curt15

Something like the old folding@home distributed molecular dynamics calculations?

Safari has the same manifest v3 limitations that preclude the full-blooded ublock origin you get with Firefox.

It doesn't ship with a blessed adblocker, but firefox + ubo is still the gold standard.

When LLMs are given a classification task like that, are they doing all the analysis natively or invoking classical clustering algorithms with tools?

Is the C-suite ready to sack the engineers and prompt Claude themselves with their business requirements? Why not cut out the middlemen if that's how they now perceive engineers?

> Love the implication that them being Chinese is the moral dilemma lol.

The moral dilemma is not with ethnicity but rather about the CCP.

> But if it does not solve it, will you really wager 30 years of your life on the possibility that you might be smarter than the AI?

The "AI" is trained on centuries of human theoretical frameworks and innovations. The "AI" can also work on problems full-time without teaching, admin, or mentoring duties. OpenAI spent millions to race human researchers on Navier Stokes, and that doesn't account for any training costs. How many person-years would that pay for? Since the key ideas were developed by humans just a few years prior, it's not unreasonable to surmise that a comparable level of total effort by humans would also have decent chances of breakthrough, with the additional benefit that the insights uncovered along the way would be immediately digestible and easy to communicate to other mathematicians.

OAI is still hiring lots of humans for business and engineering roles instead of automating them with their best-in-class AI capabilities (https://openai.com/careers/search/). What does it tell you when they apparently don't "dare to use" their own models?

The frontier labs themselves are still hiring hordes of human "account associates", "android engineers", and "data scientists" (https://openai.com/careers/search/), when they would be better positioned than anyone to leverage state-of-the art AI capabilities.

"How and whether specific mathematics might lead to improving the world (whatever that means) is usually impossible to tease out, but mathematics collectively is extremely important."

(https://mathoverflow.net/questions/43690/whats-a-mathematici...)

To extend the software analogy, asking mathematicians to decipher LLM-generated math results is like asking programmers to understand computer programs using a decompiler instead of starting from human-comprehensible source code.

> They're clearly cost effective for frontier AI firms

It's not obvious that this is a given. "Cost-effective" implies a comparison between cost and output. OAI spent millions to race human researchers on Navier Stokes, and that doesn't even account for the training cost. And how does one value the output? OAI is for some reason still hiring armies of humans instead of automating roles like "AI support engineer" or "Product Designer" (https://openai.com/careers/search/).

Calling the proofs a "side-product" is also rather dubious when OAI employs a team of mathematicians specifically to train its theorem proving capabilities.

The accounts spreading that narrative on X no doubt have their own agenda. Before people start hyperventilating about "AI" conquering human endeavors, remember that the cash-strapped frontier labs are themselves still hiring human "Account Associates" and "Android Engineers" instead of saving those salaries using their own AI capabilities.

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

While studying neural networks empirically like biology is one possible approach, there's no reason for that to be the blessed approach other than a combination of inertia and current lack of understanding. It's been only 15 years since AlexNet. Scientists struggled for centuries to model atoms before developing quantum mechanics and later QFT as an accurate quantitative framework.

Also, while biological systems simply exist in nature, artificial neural networks are ultimately mathematical objects with various properties that have yet to be uncovered.

> The companies and partners need to maximize payout. They go on this track of cost reduction via layoffs and basically saying - “the model can do everything”. They know it’s not the case yet they still flood the airwaves and cause fud among all clueless c-suite executives. Which is the goal to begin with.

While frontier labs are supposedly on track to conquer human endeavors, they are somehow still hiring lots of human "Account Associates" and "AI support engineers" instead of automating those jobs as a demonstration of their AI's economic value (https://openai.com/careers/search/).

> It won't matter, because it won't be us who will be asking the next questions anymore. Whether in math or anything else.

That's the frontier labs' preferred narrative while they themselves are still hiring hordes of human "Account Associates", "Android Engineers", and "AI support engineers" instead of automating those jobs as a show of their AI strength. Of course it will be "us" asking the questions, because "AI" are computer programs, and humans build the computers and choose what computational tools to use for any application.

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

Is that a satisfactory state of affairs? "Experts" also prescribed thalidomide for years before embarking on a decades-long scramble to unravel its teratogenic properties. "Experts" also initially prescribed ivermectin and hydroxychloroquine for Covid-19. While empiricism is an important part of medical sciences, medicine is not a purely empirical subject because pure empiricism is insufficient in the long run.

