Well, they might also like a word with all of the scientists and students who make sure their papers are available online, despite Springer's draconic requirements. But that word is no longer heeded. Well, for the most part.
One simple way to understand how this is currently manifesting is the administration's severe, and really unprecedented, defunding of basic science at NSF (likely in unconstitutional defiance of Congress' appropriations to specific NSF directorates). Instead the way the funding has been redirected (by the White House OSTP) is based on the assumption that future knowledge will be exclusively AI (sorry SI) generated, so humans not needed.
China, on the other hand, seems to be doubling down on fundamental research, including announcing these changes:
"The shift suggests China's innovation system is moving from strength in scale and application toward a greater emphasis on source innovation and breakthroughs in fields such as AI, quantum technology and life sciences," Song added.
> Instead the way the funding has been redirected (by the White House OSTP) is based on the assumption that future knowledge will be exclusively AI (sorry SI) generated, so humans not needed.
Can you go into more detail about that? My impression is the real reason the Trump administration has defunded basic science (and so much else) is an unholy combination of "government bad" libertarianism and the belief the funding was going to their ideological opponents.
I feel that and use of AI to justify the redirection would be post-hoc.
Going by the public announcements, the original plan was to cut science funding but once Congress said "no," the backup plan became directing it towards non - human - scientist items like facilities operations and the Genesis program.
Echoing what the other reply to your question said, I don't disagree that much of this post-hoc, but I think the thinking has evolved such that this post-hoc reasoning is now truly believed. I'm primarily speaking of it as an observer of how and where funds are being directed and to what ends: often big, non-university-centered programs to build labs or generate datasets for AI discovery. While some of the ideas may have merit, there is no free lunch unfortunately, as that was money that would have supported individual PIs and their trainees.
This administration thinks science is 'woke' because it says things that disagree with him. Many of these funding cuts were explicitly politically motivated, e.g. they specifically targeted research about diversity, gender, or vaccines.
It's not about AI, it's about removing funding from liberal causes and giving it to conservative causes.
No it is about AI too. Trump et al want there to be no dispensing of federal grants to academics in the future. They want the government to instead cut checks directly to xAI et al who will do all our research for us in the future and will for certain represent our best interests and not those of shareholders exclusively.
If AI didn't exist, he'd find some other excuse to do the exact same thing.
The real reason for this is that Elon Musk is a conservative megadonor, while academics are vocally opposed to Trump. He takes from his enemies and gives to his friends; that's how he operates.
Unfortunately it is entirely related. The initial wave of cancelations were about those topics, but the more structural thing they are attempting now is to focus funds on tool development for AI models as opposed to human capital development as NSF does.
This is not that difficult. It's just like what happened to physical fitness/obesity after the invention of the car. Go look at countries that do it reasonably well (ie not the US). Copy exactly what they do, wholesale.
When you can equally reasonably make the opposite argument - that AI is going to open up scientific exploration and contribution to a wider range of participants than ever before - then I think it’s a sign that nobody has any idea what’s going to happen and spending time making either claim is probably pointless until we have more data.
> When you can equally reasonably make the opposite argument - that AI is going to open up scientific exploration and contribution to a wider range of participants than ever before
Is typing a prompt into ChatGPT "science"? It's an experiment, I guess, to find out whether you get a useful response out of it, and your hypothesis is that the model is able to answer your question correctly? How do you falsify that if you don't have the academic foundations to verify its answer?
ChatGPT is the research assistant, not the experiment subject itself. The science exploration is to use it to explain the existing science and help you design a good experiment, and then once you've run the experiment and gotten the data, as a chemist/biology/someone who hates coding, it'll write the code to analyze your data and make some cool visualizations.
Whether or not people complain is irrelevant, it'll happen anyway.
The ingestion of all knowledge is also resulting in open (weight) models!
You'd be better served thinking of "all knowledge so far" as the teeniest tiniest speck compared to what we are going to get in exchange for making it all regurgitatable on demand.
