously faked data, it isn't just
| C who gets dinged on the keychain, so do A and B by extension,
| providing an enforcement mechanism.
|
| This is, in effect, how it has worked for centuries. Editor D
| calls/writes to A and B to ask, "hey, there's this new author C
| with a provocative paper that's in your subject area, but I've
| never heard of them. Are they the real deal?" If A and B vouch
| for C, but C turns out to be a fraud, Editor D will take any
| future consultation with A and B with a large grain of salt.
|
| It is possible that AI will soon be able to generate entirely-
| credible looking papers. What AI will struggle to do, until it
| starts funding research of its own, is to generate novel research
| that reflects whatever is actually true of nature. A reputation
| system is the last bulwark against entropy, if no automated tools
| can sort wheat from chaff.
|
| Lest you think versions of these systems aren't already in place,
| try posting something to the arXiv as a new author.....
| https://info.arxiv.org/help/endorsement.html
| hospitalhusband wrote:
| This is web of trust.
| api wrote:
| You get what you incentivize. We incentivize papers so we get
| papers.
|
| Also anything used as a metric tends to cease to be a good
| metric.
| caddemon wrote:
| I think the main problem is not in itself incentivizing papers
| or using papers as a metric. The problem is that they're in
| many ways the only thing incentivized and the only metric given
| serious consideration. And the general expectations for/form of
| a paper are so homogenized across a given field. This makes it
| very feasible to game the system in a particular way that is
| bad for science.
|
| It's also a serious misallocation of incentives purely from a
| system functioning perspective. At the very least you need to
| incentivize peer review alongside incentivizing papers.
| Currently there is hardly any incentive at all to help
| carefully review papers, so we have no checks and balances in
| place to deal with the natural result of incentivizing the
| writing of papers. If there were greater heterogeneity in
| scientific roles it wouldn't be so bad for some to have heavy
| paper incentives -- because there'd be others with heavy
| incentives to do a good job highlighting the best papers and
| finding flaws in the bad ones.
| riku_iki wrote:
| There is a good metric in AI research: reproducible results on
| well known benchmarks (Big Bench for example).
| sebzim4500 wrote:
| There's no good benchmark that tests for most of the issues
| with LLMs, for example.
|
| Maybe if we had a benchmark that told you how often a model
| hallucinated we would have solved the problem by now.
| riku_iki wrote:
| There are plenty of benchmarks with factual questions.
| tikkun wrote:
| The upside from this downside is that it increases the
| performance of papers that can be replicated easily. In fact,
| once AI can replicate papers, that'll be helpful for verifying a
| lot of research.
| nradov wrote:
| How will AI be able to replicate papers?
| dsr_ wrote:
| The same way a Xerox machine does, but with different wording
| and randomly synthesized sources.
| captainbland wrote:
| This seems like a bleak dystopian fiction concept actually.
| AIs being in control of human experimentation on the basis of
| well meaning but poorly interpreted ethical guidelines (with,
| of course, fully hallucinated loopholes).
|
| Maybe add in that this has become one of the few sources of
| income for people as more and more jobs have been lost to the
| machines so many are compelled to participate.
| samstave wrote:
| " _Take all the research done in [field] and compare the
| papers written by N authors, and look into their citations
| and compare the outcomes of what was published and find
| where the summation of the reads of their papers as shared
| citations and summarize the research where they agree,
| disagree and compare with international organizations and
| their papers coming from specifically the leads in this
| field from countries X,Y,Z_ "
|
| _Create a paper based on this information and coalesce all
| this data into a new outcome. Do not Lie or make up data.
| list where you think you are lacking in data - or based on
| searching, which datasets or companies should be included
| in the list. Create a table for all the citations sourced,
| with links and a comment why it is or is not included in
| your findings "_
|
| EDIT: Someone down voted this, and I am learning 'Prompt
| Engineering' - can someone ELI5 why this is a bad prompt?
| Seriously, can someone explain what sucks about the
| question?
|
| Is it a stupid premise or is the crafting of the prompt
| lame?
| venv wrote:
| What you are describing, is not replication.
| yifanl wrote:
| Physically repeating the experiments and independently
| verifying the data in the research is missing.
| logifail wrote:
| > once AI can replicate papers
|
| Is this AI to be provided with a group of poorly-remunerated
| postgrads and postdocs and a laboratory full of relevant
| equipment in which they may conduct experiments under its
| direction?
|
| (Full disclosure: have a couple of very minor published papers
| obtained under the non-AI version of this exact system...)
| [deleted]
| mtkhaos wrote:
| Cheers to an Open Simulation Testing Platform. No reason not to
| have each aspect of our knowledge base to be testable in an
| reasonable amount of time.
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(page generated 2023-05-31 23:01 UTC)