curt15

Your monopoly theory lacks a credible motive and also ignores the fact that your quote specifically targets the frontier labs, not LLMs or computational tools in general. The group (https://agmai.org/) comprises of world-renowned mathematicians, including several Fields medalists, who have nothing left to prove. If they felt like it they could quit mathematics today and take private sector jobs paying far more than their professor salaries.

The basic purpose of mathematics has always been human understanding (see for example https://mathoverflow.net/a/44213), and the working group's various recommendations simply aim to ensure that computer-generated mathematical activity aligns with that purpose.

New norms are only worth adopting if they are clearly better, and that is far from obvious for whatever norms the proprietary AI companies are trying to push. Also their letter specifically targets proprietary AI companies, not computational tools in general which mathematicians do use when they advance mathematical understanding.

The problem with proprietary models is that you don't get to poke inside and see what it is doing and how it arrives at its answer, which is precisely what mathematicians do. Mathematical understanding derives less from any particular result than the insights and methods that pave the road to results. Whenever a theorem is proved, researchers seek to unpack the proof and get inside the author's mind to learn their ways of thinking.

LLM generated results might benefit mathematical understanding if people can inspect their intermediate reasoning traces to discover erroneous human biases or patterns that they might have previously overlooked. Otherwise, the results might as well be produced by oracles.

What will the path to a tech lead look like when it's no longer paved by thousands of hours of experience internalizing code? How do future tech leads avoid becoming like people who never got comfortable with fractions because they offloaded all arithmetic to calculators from an early age?

Before extrapolating that far, take a look at the frontier labs' own job boards (https://openai.com/careers/search/?). Isn't it curious that they are still recruiting human "Android Engineers", "Account Associates", "Consumer Marketing Leads" instead of automating them with their world-beating models?

How about

> Cluster mirroring embeds cross-cluster replication directly into the Kafka broker. No external processes, no offset translation tables, no recompression.

I think the OP was referring not to their public image, but to the incongruity of paying human influencers instead of deploying computer-generated influencers created by their world-beating models supposedly worth $30T.

Or people with a vested interest in pushing the frontier labs' preferred narrative that they've achieved "AGI"...even while frontier labs continue to hire human "Account Associates", "Android Engineers", "Applied AI Engineers"...

At least Android allows truly alternative web browsers with ad blockers that aren't handicapped by Manifestv3 limitations.

You can call it survivorship bias, but another way to phrase it is that it's very difficult to forecast the practical benefit of any one piece of mathematics even while the long-term impact of mathematics as a whole is undeniable. And any schemes to further ration resources among mathematicians ignores the reality that for such foundational subject, math research already one of the least funded compared to other disciplines or domestic priorities.

> And why the hell would anyone want a job where a mistake results in personal ruin?

People will do nearly anything if the price is right.

The primary goal of all basic sciences is human understanding. "Truth" is no more a goal for mathematicians than the physical laws are a goal to physicists; they simply exist in nature. The goal is rather to develop useful language and conceptual frameworks for reasoning and communicating. That understanding underpins all practical applications.

How do you normally verify the work product of a "few-shot" development process? Do you scrutinise the source code like with human developers? Or do you just run the test suite and click around the app to check if it seems to work?

Since OpenAI is undoubtedly also under economic pressure, why is it still hiring humans for Account Associates, Android Engineers, Data Scientists instead of demonstrating its AI prowess by automating those roles?

https://openai.com/careers/search/

> He’s also always argued that mathematics is fundamentally about human understanding.

It's also worth pointing out he didn't come up with this viewpoint just to "cope" with the headlines. Thurston articulated this way back in the 90s (https://arxiv.org/pdf/math/9404236) and also more recently (https://mathoverflow.net/questions/43690/whats-a-mathematici...):

> The product of mathematics is clarity and understanding. Not theorems, by themselves. Is there, for example any real reason that even such famous results as Fermat's Last Theorem, or the Poincaré conjecture, really matter? Their real importance is not in their specific statements, but their role in challenging our understanding, presenting challenges that led to mathematical developments that increased our understanding.

> I thought the reason we were encouraging people to go into STEM, and providing clever people with large salaries, was that mathematical results were of practical value to the wider human race. In which case, it’s surely very good news that AI can get those results.

The impact of mathematics on other disciplines goes much further than specific results. Just as important, if not even more so, are the language and ways of thinking that typically come out of the process of establishing those results. Results without the accompanying conceptual understanding are about as useful as a mere oracle for math theorems.