The software analog of settling merely for "it seems to work" would be if someone were to hand you a bare binary whose behavior must be inferred entirely through black box testing, without source code, architecture diagrams, or any other documentation. Sure, users can try running it in a pinch, which is the case for various medications, but it's surely possible to do better in the long run.

Initial discovery in medicine is largely "try and see what seems to work". But there are many reasons why scientific inquiry just doesn't stop there and also seeks to understand the underlying mechanisms. For example, a naturally occurring product may be too difficult to harvest on a large scale. Or, a sample might not be sufficiently representative to uncover potentially deadly side effects or drug interactions. Humans are fundamentally unsatisfied to take everything purely on faith.

> This argument is so silly against reality, and already, almost nobody understands the things they put in their body to any significant degree, beyond the effect produced.

While the biological effects of any particular chemical still require a great deal of trial and error to determine, drug chemistry itself is unambiguous. Pharma companies employ expert chemists and chemical engineers. They don't manufacture drugs just by randomly mixing together chemicals. At the very least, they must understand what they are making thoroughly enough to determine its physical properties and design a reliable and commercially viable manufacturing process.

Would you take an "AI-designed" airplane where no human understands its physics?

Would you drive on an "AI-designed" bridge unvetted by expert engineers?

> The Navier Stokes results used millions of dollars of tokens and thousands of parallel agents to get the result.

Not to mention a century of theoretical foundations and innovations by humans, written for human understanding.

If this is an intentional move, Apple is just acknowledging reality. UNIX(tm) is no longer so relevant compared to the early days of OS X, when Linux was still in its infancy and the Unix certification gave Apple street cred among developers. Most developers these days target Linux for production. That's why Microsoft developed WSL, not WSU.

Isn't it curious that while LLMs are apparently on the precipice of "AGI" and possessing superhuman capabilities for knowledge work, the frontier labs are still hiring armies of humans as "Account Associates", "Account Directors", "AI Support Engineers", and "Applied AI Engineers" (https://openai.com/careers/search/). Since those roles are much more pedestrian than top math problems, one might expect the frontier labs to be at the forefront of automating those jobs using their world-beating models. Wouldn't that be a more direct flex of their economic value?

The most motivated students don't need help. They will always find ways to grow with or without their teachers.

> Mathematics was already not financially well rewarded,

Then why are frontier labs ploughing millions into racing human math researchers when they're already cash-strapped? What's the ROI for that?

> But leaning computer science using LLMs all the time will be like learning to ride a bike using training wheels, and never taking them off.

Or like learning to add fractions by looking up the answer key instead of actually struggling to figure it out.

How do you prevent them becoming like students who never get comfortable with fractions because they punched all of their homework into wolfram alpha?

The amount of money they're lately ploughing into proving math theorems is inconsistent with how societies and markets have priced pure mathematics. The entire US federal budget for math research is something like $100M annually. A single college football coach can already earn 10 percent of that.

Pretty much the only enterprise that historically pays some mathematicians handsomely is quant finance, but those people are actually compensated not for proving theorems but rather for statistical modeling and programming skills. And even that industry is so technologically driven these days that pure research mathematicians no longer hold a clear edge over strong programmers with undergrad level probability and statistics at their fingertips.

That's what math has always been about, even if not everyone is necessarily as talented a communicator as someone like Thurston -- who was himself an exponent of computational tools in math research:

>I think of mathematics as having a large component of psychology, because of its strong dependence on human minds. Dehumanized mathematics would be more like computer code, which is very different. Mathematical ideas, even simple ideas, are often hard to transplant from mind to mind....Translation in the direction conceptual -> concrete and symbolic is much easier than translation in the reverse direction, and symbolic forms often replaces the conceptual forms of understanding....

https://mathoverflow.net/questions/43690/whats-a-mathematici...

> If the models can do this, they're almost certainly good at just about everything, because the reasoning and creativity required to solve these problems will translate.

The "then" in your "if-then" bears a heavy load. Why would society assign so little economic value to pure mathematics if the skills for proving math theorems translate to massive value in "just about everything"? Would you expect top mathematicians to cure cancer if you transplanted them from the math department to a medical research lab?