I'd honestly be a little surprised if there's much protective effect.

It's possible that having to go through the setup process yourself is a little too much seeing how the sausage is made vs. interacting with a pleasant frontend cynically put together by someone who knows how 'engagement' works; but it's not like the nerd reputation for skewing socially awkward or the AI bro reputation for reaching a bit too quickly for slightly mystical anthropomorphisms are entirely unearned(the 'soul.md' is a frankly somewhat depressing genre).

At least for now; there might be some confounding demographic effects if you are talking one of the chunkier local models; in a country with a per-capita GDP of ~$14k being able to comfortably afford, or willing to uncomfortably afford, the necessary hardware would make you either at least modestly wealthier than average or significantly more interested than average; but as you slouch toward stuff that runs on more typical hardware the demographic differences presumably decrease.

Yeah, there's two main problems:

1) People entering the wrong fields. For example, medicine really needs workers, at all levels, but not enough people are going into it.

2) Certain manual labour fields, like field work and home construction, because... well, I think we all know why there's a shortage of workers in those fields.

Merit should always consider strictly value.

I don't want to diminish the accomplishment; that seems like a very cool dataset and probably one that was really fiddly to pull together; but, if you are talking single-neuron resolution; I am curious about whether you can still call an individual sample "the human brainstem" rather than "a human brainstem" and what comparative purposes you can use it for without running into trouble with cases where there are multiple ways for a brainstem to be adequately healthy, so long as certain requirements are met, so you'll need considerably more samples to draw useful inferences about exactly what the problem abnormality is.

Same sort of thing as when "sequencing the human genome" was a big project. Obviously a major exercise in gene sequencing and a basis for situating subsequent sequencing operations; but once you start talking detail there isn't 'the human genome'; literally everyone has one; and it turns out that different differences matter or don't at radically different levels.

Presumably the methods used to do it once will be helpful in doing it more often in the future; but I'll be curious what we discover about the balance of 'normalcy' vs. some relatively subtle and confusing combination of surprisingly variable ways to have a brainstem that seems to work just fine along with surprisingly subtle, no ghastly big lesions, ways to have one that ends up being totally dodgy.

I find "NOTE: Experiences vary by region." to be a bad sign for something that would be so trivial for MS to alter the behavior of; and where they are obviously not earnestly making improvements that were previously impossible but grudgingly rolling back bullshit they thought they could get away with.

Probably means good news for users in the EU; same way they get left out of some of the most egregiously bullshit 'AI' stuff; may help EDU and enterprise; but I'm guessing that it's no promises for less favored users.

Or let's put this another way. Show of hands - how many of you "spicy autocorrect" / "stochastic parrot" people had "AI will start mass-solving Erdos problems" on your forecast list a couple years back? Huh, none of you? Fascinating!

Take some time to reassess your priors. And while you do so, understand that, yes, they are doing logic / reasoning.

They weren't discovered by an LLM. They were known conjectures that were proven by an automated solving language that was linked to an LLM.

I'll take "Things That Didn't Happen For $200", Alex.

Only a handful of meaningful proofs have ever been done by automated formal theorem solvers (the Four Colour Theorem being the most noteworthy example - but its proof is so long that humans can't verify it). By contrast, AI tools have been solving Erdos problems en masse. The majority of them just bog-standard commercial models. In case you need help, the only ones on that list that were hybrid (AI / non-AI) in the actual solving phase are:

1) AlphaProof / DeepMind Prover Agent / AlphaProof Nexus
2) Aristotle (Harmonic)
3) Seed Prover / Seed Prover 1.5 (ByteDance)
4) AxiomProver (Axiom Math)

In each of the above, LLMs come up with the lemmas / strategies but then use Monte Carlo search ("brute force") or likewise to investigate what they came up with. These are a minority. In the "AI Standalone" category, these "hybrid" tools made up only ~20% of attempts and successful proofs. Hybrid tools actually made more of a contribution in the "AI Alongside Literature" (related literature found afterward) and even more of the "AI Building On Literature" (related literature known beforehand) categories, which is the opposite of what people like you expect.

And even with the hybrid tools, it's still the AI doing the heavy lifting when it comes to strategy. Non-AI theorem solvers, again, don't have a spectacular record for churning out novel proofs to unsolved problems. Tools like Lean are more about mathematical rigour - a passive environment that requires a driver (a human or AI) to feed it actual strategies, lemmas, and proof steps. And no, you cannot brute force "strategy" in the vast majority of cases, which is, again, why automated theorem solvers don't have much of a track record with unsolved mathematical problems.

Let's take a random example: the disproof of the unit distance conjecture. It was solved purely by a general purpose commercial GPT model, not custom-trained to mathematics, with no external tools. Read what the various mathematicians reviewing / commenting on it have to say (sections #3 and onward). Seriously, don't skip reading them, actually read them. This was one of Erdos's favourite problems. He mentioned it commonly in his lectures. Essentially every mathematician working in complex geometry has thought about this problem. The approach that the model came up with was highly novel approach, based on CM-fields and class field towers.

I know you don't want to accept this reality, but it is the reality, so you better improve your ability to accept it,. The field of mathematics is already doing so.

Dang, link didn't post.

Ah, good 'ol "Model Collapse" theory that people have been pounding on for years now, predicting an imminent collapse in model capabilities. How has that been working out for you?

Except what the US is actually facing, at least in the near term, is just the opposite, a worker shortage.

You can't pay royalties to the entire internet. That's not realistic.

What is practical are things like taxes to fund public benefits, or requirements of returning things to the commons (for example, open models).

"The notion that there is a shortage of physicians is preposterous - the residency programs will continue to be filled every single year."

How on earth are those two things contradictory? The problem is there are too few residencies for how many physicians we need.

You're the one making the claim that modern frontier models still make this mistake (they don't), so how about you go to a frontier model, reproduce what you're claiming happens, and click the "share" button and post the link here?

Don't worry, I'll wait.

Your inability to reproduce the thing that you claim happens is duly noted.

You are speaking irrelevant nonsense. LLMs are trained in words

They are not. They are trained in tokens. Tokens do not align with word boundaries, and an arbitrary word can be tokenized in many different ways.

and they think in words

They do not. They don't even think in tokens. The process is: words are split to tokens, tokens point to an embedding position (latent space) while RoPE encodes a relative position, and all reasoning is done within latent space, which is not at all verbal (concepts are directions in latent space, and math is done on concepts, not words).