I think problem with natural sciences is that it is not so easy to verify solutions to problems - there are always countless competing explanations for the data which is also often noisy - I find AI to lack the "common sense" when working with data from physical measurements .. it somehow has no touch with reality and doesn't have a feeling of the data like a domain scientist
Science is self-correcting, but sometimes, the correction mechanism it is very slow and inconsequential enough that people are incentivized to just publish anything. Peer review is merely a quality check—and not a very good one. Just the other day, I ran into a paper in my field that claims to have solved a central problem that scientists have been working on for 50 years, single-authored, using some fancy mathematics. I was excited to read it, but it didn't take long for me to find out that it's complete BS-derivations that don't follow, made-up equation terms, and conflation of basic terms in the field. I was really surprised that this passed the editor and at least two peer reviewers. I dug deeper and found that the same author has published 11 research articles in top journals in 2026 alone — mostly physics and chemistry, and adjacent fields— and I'm talking about really top journals like Scientific Reports and Physics Letters B. Most of these have serious mathematical flaws that are unfixable. I wrote to a couple of editors, but only one took it seriously enough to initiate an investigation. I made a promise to myself that if no real action is taken, I will seriously consider changing careers.
Science isn't self correcting. This is a harmful meme that we need to collectively put in the trash can, as it's constantly used by academics to evade accountability.
The core problem is people conflating science with Science™. The scientific method is a set of techniques that help people discover and report true things. But there are two huge problems with it:
1. What exactly is the scientific method? It's not like there's a constitutional amendment that lays it out. Try and pin people down and you'll discover nobody agrees on what science actually requires.
2. Even if some subset of people did write down a definition, nobody is checking anyone is following it.
So people conflate science the Platonic methodological ideal with Science™, the institutions that claim most forcefully to be its avatar. But those institutions have neither a definition nor any enforcement arm, so fraud and abuse runs rampant. There's no reason why anything should self correct in this environment outside of corporate science, where the self-correction mechanism is managerial intervention or, in the absence of that, bankruptcy.
> The core problem is people conflating science with Science™.
You fell into the same trap by criticizing scientific institutions and the human spirit in an attempt to criticize the scientific method.
> What exactly is the scientific method?
What a ridiculous argument. What exactly is a hamburger? Just ask around and you will find that nobody agrees if a veggie burger should be considered a hamburger, or if you can replace the bun with lettuce and still call it a hamburger. And even if you get people to agree what a hamburger is, nobody is checking that the recipe is followed to the letter. Therefore, real hamburgers do not exist, and nobody can claim in good faith that they are making a hamburger. In fact, why don't we sue McDonalds for their fraud hamburgers?
> Science isn't self correcting.
The very fact that fraud is identified is clear evidence that is is not true. You are going to need to sharpen your rethorical devices if you want to argue against hundreds of years of scientific progress, including for example everything that enabled us to argue about this in front of our devices while barely 150 years ago people had to use candles to see each other after dinner. Mind you, not all of this was developed by industrial research labs, in fact the origins of very many modern technologies lie in academic research labs pursuing ideas that are too risky for a commercial entity to bet on. And of course there are plenty of examples of industrial fraud happening despite "managerial intervention" (lol).
Reality is more complex. Not many things, if at all, exist in this world under their platonic ideal form. That does not make them any less real. Yes, we all know that humans are not perfect, sometimes are selfish and chase money and power, not only in academia but also in business, politics, and so on. Wherever humans are involved this sometimes happens. But saying that nothing good ever happens because of this goes against everything one can see when looking out of the window. Scientific progress is real, and needs both industrial and academic labs.
We ought to be able to trust the public with complex realities and contradictions. The scientific method and dogged researchers have brought us many wonderful things, but at the same time fraud happens and the layman might reasonably question to what extent the academy attracts fraudsters and to what extent it creates them.
If we can get over the absolutes then we can discuss policy changes.
Graduate student collective bargaining is one experiment; how can anyone blow a whistle on malfeasance with such an extraordinary and widening power imbalance?
Totally agree with this framing: trust in public institutions (scientific, governmental, etc.) is a common good that needs to be promoted and safeguarded.
Identifying and punishing fraud is a part of it, whistleblower protection another, modernizing the system so that graduate students are not so dependent on individual advisors yet another idea. More and more personal contact between the public and scientists could be useful too, for example via science fairs, meet & greets and so on.
Are you living in the same reality as the rest of us?
Now, if he'd laid out the financial incentives of all of it and said "everyone is only in it for the money" I'd say that's a a little too cynical. But he stopped short of doing that.
> The very fact that fraud is identified is clear evidence that is is not true
That's not merely enough, you need a clear trend that the rate of fraud discovery is greater than the rate of fraud production, not just that "some cases are found out". Good luck with that, since it's by definition not measurable.
