killerstorm

Russia could fully integrate into EU/NATO if it really wanted.

That's just not compatible with ambitions of its corrupt leaders who want to be a root-level player, not "one of NATO countries". And I'm not sure how legitimate is prioritization of wants of corrupt leaders over well-being of your compatriots.

Ordinary Russians think "yeah war sucks, I wish Ukraine surrenders so peace can be restored". I'm afraid only a tiny minority believes Russia needs to surrender.

Solving a well-known open problem like N-S "millennium" problem has some marketing value, as this can be referenced in a press release, etc.

OTOH a marginal value of a single paper in a paper dump is basically zero. Nobody gives a flying fuck if they made 100 or 200 papers. It won't give them even a single customer. If they released, say, 5 paper per month it would generate a buzz but not as much backlash from mathematicians.

The whole point of that paper is to show that it's possible in principle. Now people (and AIs) can think of better algorithms, etc.

Regarding elegance, take a look at Graham's number. It was not some meaningful constant - it's just a big-ass number which could be used in existence proof. Human mathematicians have been using this approach for quite some time, it's not really AI doing things odd

My man, there are more than 100,000 professional mathematicians in the world.

Are they all too busy having brilliant ideas? Doubt.

OpenAI math paper dump should be considered like a hint from 200 IQ eccentric genius - unreliable but perhaps insightful. If it was not "ugh AI" people would be happy about it.

> no one will pay them when a machine can do it.

Let's start with the fact that mathematicians aren't paid for results. Institution which gives them salary fundamentally doesn't give a fuck about theorems. They might care about having top-grade mathematicians for prestige, or because they believe that countries which are "good at math" also do better in science, engineering, etc.

> somehow the skill of building new theories in math is directly tied to slaving hard over a problem

We don't know if that's the only way. Perhaps collaboration with AI is just as good. Why reject it before it has been tried?

> what about junior ones

Junior guy with brilliant new ideas might benefit the most from AI as it can compensate for lacking technical chops and breadth of knowledge.

OpenAI does not make money from math papers LOL they just release them for the sake of community. (Because sitting on those results would be considered worse.)

> the grunt work of verifying, refining, and expanding on them

What work do you think mathematicians do normally?

Like they sit whole day and have ideas? And where are the ideas?

The way I see it, _some_ mathematicians enjoy solving puzzles, and now AI is better at solving puzzles.

This does not affect people building new theories.

Also, it's quite prestigious to write a _book_ on some topic. And guess what writing a book entails? Refining and expanding. What you call grunt work.

IIRC Google had an experimental project with this kind of stuff 2 years ago, but Google being Google...

I'd recommend Lem's _Fiasco_ as thematically related. Very different writing style, obviously.

Also Greg Egan's "Learning to be me" and "Closer"

The book is provocative on purpose:

a person with "no emotions" spends pages and pages describing his subjective experience and feelings.

So much more than a normal sci-fi book. I'm not sure how we are supposed to interpret it. That it's all fake? That AI worked well and he's like a normal person, but too obsessed with a question whether it's fake?

No. It was about possibility of intelligence without consciousness.

When AI proves theorems, it uses divide-and-conquer approach just as humans - it breaks a big theorem into lemmas and tackles lemmas one by one.

An alternative approach where it is just one big-ass logical expression is just not better.

Same thing with code, I think - you need some intermediate results like a calling convention, helper subroutines, etc.

A sufficiently powerful AI can do compilation "mentally" - i.e. producing machine code conforming to a specific calling convention. It can also decompile machine code. But you, obviously, don't gain anything doing it this way, if there's one-to-one correspondence between high-level code and machine code. You might as well just write high-level code.

I really hope that software becomes more efficient. But I don't think that it can only be done by generating machine code directly.

Related: "Recursive Language Models" https://arxiv.org/abs/2512.24601

Mathematicians can only make these demands because they believe that AI labs get a lot of credibility from math results. So they propose a trade: we give you credibility, you give us funding and let us to gate-keep.

The proof itself can be studied, either with AI or not.

"Intermediate reasoning traces" might be interesting but they are not by any means requited.

This letter has nothing to do with reasoning traces anyway: they just don't want math research to be front-run by internal models, that's all.

?

Are you confusing "uses solar power" with "could rely solely on solar power"?

And norther areas of what, exactly? I mean that there are areas where nuclear power would be useful (as it can displace coal/natgas).

There's not much sun in the northern areas in winter. Storage might cover 12 hours worth of usage, not 6 months of usage, including all the heating.

I got some extremely long responses from 'ChatGPT' yesterday - e.g. 10+ pages long essay whereas other models reply with 1-2 pages to the same question.

I wonder if it's a regression of 6.x Sol. They removed model indicator from 'chat' section, so it's now mystery meat model.

Also, I'm generally very angry that we don't have "this response was generated by XXX" for each response because that's pretty damn important.

There's no "imminent" danger, but right now we are on a course where ASI will be developed. There are many reasons to believe that ASI will be dangerous. There are some books you could read...

To a guy who knows nothing about nuclear physics, an atomic bomb is very abstract until it blows up and becomes very concrete. Should we wait until it explodes to consider the risk?

No, layer looping increases effective depth, but it still has to go through decode. So it's more like they increased number of layers from 100 to 200 without increasing number of parameters.

"Latent reasoning" is rather trivial - you can just replace unembed-embed step with a MLP. But labs don't do that largely because they want to read the output of unembed.

There have been a huge progress in AI reasoning in the past 2 years. GPT-4o mentioned in the article would struggle with high-school math problems, OTOH GPT-6 can solve problems beyond capability of professional mathematicians.

I'd wager GPT-6 would not depend on high-quality prompts, although it might still be good to get a trained person to enter information and do a sanity check.

There are several papers demonstrating AI is at least as good at diagnostics as fully qualified doctors. So why would NP + AI be worse? AI should compensate for the lack of knowledge.

And I'm not saying NP + ChatGPT - it should be properly calibrated system which would defer to a 'proper doctor' in more complex cases.

You need to train data for a BERT-based classifier, and then there's a risk that it will pick up specific biases from the data instead of what you want.

As far as I understand, the idea of Jev is zero-shot or few-shot classifier: it learns a lot of stuff at pre-training, but unlike a classic LLM it doesn't need to learn how to chat, so it can be much smarter at a particular size

Education system was set up in XIX century. It's not clear how much of it is necessary in XXI century.

Back in the day religious books were copied by scribes educated in a monastic tradition. Now printers can print them in a completely godless manner but the result isn't any worse.

It's low because there's no competition. MDs are like medieval guild: once you're in, you're set for life. Restrictive regulations are lobbied by MD associations, which limit competition.

Probably better solution is to upskill nurses + AI to do handle all the simpler tasks like prescribing standard treatments, etc. There's already a concept of mid-level practitioner which can be expanded.

There's basically no need for GP to be a doctor.

> my ideal sandboxing is "prevent writing to anything outside this dir but still allow reading to most things so that I don't have to manually copy things into a container/VM"

That's what Codex does out of the box, and it's not good against malware - i.e. a rogue npm packet (or even just codex after prompt injection) can read your ssh key and send it to the attacker.