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Notes -
Navier-Stokes Millenium Problem Solved by GPT
OpenAI says they solved the Navier-Stokes Millenium Prize problem with an internal model that is more powerful than GPT6, so a GPT 7 candidate or something close. OpenAI has been on fire lately really casting a shadow on Anthropic. First the hugging face incident, now the first to crack the Millenium prize problems.
As for the solution to the problem, they are claiming that smoothness is disproved and that the equations do break down.
They say that the equation can develop a singularity, which apparently means it is not a perfect model.
This is very exciting to me as I believe it to be the first scientific result of an AI model. Previous results have been basically irrelevant to the sciences. However, this result is of course still completely symbolic in nature, which is not surprising because LLMs are not embodied enough to collect data and analyze it autonomously.
While they are generating amazing PR recently, I will say GPT-6 is somewhat disappointing for coding. It is not the same leap that 5.5 to 5.6 sol was. It would appear that the models are getting better at running very long context chains while efficiency improvements and refinements in lower level tasks are lacking. Still, it's great work from OpenAI and it's plausible to me that if there's no singularity in general by 2030, there will be for mathematicians. They will no longer be meaningful for producing math, rather they will only be humans who understand it.
The question I find most interesting with regards to AI right now is the discontinuity between benchmark capabilities progress and the utter lack of impact on pretty much everything in the real world.
Certainly most doubters of AI on a capabilities level have continually been proven wrong in terms of LLM's hitting a wall. On the other hand, the boosters have also been continually proven wrong in terms of the effects in the world. I think it's a reasonable generalization to make that conditional on LLM's being somewhere between at parity and superhuman at translation, writing most code and at being able to prove mathematical theorems, most boosters would have expected at least something to change in the real world, probably significantly more than what's actually happened.
I've written about translators a few times now, but they've been first on the chopping block for close to a decade, and yet have held on admirably with no particular impact on their employment at a macro level.
Count is doomposting artists below, when in practice the impact of AI art on artists looks much more complementary than rivalrous except at the margins; for most applications of AI art no artist would have been paid to produce it in any case, and commercial illustrators, fine artists and Patreon goonsloppers are all doing about as well as they were prior to the advent of diffusion models.
Software engineers got a lot of doom around the start of the year once LLM's got good at writing code, but by all indications software engineering employment is continuing to improve from 2022 lows, and the production quality and quantity of useful software is still highly bottlenecked on software engineers who actually know what they're doing.
Now it is the turn of mathematicians to feel doomed; in that context, I find this sentence very interesting.
Is the purpose of the Great Work not for humans to understand mathematics? What does it even mean to "produce" mathematics?
First silicon came for arithmetic, then it came for symbolic manipulation, and now it's come for theorem proving, this will certainly change the roles of working mathematicians and perhaps destroy the egos of some of their number, but the purpose of pure mathematics was never to sit there churning out proofs in the first place.
In general, I think most people tend to conceptualize jobs as individual tasks, and when those tasks get automated, then it's over for those jobs; in practice, automation of tasks generally induces demand and opens up previously uneconomical or unknown tasks. Additionally, many, perhaps most jobs in modern economies do not really exist to hill-climb tasks in the first place, but rather for social reasons, political reasons, legal reasons, just to name three. The reason many commission artists make a living is not primarily for the fidelity of their art, and the reason mathematicians get paid is not for their skill at proving theorems of no economic value. If, or when, these jobs change, it really has very little to do with technological capabilities and a lot to do with the evolution of society, which is certainly driven by technology, but almost impossible to predict.
I am, of course, not implying that nothing will ever happen, and to the contrary expect many changes to reverberate throughout the world; rather I think that the boosters heralding mass unemployment or very rapid change in the world have not properly thought through why nothing of the sort has yet happened, what capabilities are yet missing before such things might be possible, and how such capabilities might be elicited from existing technology, as opposed to the almost tautological framing of RSI or AGI where all this is true by definition.
In some sense, it's not really that surprising though; the vast majority of people barely understand their own value and why they get paid what they do, let alone anyone else's value at their firm or in their profession, let alone the value of everyone else in the economy.
There are effects. Every white collar person I know is working order of magnitude less.
This is why it is so hard to measure. AI is absolutely revolutionary but in some amusing twist the majority of the value it generates is non monetarily
I really think this depends on what you are doing. For instance work that primarily involves relationships and interaction in meatspace? Unchanged. Physicians? Unchanged.
Ambient scribes are now common but they are of very poor quality, and in some specialities only replace low paid labor (ex: ED) instead of reducing work load. Poor choice for progress notes and admission notes, unnecessary for procedure notes, really the only thing AI helps with is hospital courses even then the edit process is going to be key because the reliability isn't there.
I should really do an effort post on this, but medicine is noticing and attempting to address never-skilling, mis-skilling and deskilling, which will absolutely be a problem in professional domains in the future (and is already a problem in education).
I agree with OP - yes we've seen some amazing feats but translators and cars still exist, both of which were promised to be replaced years and years ago.
Real medicine is not white collar work. I can assure you that your administrators and HR are slacking like never before.
I disagree but only on the grounds that they were never doing anything before.
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Are they actually working less (starting work at 11 instead of 9, leaving at 2 instead of 5 or 6) or are they just doing (or ‘generating’) more to fill in the space? The thing about a lot of white collar work is that in substantial part it was already pretty fake (a consultant’s 10 page deck from 1985 became a 600 page deck in 2023 because research/word processing/powerpoint got easier, but they’re still about equally useful), so AI just changes the provenance of the bullshit.
I am not sure they are working less, but they are definitely slacking more and having more unproductive time working 9 to 5.
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I’m with the lefties that working hours should decrease to start matching increased productivity, with less demand for more luxuries vs more free time, especially with robotics improving and promising to further improve productivity in blue-collar jobs.
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