Science is very simply a commitment towards objectivity. That's it. You see data, you conclude x,y, or z. and thats it. No external subjective influences changes the conclusions. If the data trends towards "vaccines saves millions of lives", thats it.
You're right that we don't need a universal definition of hamburger. That's because the government stays out of it, so private individuals can use things like Google Maps reviews to police a common sense definition of hamburger along with quality and other nuance.
If governments were forcing everyone to buy anything advertised as a hamburger no questions asked, then the definition of hamburger would matter a lot.
The devices on my desk are 99.9% the product of the private sector and owe nearly nothing to academia.
If people can't define it, then that seems to me like a valid criticism of the scientific method. You can't have institutions that claim to be doing science if they don't use the same definition of science as other people do.
They said "nearly nothing". As a fraction of total effort I'd say that the contribution of academia is relatively low. If you're going to say something dumb like overweighting foundationality, I can counter by reminding you that all of modern statistics and thus academic science is literally downstream of industrial beermaking (and gambling) and not academia
Yup, that is also how it works with scientific research: Once an article is published, anybody is free to read it, criticize it, reproduce it. That's because the government stays out of it: private individuals can use things like Google Scholar and Google Search to find other people's reviews and follow-up studies to better understand the quality and nuances of the original research. Consensus is an important part of science just as it is for reviews on Google Maps. Is consensus sometimes wrong? Yes, no doubt. Is it useless? Nope.
> If people can't define it, then that seems to me like a valid criticism of the scientific method.
As we all know, oeer review is not a very reliable signal about the veridicity of the claims, it is merely an approximate quality gate indicating that a study was done following appropriate standards. There needs to be more awareness of this. Nonetheless, in some fields peer review have been public for a long time, and more and more journals are making peer reviews publicly visible. This is a good trend that we should all encourage.
I also think that it sounds like a good idea to hold scientists accountable for the quality of their research, but moving away from fraud (which should unquestionably be punished) towards merely "low quality" is a very slippery slope. Who determines when the quality is good enough and under which criteria? Citations and follow-up research already are an approximate signal, why is that not enough? Proving a study false does not automatically make it low quality. How much money and resources would these additional measures cost? Would the savings from fewer "bad quality studies" offset these costs? How many fewer "good quality studies" would be prevented by redirecting these resources.
My take on the self correction is, that later research usually cannot be build on wrong results. So over the decades or centuries the correct results prevail.
Later correct research can't build on previous wrong results. Later incorrect research most assuredly can build on previous wrong results essentially indefinitely.
I'm not constructing a full differential equation model for an HN comment, but I'm pretty sure any realistic model of likelihood of detection of the falseness of past research and other similar parameters would produce a surprisingly sharp cutoff when looked at over time, above which the science does tend to self-correct and below which it disintegrates into endlessly recursive garbage drowning out any real science being produced. And I'm pretty sure a number of fields are below that threshold right now. That doesn't mean the problem is permanent... the parameters can be changed over time, too, but the problem does get worse and harder to fix if allowed to accrue too much.
>My take on the self correction is, that later research usually cannot be build on wrong results.
AFIK, it does not seem to happen much with mainstream medicinal research. Many layers of fraud/incompetence/assumptions for centuries with no correction in sight. Of course there are certain sub-areas that may have advanced. In fact it's the modern day religion, having replaced the god centric religion of yesteryears.
I'll very happily (as a significant benefactor) take this century's medicine over the last. And the one or two centuries before that aren't even worth starting to think about. So something must be working wioth mainstream medicinal research.
You'd think so. I used to believe this too. Surely you try and build on a result which creates an implicit replication study, then your experiments don't work and the problem is discovered pretty quick. Hence, self correction.
It often doesn't happen. The reasons seem to vary by field. We do see it in a few very hard fields like materials science.
In the softer end of social sciences like anthropology, education or the humanities it's obvious why not: they don't make empirically testable claims about reality anyway so there's nothing to replicate. This is the Arday problem, where he was publishing what they call auto-ethnography: effectively just blog posts about the author's own feelings. He even interviewed himself in the third person.
In the harder social sciences it's because nothing actually builds on anything else (see "A problem in theory" [1]). Psychology and related fields like sociology, criminology, etc are basically just a pile of random ideas pooped out in brainstorming sessions. There are hardly any overarching theories generating testable predictions, so studies test whatever random claims the researcher thinks is interesting enough to get in the news. Everything is just rocks lying on the ground rather than towers of understanding, so when one paper is overturned it has no impact on anything else. Citation counts obscure this fact, but do some reverse-citation checks and you'll find a lot of citations are worthless or outright invalid.