We are at the inflection point of a knowledge and art hockey stick chart, and pulling the wool over your eyes - by looking to the past - will cause you to not engage your creativity and thus miss this oppprtinity.
I agree it is terrifying because it is unknown. It is also true that the frontier AI labs have not worked well with affected communities at all and often downright exploited them.
BUT I also agree that, like any new technology, people will find mind-blowingly creative paths forwards with it, opening up whole new art forms.
> AI labs have not worked well with affected communities at all and often downright exploited them
The first reviews of OAI's dump on math are coming out. Quasi-Riemann Hypothesis Proof looks to be valid. The irony is that is uses and approach that the people who work in that area have disregarded for the most part. As well as mining some other parts of maths corpus. There really isnt "innovation" there.
For as much as OAI is guilty of leveraging work in progress, the complaints about some of the other work(s) are looking spurious (and I'm being very kind using that word).
It's not terrifying because it's unknown. It's terrifying because a tiny number of obscenely rich psychopaths are trying to take sole control of it so they can enshrine themselves as living gods and make 99% of humans so obsolete that they die sick and cold in the streets. That's their overt goal, and almost all evidence we have from history and current politics suggests that they will be wildly successful at this. None of this is for you, it's all for them. All of it. That's why it's terrifying.
this approach of calling critics "whiners" then making sweeping, naive statements regarding both capital-S Science and also markets, is shallow.
The West collectively has touched markets with Science using concepts of property rights. All of that is upturned now. I agree that publishing portals like Elsivier have done damage to Science while enriching the company and its allies. However that complaint looks quaint compared to literally four military-industrial groups running all new discoveries and their commercialization.
This is supposed to be The technology that will colonize the universe, and you're saying "Games are being remixed" is the happening we're supposed to be excited about? That's the hockey stick inflection point?
I understand the all-knowledge so far types of references, but it also reveals to me that most people who say that actually have little to no idea that AI doesn't possess information that exists only in the heads of people which has never been written down and there's substantial value to it which day-to-day uses of AI are not able to reach.
There're two effects: first, the "envelope effect" wherein everything within the "knowledge envelope" of humanity becomes easier to "fill in", with more people making more connections possible faster among known things. Think cross-disciplinary insights.
And then there's a different effect, where a person or team can leverage AI to leap straight to the frontier of a field and then proceed outward from there.
Both are possible right now and available to anyone with a phone.
" an intangible resource that "no one owns and is shared by everyone."" ... but if you try to share this "un-owned" intangible resource with others you will have police all over the globe harassing and maybe even imprisoning you (see: Anna's Archive).
So ... "no one owns" is doing some pretty heavy lifting there ... and it's kind of the foundation of the entire article.
We need to create a new scientific regime in the spirit of open source where scientific papers are not permitted to be used to train closed models. If you want to use the output of human ingenuity, you need to give something back.
I don't really understand the rationale behind accepting change and knowing change is inevitable, but wanting any such change to be fully controlled every step of the way?
It's hard to imagine what kind of contortions someone would have to go through to come to this conclusion.
Not one word of copyright length, which is now effectively over a century long. Not one word of some of the most successful AI models being open source.
Commons encirclement, as the author is describing, has been happening since source code was labelled art instead of engineering and since lobbying efforts have been extending copyright terms. My view is that AI technology are trampling over the encirclement, which is why so many people are upset.
It's hard to imagine what kind of contortions someone would have to go through to come to this conclusion after reading (?) this paper.
Literal quote:
"Over the course of the twentieth century, the scope of human cre-
ations that were treated as IP expanded dramatically, a phenomenon that has been
called 'the second enclosure movement'."
Open-weight models aren't open source. Companies that release open-weight models do so because it currently suits their business interests. They can cut you off whenever they want or if they come under political pressure to do so. If they truly cared, they would release their training data and procedures and democratize access to their training infrastructure.