Another point that seems to be frequently missed or mischaracterized is that mathematicians are not opposed to computational tools as a matter of principle and in fact do use them when they help their research. The current controversy is not about a hypothetical future where mathematicians have easy access to open-source, open-weight, auditable natural-language assistants to help them internalize a new result or search for counterexamples. It's primarily about the recent behavior of certain for-profit companies suddenly trying to disrupt mathematics by caricaturing it in the public eye as a game they can "solve" or "beat" for headlines and valuation.

> Math, on the other hand, was paid for because people and states believed progress in math might lead to meaningful improvements in other branches of science, and, in turn, in our lives. If this is better served by AI, should we keep paying for the same number of tenure positions?

That is a pretty huge "if". The ultimate purpose of mathematics and really all scientific inquiry is human understanding of the natural world. It's not at all clear whether large language models can replace that any more than calculators can replace human mastery of arithmetic. Is society ready for engineers to design bridges and airplanes without understanding the underlying mathematics by simply handing off the entire process to a black box "AI architect"? Are people ready to ingest drugs "vibe-designed" by human drones pushing buttons on an "AI drug discovery" machine and just going "meh, seems about right!"

It might also be useful to take a step back from the hype that the frontier labs are obviously incentivised to incite. Before speculating about how "AI math" capabilities might supplant cutting-edge research in mathematics and other sciences, take a look at OpenAI's own job postings (https://openai.com/careers/search/?). Why doesn't OpenAI demonstrate its world-beating AI capabilities by automating more routine roles like "Account Associate", "Systems Architect" or "Android Engineer"?

Where do you draw the line between system packages and user facing apps? Some software defies such an easy categorization. If your default install doesn't come with docker and you install docker later for development, does that make docker a user-facing app? What about language toolchains like golang, rust, npm, etc?

> So let’s say OpenAI cure cancer and put every cancer researcher out of work depriving them of intellectual satisfaction, this would also be a problem? It would certainly impact the field.

Would you expect Fields medalists to cure cancer if you moved them from the math department to a medical research lab? This is precisely the fallacy that the frontier labs are counting on to inflate their valuation as their IPO approaches. They want to use headline-grabbing problems in pure maths to make their models look "smart" in the public eye. But what does "smartness" in mathematics really mean in terms of economic value? It is not at all obvious whether success in abstract mathematics should translate to successes and, more importantly, profitability, in more grounded endeavors.

Look at OpenAI's job postings (https://openai.com/careers/search/). Those roles involve far more pedestrian yet profitable duties than research mathematics. So why isn't OpenAI automating them with their vaunted models? Success in one field, no matter how "difficult", does not predict results in another field.

> This is a demonstrable fact as no human has figured this out despite the problem being open for almost 100 years.

That's not true. Alpoge and Buckmaster's related LLM-assisted blowup result (https://news.ycombinator.com/item?id=49605915) utilized a strategy developed recently by Cordoba and Martinez-Zoroa.

> I suspect that with six good mathematicians spending their whole careers primarily focused on it, and working together closely, Navier-Stokes might well have fallen already.

> The perverse incentives of academia mean this has never occurred.

This. Mathematicians in their most energetic years are trying to get tenure or land a tenure-track job. They are disincentivized to go all-in on ultra high risk, high-reward problems. The potential downside is just too forbidding. It's much safer to develop a research program in a mainstream field that affords many opportunities for partial progress that can translate to a robust publication record.

Suppose there is an oracle that can tell you whether a particular result in maths or physics is true. Would research still have any value?

If you transplant a world class mathematician into a chemistry lab, do you expect similarly ground breaking results? Do you expect a top chemist to make major advances in mathematics?

The frontier labs are likely betting on lay observers (read: investors) confusing headline-grabbing results in abstract mathematics with phenomenal profitability in more grounded endeavours. There is an implicit fallacy that "If our models can solve mathematics they can do everything else."

A "massive amount of AI-powered effort" costs a ton of money. What's the return on investment for these frontier labs? Do headline-grabbing successes in mathematics translate to expected *profitability* in disciplines with more immediately quantifiable economic value.

The scientist allegedly making that request comes from a machine learning background. Perhaps he's not familiar with the culture in mathematics regarding authorship. That sort of squabbling over author priority would be unconscionable to mathematicians.

They're also counting on more casual observers to extrapolate optimistically from successes in high profile math theorems to the company's economic value.

Hypo-dissipative NS != NS

> But Tristan rejects the first, why? Because he thinks it's theft? But then why would OAI threaten him?

Did the first offer also come with the outrageous condition that he exclude his co-author from the credit?

Buckmaster is already an established world expert at these sorts of problems. Declining the Clay prize would hardly dent his career.

This remains true: https://github.com/gorhill/uBlock/wiki/uBlock-Origin-works-b...