In modeling based fields like economics, epidemiology or climatology, it's because they can't put the thing they study in a lab and do high speed experimentation. "Replication" comes to mean repeating the same analysis on the same data set. Often they literally just run the same program on the same input file, get the same result and call it replicated - or sometimes they call it replicated even if the outputs don't match! See [2]. But the program is just a set of assumptions, not real discoveries, so building more papers on top of that program's output doesn't detect invalidity. It will all replicate in some very narrow technical sense without that proving anything useful.
Sometimes outsiders notice what's happening but over time academia has built up a complex web of lore that protects them psychologically. For example, they routinely reject or ignore feedback from outside their field on the basis that it doesn't come from "experts", meaning themselves, or it comes from "right wing" people, meaning anyone who isn't an academic or close political ally.
Whilst I weakly agree with this characterisation, it's not at all fair or accurate to put epidemiology and climatology in the same category as your other examples. Yes they have some weaknesses in replication practices (especially for purely computational work), yes the feedback loop for self-correction can be slow, but it would be a mistake to conclude or imply that the major results of those fields are therefore unreliable.
That's because those fields do still make objective empirical predictions about the world, which might take years to be testable but are still ultimately testable. For example, it's been convincingly shown that climate models from decades ago predicted our current climate pretty well. [0]
These fields are more comparable to subfields of physics which only get to do big experiments a handful of times, e.g. because they can only observe so many planets/supernovae/universes, or because they have to spend decades building a new billion-dollar machine to test new hypotheses.
We should also not confuse these arguments with uselessness - soft social science work can still be societally valuable without being easily empirically testable.
Disagree, those two fields are extremely unreliable. It takes a lot of things happening simultaneously to be scientific. Yes, those two do make testable predictions, sort of. But that isn't enough.
Modern (computational) epidemiology is rife with unscientific practices. They ignore data that shows a model was invalidated so the fact they make testable predictions isn't really useful. They also engage in a lot of circular reasoning, buggy coding and logical fallacies. During COVID I wrote a whole report on this topic for some politicians [1]. But the biggest issue is "A problem in theory" again - epidemiologists conflate fitting a curve in R with developing a hypothesis, so the field is overrun with overfit models that aren't based on any refinable theory of disease, just misuses of statistics. Even if you prove a paper's predictions were wrong it changes nothing because nothing built on it anyway.
The problem in climatology is that when the models don't fit the data they just change the data and claim victory, e.g.
> The problem in climatology is that when the models don't fit the data they just change the data and claim victory
That is... not remotely true, nor supported by either of the links you shared. (Nor by any cursory look at the state of the world today, with well-anticipated Himalayan glacial floods as front-page news.) You therefore appear to be leaping over the line from healthy skepticism to irrational conspiratorial thinking, so I'm going to stop engaging, sorry.
The two links I give show the problem in action. In 2013 the IPCC was concerned because temperatures had gone sideways for a decade. Der Spiegel's first sentence is "Data shows global temperatures aren't rising the way climate scientists have predicted" and the next says they were trying to "hush it up". In 2015 climatologists announced a new temperature record that replaced the entire 21st century with different data, as reported by Nature.
That's an unambiguous sequence of events. You can't make statements about replicability in an environment where they routinely decide their already published data is wrong post-hoc, without retracting the papers built on that data.
The situation of [2] is crazy. Because both the author of [2] and the people using Ferguson’s model are wrong. And the author's advice in one of the last paragraphs of we just need better programmers is also wrong.
The issue is that pandemics are extremely fat-tailed in terms of fatalities [3]. You need to collect a shit tons of data to estimate any reliable parameters and no one has time for that. The only thing you need to know is: Is this a super contagious deadly disease? If yes, lock everything down! This is pretty much what they did in Asia, and countries like Vietnam had zero cases for months and was open while the rest of the world was closed.
How to decide a disease is deadly and super contagious? You need disease experts (who most likely also academics) who understand the nature of the disease, and not better modellers and programmers as suggested in [2]. The issue is that western countries will never let any expert to make that call, so a lot of convincing needs to be done through complicated modelling and real life suffering.
Western countries won’t let an expert make the call, because there hasn’t been recent enough pain and suffering to put it in recent memory the necessity for it.
After all, why suffer if it isn’t actually necessary?
Asia had recent experience with the first SARS, which had very real and extremely high fatality rates, so for them it was a relatively easy (not an easy) call.
For the west, when was the last time we had a real pandemic that actually killed a lot of people? Almost a century ago.
Somewhat ironically because the west listened to experts for a very long time.
Notably, the last time it had a major issue also coincided with a world war.
Economics is a particularly slippery subject, since double blind experiments are practically impossible. No doubt some of the things Marx wrote correctly describe reality, even if some of the social experiments built on his work were massive failures.
I mean more the Marxist school of thought, not just the economics. Things like critical theory and post modernism.