The discussion of open source was pretty anemic. The author seems to think open source is only GPL copyleft. In reality the vast majority of open source is permissively licensed, and the development was funded by for-profit companies. That seems like a crucial thing to consider when discussing how our information commons has come to be.
Near the end they include a paragraph claiming we should interpret this piece as being about more than self-interest and resistance to change, but it doesn't really feel like the piece offered a taste of what that "more" is.
The most thought-provoking part for me was considering that AI companies have used pretty much all available data for training while "they spend more and more money to create proprietary training data". That seems to break a sort of intuitive social contract.
Grant from 3brown1blue released a video exploring the economics and mechanics of the math "industry" in light of the AI company releases over the last couple weeks. Overall it felt a lot more hopeful, and didn't need to strain really hard to apply a historical framework like enclosure. Worth checking out: https://www.youtube.com/watch?v=LKrwhjOI8Nc
given than LLM training doesn't "need" yet to adhere to much licensing, i'd say any source available, which with "reverse engineering" as skill anything is, makes the field ripe for disruption by llm's
I have read more paper in the last year than the last ten years. Across a wide variety of subjects. While the AIs still need open access to science in order to function, they have make that open access something I can actually use like no other technology before it. Nothing has opened the scientific commons to me like AI.
There may be a concern going forward with locking things down. One can lament that AIs need to be prompted to "hit the literature" directly or they don't really think about it, prefering random web pages most of the time. But at least at the moment science is more open and accessible than ever before.
You have read more paper(s) or you have had an AI that is RLHFed to please the median consumer who reads at an 8th grade level summarize more papers than ever?
Keep in mind if Alexandra Elbakyan shares the knowledge she is a criminal who needs to be extradited to face consequences. When you are a Californian AI lab that needs to train a model to give back all this static information to users, you are alright, actually you are the only thing keeping US economy going. Never be in out group its hard out there.
The academic literature has a problem in that it is relatively incoherent. Each new paper provides some small advance, with only a meager indication of how it fits into the known science in the area, its importance, and its consistency with previous results. It also remains in the corpus if later publications disprove its results or fail to replicate them. Only slowly do the aggregate of published results find their way into more systematic and complete authoritative texts.
AI promises to solve this problem by absorbing the entire corpus quickly and making a coherent whole available for further research and progress. It will be particularly useful if it can immediately flag any conflicts and inconsistencies, as well as any new relationships with other results not indicated in citations.
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[ 3.9 ms ] story [ 10.3 ms ] threadSpringer would like a word
China, on the other hand, seems to be doubling down on fundamental research, including announcing these changes:
https://www.globaltimes.cn/page/202609/1371583.shtml
"The shift suggests China's innovation system is moving from strength in scale and application toward a greater emphasis on source innovation and breakthroughs in fields such as AI, quantum technology and life sciences," Song added.
and this: https://www.scmp.com/news/china/science/article/3368750/chin...
Can you go into more detail about that? My impression is the real reason the Trump administration has defunded basic science (and so much else) is an unholy combination of "government bad" libertarianism and the belief the funding was going to their ideological opponents.
I feel that and use of AI to justify the redirection would be post-hoc.
This administration thinks science is 'woke' because it says things that disagree with him. Many of these funding cuts were explicitly politically motivated, e.g. they specifically targeted research about diversity, gender, or vaccines.
It's not about AI, it's about removing funding from liberal causes and giving it to conservative causes.
The real reason for this is that Elon Musk is a conservative megadonor, while academics are vocally opposed to Trump. He takes from his enemies and gives to his friends; that's how he operates.
https://www.youtube.com/watch?v=KPUlgSRn6e0 [Not Just Bikes]
Is typing a prompt into ChatGPT "science"? It's an experiment, I guess, to find out whether you get a useful response out of it, and your hypothesis is that the model is able to answer your question correctly? How do you falsify that if you don't have the academic foundations to verify its answer?
The ingestion of all knowledge is also resulting in open (weight) models!