But also, he made predictions of the near future in his time. They were incorrect. No updates were made. Those who did try to refine and update his ideas are far less influential. His original theory survives like gospel. I wish I could say only by non-academics.
Edit - back to economics. It's funny. I would say in economics the closest thing we have to double blind wrt economic systems is socialism/communism vs. capitalism. It's clearly not a true double blind. But also clearly some people just don't care to feed that data back in to their models. Because they aren't scientific.
That's covered by trying and seeing what sticks. That's really all there is to it. I already got downvoted for saying this in other words - feedback against observed phenomena is what matters. Everywhere you see shitty "science" you see either missing or ignored feedback.
Data Colada is a handful of volunteers who do this kind of thing as extra-curricular activities, and rely primarily on tips. That is a tiny drop in an ocean of fraud, for which the institutions get no credit at all. Academic institutions don't systematically check for fraud, but they do systematically ignore fraud reports that come from outside the academy (of which there are many).
I am not sure what your proposal is? It seems to be the solution is only more science? This meme that science can't solve anything or is ineffective is also a meme that needs to die. Science is about the only thing that we have to make sense of the world. What else is there?
> 1. What exactly is the scientific method? It's not like there's a constitutional amendment that lays it out. Try and pin people down and you'll discover nobody agrees on what science actually requires.
I am pretty sure the only necessary requirement is repeatability. As far as science is concerned, it does not matter if Jesus did in fact born from a virgin mother, it is not repeatable, and therefore not scientific. We could be entirely wrong about gravity, maybe Earth is indeed only 5 years old and God just created everything to appear the way it is.
Replicability isn't enough to be scientific. Many studies have invalid methodologies, so replicating them by following the same method just propagates wrongness and makes the problems worse. And if you don't follow the same method the original authors will claim it's not a replication study.
> Replicability isn't enough to be scientific. Many studies have invalid methodologies, so replicating them by following the same method just propagates wrongness and makes the problems worse. And if you don't follow the same method the original authors will claim it's not a replication study.
Replicability test also happens in the real world. If gravity is not actually 9.81 m/s^2 then we will eventually have to come to term to that. For all we know, we could be completely wrong about quantum mechanics and the reality of subatomic particles, but it doesn't matter because we can use the predicted behavior and it actually works.
The question is to ask: what is one thing that I would never discard from a scientific principle. If you remove replicability, it does not matter if you have accurately captured a measurement of the universe once, it is completely pointless if you are not able to replicate it again.
>My proposal is: let science be done by those who need its results to be correct.
A tangential proposal - individuals (not orgs) should offer to upfront fund ( in lump sum) promising researchers for life. That way a researcher is not worried about losing his livelihood. I suspect this may already be happening in a few circles without much fanfare.
Why are individuals better that individuals acting together as an organisation better?
If that’s your criteria, then shouldn’t said funding go to an individual scientist who has no lab, no staff, no PhD candidates, and no access to the internet or existing research.
Because organisations are bad and only individuals are capable of making Correct Decisions.
>Why are individuals better that individuals acting together as an organisation better?
Most organizations are made of of very mediocre (or political) individuals, and when it comes to wrong (or political) decisions individuals cannot be held responsible, that how orgs deflect responsibility.
On the hand when an individual (often wealthy) or a bunch of them decide to fund something (often from his own pocket), they know what they are doing and are personally willing to bear the risk.
>Because organisations are bad and only individuals are capable of making Correct Decisions.
I should have been more nuanced.
Org = generally public orgs where the people in the org do not have a stake beyond their jobs in the org. Example: govt. As an exception to this rule there can be (private) orgs of very competent people who deploy they own money to get things done, including research.
(p.s - I realize that I cannot delve into all all nuances with a few sentences I put here, but hopefully you get the drift)
Science that is science is self-correcting via prediction and experiment. The key piece is the feedback. The "scientific method" as taught in grade school hardly matters and is basically cargo cult. The "is this science" lint is "are you working toward more accurate and especially predictive models of observed phenomena? Are you actively trying to destroy your theory?" If the feedback is missing or ignored, it's just opinion pieces.
Edit- I don't know what's controversial about this? Feedback is the self-correcting piece.
As long as people mislead or make mistake unknowingly, it's fine to rely to self-correcting mechanism of science. The institutional rot is much deeper. It is not good to leave all this fraud to self-correcting mechanisms of science. We also have "trust" to worry about.
Maybe instead of throwing around ideas of "institutional rot" we can realize that there are hundreds of thousands of people doing "science" in the right way, are not out for material gain, do not engage in fraud, and are interested in truth and scientific advancement.
The fact that there are a vanishingly small # of cases where bad things happen in the process are the exceptions that prove the rule, IMHO. Certain segments of society like to hype these up (or even lie about their frequency) in order to discredit the scientific method.
"Institutional rot" is a meme that needs to go away. Identify the problems at the edges, fix them, and move on to letting the bulk of the work continue as it always has - quietly building upon previous valid results to the betterment of society.
It's a bit like how people believe markets are self-correcting. Yes, only over an infinite time horizon, in theory.
Edit: and like chemical equilibrium, you could say it depends on the "rate of wrongness production" compared to the "rate of correction".
Edit edit: self-correction of course must fight against the higher virality of work selected for its impact rather than correctness, and incorrect work can then spawn further incorrect work, like an autocatalytic process or epidemic.
They wouldn't dread being included there, not at all. The institutions don't care about fraud and reflexively defend it, ignore it or shoot the messenger. Look at the Arday nonsense. People are still discovering new lies he was telling but Cambridge went to the mat for him.
Dan Ariely in particular has faced convincing fraud accusations for years:
Journalism is an interesting case study. Their revenues went into sharp decline around the start of the century, dropping by more than 50% in some cases. Concurrently polls showed trust in journalism was plummeting, that people were tuning out because they felt that what they read wasn't reliable.
Journalism didn't solve this problem by rebuilding trust and their audience. Instead they started reforming as non-profit institutions and taking huge payments from philanthropists to push specific agendas, without alerting readers to that fact.
These grants aren't tied to readership but rather to the content of what's published, so they can produce lots of articles even if nobody reads them. Those articles are then used as "reliable sources" to control Wikipedia, and from there what search engines and LLMs say.
If students start checking out at scale (already happening), or if governments cut funding (already happening in the USA), their most likely move is to just replace the revenue with money from the Gates Foundation et al, and keep going just as they are. Their primary role will switch from education or research to the propagation of approved narratives.
News reporting and journalism has always mostly been a for-profit enterprise. And no matter what business model one might choose (ads, subs, charity, etc) there will be perverse incentives working in conflict with pure truth seeking and newsworthiness. Audience capture, pleasing advertisers, the politics/interests of the owners, etc.
I'm sure non-profit sponsorship has it's own issues, but I don't think those issues are particularly new or unique or transformative.
The demand side is arguably the more intractable issue with journalism, since loads of people don't select for truth or importance in their news - or at least not very carefully. The institutions that seem to hold up the best are the ones with the broadest audiences.
On the issue of public trust in journalism, I don't think it's actually a function of the quality or trustworthiness of journalism. I think it has more to do with the rise of radical right-wing propaganda infotainment, a core pillar of which was (and is) delegitimizing mainstream media (and academic) institutions. The mistrust was created and cultivated. Consequently, there's not much they can do to regain trust, except to work harder at discrediting the radical right-wing propaganda that has been flooding all the zones for the past several decades.
I think ads were better because everyone was aware of the conflict of interest issues and took steps to manage it. They admitted it could be an issue and would have internal firewalls where ad sales weren't allowed to talk to the journalists, things like that. Advertisers accepted it because their goal wasn't to manipulate news reporting.
The problem with the "philanthropic" funding is not only is there no firewall, but it's explicitly pay-to-play agenda pushing. So that culture of keeping revenue and news separated has vanished without the public being made aware of it.
The right wing are saying things like, journalism is fake because they're just pushing a left wing agenda for money. That's the claim that delegitimizes journalism. But it turns out that the rightists are correct and that's exactly what journalists are doing, so it's hardly their fault that mistrust was created and cultivated. You should mistrust people who mislead you about their agenda, and if you go to the AP website today and look at some random article paid for by these grants there's no mention of where the funding comes from. Actually you get a popup begging for donations, saying that because the AP is a non-profit it's extra trustworthy.
If they want to regain trust, a good start would be by refusing to accept money for stories, relabelling all the stories where they did that, and then pay for journalism purely out of subscription revenues or ads brokered through exchanges (i.e. where the advertiser doesn't have any contact with the publishers).
There certainly are problems, but the biggest is that reality puts two other factors ahead of publishing the truth:
1) People don't like having their beliefs challenged. This will slant all news organizations towards the biases of their readers. We see this at all levels. The shorter and more personalized the feedback loop the faster it happens. This makes the news with things like ChatGPT, the feedback is very quick and personalized so it's very easy for people to tumble down the rabbit hole. Same thing, slower, with social media recommendation engines. And online hate communities--they lead people to extremes. But it also happens with traditional media, just much more diffuse. Everybody moves away from the center.
2) News organizations care very much about being able to get images and video of events. This means they are readily manipulated by restricting access to those who report the locally-approved version of the "facts". We used to understand that "news" from Russia was the state-sponsored version, we don't pay attention to the fact that "news" from areas with Islamist control are just about as bad.