You'd be better served thinking of "all knowledge so far" as the teeniest tiniest speck compared to what we are going to get in exchange for making it all regurgitatable on demand.
We are at the inflection point of a knowledge and art hockey stick chart, and pulling the wool over your eyes - by looking to the past - will cause you to not engage your creativity and thus miss this oppprtinity.
BUT I also agree that, like any new technology, people will find mind-blowingly creative paths forwards with it, opening up whole new art forms.
The first reviews of OAI's dump on math are coming out. Quasi-Riemann Hypothesis Proof looks to be valid. The irony is that is uses and approach that the people who work in that area have disregarded for the most part. As well as mining some other parts of maths corpus. There really isnt "innovation" there.
For as much as OAI is guilty of leveraging work in progress, the complaints about some of the other work(s) are looking spurious (and I'm being very kind using that word).
The West collectively has touched markets with Science using concepts of property rights. All of that is upturned now. I agree that publishing portals like Elsivier have done damage to Science while enriching the company and its allies. However that complaint looks quaint compared to literally four military-industrial groups running all new discoveries and their commercialization.
This is not resolved at all...
I'm just advocating mindfully managing your own influences.
I've never seen so much faith outside of a religious setting. Do you have any proof of this at all, or is it a faith-based system?
Its been 4 _years_ since GPT 3.5's release. Where are all these marvels people keep saying are just around the corner?
It's happening all around but you're somehow not seeing it.
This is supposed to be The technology that will colonize the universe, and you're saying "Games are being remixed" is the happening we're supposed to be excited about? That's the hockey stick inflection point?
And then there's a different effect, where a person or team can leverage AI to leap straight to the frontier of a field and then proceed outward from there.
Both are possible right now and available to anyone with a phone.
Whether it meets human needs and meets them in a rich and satisfying way is another question.
So ... "no one owns" is doing some pretty heavy lifting there ... and it's kind of the foundation of the entire article.
UK has strong inheritance tax, so common angricultural land is very strong and doing quite well!
Not one word of copyright length, which is now effectively over a century long. Not one word of some of the most successful AI models being open source.
Commons encirclement, as the author is describing, has been happening since source code was labelled art instead of engineering and since lobbying efforts have been extending copyright terms. My view is that AI technology are trampling over the encirclement, which is why so many people are upset.
Literal quote: "Over the course of the twentieth century, the scope of human cre- ations that were treated as IP expanded dramatically, a phenomenon that has been called 'the second enclosure movement'."
Open-weight models aren't open source. Companies that release open-weight models do so because it currently suits their business interests. They can cut you off whenever they want or if they come under political pressure to do so. If they truly cared, they would release their training data and procedures and democratize access to their training infrastructure.
I still disagree but it looks like the author makes a cursory attempt to address the issues that I think are more relevant.
Near the end they include a paragraph claiming we should interpret this piece as being about more than self-interest and resistance to change, but it doesn't really feel like the piece offered a taste of what that "more" is.
The most thought-provoking part for me was considering that AI companies have used pretty much all available data for training while "they spend more and more money to create proprietary training data". That seems to break a sort of intuitive social contract.
Grant from 3brown1blue released a video exploring the economics and mechanics of the math "industry" in light of the AI company releases over the last couple weeks. Overall it felt a lot more hopeful, and didn't need to strain really hard to apply a historical framework like enclosure. Worth checking out: https://www.youtube.com/watch?v=LKrwhjOI8Nc
- Dario
There may be a concern going forward with locking things down. One can lament that AIs need to be prompted to "hit the literature" directly or they don't really think about it, prefering random web pages most of the time. But at least at the moment science is more open and accessible than ever before.
I've also read some summaries of even more papers, but yes, actually read papers, beyond the abstract.
AI promises to solve this problem by absorbing the entire corpus quickly and making a coherent whole available for further research and progress. It will be particularly useful if it can immediately flag any conflicts and inconsistencies, as well as any new relationships with other results not indicated in citations.