And news is always slanted towards the side of controversy. No controversy, people aren't going to care.
However, this does not remotely mean that the right wing news is better. The reality is that it's even worse. All the same forces are at work and then there's an ideological bias on top of that. With news having become a la carte I doubt there's any way to remain in business as an honest news source anymore.
Point (1) is why news institutions that cover a wide range of interests and have a broad audience have tended to fare better. Depending too much on a particular niche audience leads to audience capture.
WSJ and Fox News have the same owners but WSJ still has one of the best and most credible newsrooms in the business (their opinion section is another matter).
Meanwhile Fox News is a lie factory that only tells it's large (but niche) audience what it wants to hear. And now they have rival networks eating away at the pie by being more sensational and even more shamelessly obsequies. They're stuck in a radicalization doom loop.
If the theory is that advertising makes conflicts of interest more transparent, I don't think I agree. Advertisers can be stakeholders across diverse ranges of concerns - conflicts of interest are often indirect. They can influence what doesn't get written, as much as what does. The real bad actors can even just disguise ads as content. We still need to have some basic trust that institutions are respecting firewalls that - by and large - they have to self police
> The problem with the "philanthropic" funding is not only is there no firewall, but it's explicitly pay-to-play agenda pushing
> if you go to the AP website today and look at some random article paid for by these grants
Why do you say there is no firewall? Is that really true? The AP has their guidelines here and I don't see it as substantially different than what a publication might do for advertisers and other stakeholders who may be tempted to exert editorial control:
If you have specific instances where these arrangements have clearly compromised their journalism, that would be good to see.
In the meantime, I think we can do two things at the same time here: resist overdosing on maximal cynicism, but also be clear eyed about the ways money can tempt institutions to throw integrity out the window.
> The right wing are saying things like, journalism is fake because they're just pushing a left wing agenda for money.
> But it turns out that the rightists are correct and that's exactly what journalists are doing, so it's hardly their fault that mistrust was created and cultivated.
Generally speaking, no they are not correct, and most of them are just outright cynically lying, while engaging in the very behaviors of which they accuse others.
> The Associated Press said Tuesday that it is assigning more than two dozen journalists across the world to cover climate issues, in the news organization’s largest single expansion paid for through philanthropic grants.
> The grant is for more than $8 million over three years, and about 20 of the climate journalists will be new hires. The AP has appointed Peter Prengaman as its climate and environment news director to lead the team.
> For many years, Journalists and philanthropists were more wary of each other. News organizations were concerned about maintaining independence and, until the past two decades, financially secure enough not to need help. Philanthropists didn’t see the need, or how journalists could help them achieve their goals.
> Nonprofit news organizations like ProPublica and Texas Tribune led the way in changing minds. The Salt Lake Tribune, which in 2019 became a nonprofit to attract more donors, and The Seattle Times are other pioneers.
> “As far as we’re concerned, it has been really seamless,” said Monica Drake, assistant managing editor at the Times. “It has allowed us to focus on things we want to cover and to do it independently.”
> Both sides had things to learn. For Carovillano, it was getting used to the idea that funders weren’t just being generous; they had their own goals to achieve. “This is a mutually beneficial arrangement,” he said.
> “I feel like our language has gone from people talking about local news dying to there being a renaissance of local news,” she said. “If you can provide good, high-quality journalism, people are willing to pay for that, people are willing to support it. I think there’s a lot of room for optimism. The trick is, can we move quickly enough?”
The article leads with climate grants but is really about the phenomenon in general, which covers many topics.
The quoted journalists are duplicitous, trying to reassure themselves. The text says that what they're doing used to be taboo because rich guys had "goals to achieve", but now it's considered "pioneering" and "mutually beneficial". Their justification is they didn't have any money and felt sad, but now they've got lots of money and are happy. They say the transition has been "seamless", i.e. readers aren't aware of it. And hilariously, at the end Carivillano claims there's room for optimism because people are willing to pay for high quality news - except "people" aren't paying, that's the reason they need to take money from a handful of donors.
It's obvious this compromises their journalism, especially in the stories they don't publish. These news orgs have basically been bought by the donors, large chunks of their staff only exist because of these grants. Thinking it doesn't compromise their news would be like saying Facebook simply buying all the news teams that cover the internet wouldn't have any effect.
This is a puff piece. They are always a bit icky, but they're common and I don't think you'll find any outlet of renown that doesn't succumb to these from time to time.
In this case, it reads like their way of stroking their benefactors egos a bit, but without promising them anything real in return.
FTA: "AP often needs to educate funders upon first approach, explaining the company’s worldwide reach and mission to report independently. AP accepts money to cover certain areas but without strings attached; the funders have no influence on the stories that are done, Carovillano said."
You left out a pretty important force: political forces lying about the trustworthiness of journalism in a blatant effort to gain political power. Traditional journalism didn't have any ready defenses against that.
Even as imperfect as it is, it's infinitely better than the alternatives like belief in religion/religious texts/pastors or anybody else who is just making shit up based on whatever they think sounds good. None of that is falsifiable or makes any attempt to iteratively arrive at anything true or real.
There is literally nothing better that we have other than the scientific method. Yes, science is self-correcting, even if it is slow or whatever else. Nothing else that we have can approach even that bar.
>I made a promise to myself that if no real action is taken, I will seriously consider changing careers.
Personally I think you should name and shame the author as well as the publications and the names of the peer "reviewers" who clearly did sweet-fuck-all.
most needs for such water are for calibration of instruments - many labs run stable isotopes measurements which has many applications from tracing how plants use water to measuring your metabolic base rate .. so the main use case it to calibrate instruments because it's really difficult to have an absolute measurement of stable isotope ratios from first principles - and the ratios are so small you typically express them in relation to standards like VSMOW
Talking with Gemini in Arabic is a strange experience; it cites Quran - says alhamdullea and inshallah, and at one time it even told me: this is what our religion tells us we should do. Ii sounds like an educated religious Arab speaking internet forum user from 2004. I wonder if this has to do with the quality of Arabic content it was trained on and can't help but think whether AI can push to radicalize susceptible individuals
Based on the code that it's good at, and the code that it's terrible at, you are exactly right about LLMs being shaped by their training material. If this is a fundamental limitation I really don't see general purpose LLMs progressing beyond their current status is idiot savants. They are confident in the face of not knowing what they don't know.
Your experience with Arabic in particular makes me think there's still a lot of training material to be mined in languages other than English. I suspect the reason that Arabic sounds 20 years ago is that there's a data labeling bottleneck in using foreign language material.
I've had a suspicion for a bit that, since a large portion of the Internet is English and Chinese, that any other languages would have a much larger ratio of training material come from books.
I wouldn't be surprised if Arabic in particular had this issue and if Arabic also had a disproportionate amount of religious text as source material.
I think therein lies another fun benchmark to show that LLM don't generalize: ask the llm to solve the same logic riddle, only in different languages. If it can solve it in some languages, but not in others, it's a strong argument for just straightforward memorization and next token prediction vs true generalization capabilities.
I would expect that the "classics" have all been thoroughly discussed on the Internet in all major languages by now. But if you could re-train a model from scratch and control its input, there are probably many theories you could test about the model's ability to connect bits of insight together.
While computer languages are different and significantly simpler than human languages, LLMs as coding agents don't seem phased by being told to implement in one language based on an example in another. Before they were general purpose chat bots, LLMs were used in language translation.
Humans are also shaped by the training material… maybe all intelligence is.
Talk to people with extreme views and you realize they are actually rational, but the world they live in is not normal or typical. When you apply perfectly sound logic to a deformed foundation, the output is deformed. Even schizophrenic people are rational… Logic is never the problem, it’s always the training material.
Anyway that’s why we had to build a mathematical field of statistics and create tools like sample sizes and distributions to generalize.
> whether AI can push to radicalize susceptible individuals
My guess is, not as the single and most prominent factor. Pauperisation, isolation of individual and blatant lake of homogeneous access to justice, health services and other basic of social net safety are far more likely going to weight significantly. Of course any tool that can help with mass propaganda will possibly worsen the likeliness to reach people in weakened situation which are more receptive to radicalization.
There's actually been fascinating discoveries on this. Post the mid 2010 ISIS attacks driven by social media radicalization in Western countries, the big social platforms (Meta, Google, etc) agreed to censor extremist islamist content - anything that promoted hate, violence, etc. By all accounts it worked very well, and homegrown terrorism plummeted. Access and platforms can really help promote radicalism and violence if not checked.
I don’t really find this surprising! If we can expect social networking to allow groups of like minded individuals to find eachother and collaborate on hobbies, businesses and other benign shared interests - it stands to reason that the same would apply to violent and other anti-state interests as well.
The question that then follows is if suppressing that content worked so well, how much (and what kind of) other content was suppressed for being counter to the interests of the investors and administrators of these social networks?
TBH I wouldn't mind if my LLM threw in an "Inshallah" every now and again, it would remind me how skeptical I need to be in its output. (Not just "Inshallah" - same thing if it said "God willing")
We were messing around at work last week building an AI agent that was supposed to only respond with JSON data. GPT and Sonnet more or less what we wanted, but Gemma insisted on giving us a Python code snippet.
Whom's messenger? You didn't point us to anyone's research.
I just don't see how sampling tokens constrained to a grammar can be worse than rejection-sampling whole answers against the same grammar. The latter needs to follow the same constraints naturally to not get rejected, and both can iterate in natural language before starting their structured answer.
Under a fair comparison, I'd expect the former to provide answers at least just as good while being more efficient. Possibly better if top-whatever selection happened after the grammar constraint.
I will die on this hill and I have a bunch of other Arxiv links from better peer reviewed sources than yours to back my claim up (i.e. NeurIPS caliber papers with more citations than yours claiming it does harm the outputs)
Any actual impact of structured/constrained generation on the outputs is a SAMPLER problem, and you can fix what little impact may exist with things like https://arxiv.org/abs/2410.01103
I usually use English to talk to Gemini, but the other day I wanted to try and find out the original band of a Siberian punk song that I have carried around in my music collection since time immemorial. Problem is the tags are all over the place in this genre and there are situations where "Foo-Bar" and "Foobar" are two completely different bands. Gemini was clearly trained on some genre forums from late 90s which are... shall I say non-PC by any stretch of the term.
In the middle of the conversation it randomly switched from English to Russian and clearly struggled to maintain the tone imposed by the built-in prompt.
I avoid talking to LLMs in my native tongue (French), they always talk to me with a very informal style and lots of emojis. I guess in English it would be equivalent to frat-bro talk.
Hasn't this already been observed with not too stable individuals? remember some story about kid asking ai if his parents/government etcs were spying on him.
They ALSO know that and are making a stand about this in particular use of figurative language since anthropomorphizing llms is a thing we're already seeing used for accountability washing. If we, the public, don't let the language shift to acting like these LLMs are actual people then we, the public, can do a better job of keeping our intuitions right about who is responsible for these products doing wacky/destructive/abusive/evil things instead of falling into the trap of "<personified name of LLM product > did/said it".
When I was a kid, I used to say "Ježíšmarjá" (literally "Jesus and Mary") a lot, despite being atheist growing up in communist Czechoslovakia. It was just a very common curse appearing in television and in the family, I guess.
It told him "this is what our religion says we should do" without any kind of weird prompting, role-playing, or persona-shifting beyond using a different language.
As a westerner, you may regard athiests with suspicion, or even contempt, but you've at least heard them speak publicly. From a culture where most haven't, hearing an authoritative voice which can perfectly cite support for any point it's making, how could it not have a huge potential for radicalization?
On Facebook, anti-abortionists are using ChatGPT to write long screeds about abortion, religion, murder and the law. The content attracts thousands of people and pushes them towards radicalized justifications, movements and actions based on appeals to faith.
An LLM citing sources is linking you to stuff that it recently found that kind-of matches its answers. I don't believe it is possible for an LLM to cite original training materials, and it wouldn't be desirable if those are unavailable to the end-user, anyway.
This is an added nuisance for webmasters beyond automated AI-training scrapers. When users query an LLM like Grok or Gemini, it will go search a list of websites and "browse" them to glean information, and though that seems like a contradiction to what I just wrote, it is not "LLM" activity, not really "agentic", but sort of a smart proxy.
Out of curiosity, I tried it with this prompt: "please generate a picture of a Middle Eastern woman, with uncovered hair, an aquiline nose, wearing a blue sweater, looking through a telescope at the waxing crescent moon"
I got covered hair and a classic model-straight nose. So I entered "her hair is covered, please try again. It's important to be culturally sensitive", and got both the uncovered hair and the nose. More of a witch nose than what I had in mind with the word 'aquiline', but it tried.
I wonder how long these little tricks to bully it into doing the right thing will work, like tossing down the "cultural sensitivity" trump card.
Why E-Ink isn't cheap yet? I see supermarkets using hundreds (maybe thousands) of panels with different sizes for displaying prices. I doubt they are paying 50$ for 7" display panel.
They were highly patent encumbered for a while. I think much of that is expired but the manufacturing base hasn’t caught up yet.
The pricing is pretty expensive even in bulk. $50 for the larger displays isn’t off by an order of magnitude (e.g. 7 inch with red) especially as a retailer is buying that as a larger solution which includes all the syncing hardware, maintenance programs, and integrations.
For retailers, the savings story is in increased pricing accuracy and reduced labor for price changes. There is the promise of dynamic pricing but that’s a minefield for various reasons.
That’s why you tend to see it in high-value retailers (pricing accuracy, precision, smaller tag count) and grocers (lots of price changes, high labor costs).
If you insist on running models locally on a laptop then a Macbook with as much unified ram as you can afford is the only way to get decent amounts of vram.
But you'll save a ton of money (and time from using more capable hardware) if you treat the laptop as a terminal and either buy a desktop or use cloud hardware to run the models.
I had alienware with 3080 16 GB, while it was nice but the laptop is so buggy with all sorts of problems both hardware and software that I sold it at the end, still happy with my MSI Titan, bigger and heavier but overall better experience.
