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Why I'm still bearish on LLMs after Navier-Stokes

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Re: Why I'm still bearish on LLMs after Navier-Stokes

#221

Post by tossandthrow »

wodenokoto wrote: post #219
tossandthrow wrote: post #210

Earlier quoted context omitted.

Llm systems are not really build for adhering to a grammar (other than "a string og tokens"). It is also not clear whether the llm adhering to a grammar is necessary for intelligent agents. Certainly,a harness can easily correct for it.

It seems absolutely crazy to me to expect an LLM to code a solution to a problem while also not expecting it to be able to adhere to a grammar.

Why?

You might never have tried to program before, so I don't blame it on you.

But most programmers, even experienced ones, see grammar and type errors regularly.

Re: Why I'm still bearish on LLMs after Navier-Stokes

#222

Post by orwin »

svachalek wrote: post #154
manquer wrote: post #63

Earlier quoted context omitted.

> People who are good at it rely more on experience and deep domain expertise People are good are 1900 or 2100 above and the top ones who spend decades in the field i.e. deep expertise are well in the 2200-2700 range. A 1100 player is none of these things, they are purely relying on strategic reasoning there is a good chance they cannot name a single opening or articulate clearly why a move was appropriate. 1100 is q…

1100 at online speed chess or something, could be. I'm not that deep in the chess world but everyone I know that can make 1100 in official rating can name a dozen openings and most of the known tactics, and is pretty good at applying at least one opening.

1100 lichess/chess.com does not represent real elo. I'm around 1400 online, I would still be unranked in the real world. The fact that I easily beat any model publicly available is not a great look for AGI.

Re: Why I'm still bearish on LLMs after Navier-Stokes

#223

Post by utopiah »

utopiah wrote: post #217
yshklarov wrote: post #209

Great article, but the lack of sentence capitalization makes it unnecessarily difficult to read. Apologies if this comment is off-topic, but it really is quite egregious, and since the article was submitted by the author I presume they are open to the feedback.

Array.from(document.body.querySelectorAll('p,li')).filter(e=>e.innerText).map(e=>e.innerText = e.innerText.split('\. ').map(s=>s[0].toUpperCase() + s.slice(1)).join('. ') ) Not perfect but hope it helps.

~Gosh, getting downvoted for trying to help a stranger, what is wrong with you people?~ thanks for the upvotes, guess was just a misclick.

Re: Why I'm still bearish on LLMs after Navier-Stokes

#224

Post by orwin »

hackinthebochs wrote: post #123
what wrote: post #96

Earlier quoted context omitted.

Humans don’t code a $game engine to play $game, they can just play it. It seems like you are the one that has gone insane.

And how many years of direct play and study does it take for a human to get good at chess or any other game? Absolutely no human ever could be good at chess just by reading a few books, or even every book on chess. That's just not how the brain works. If LLMs could do that they would truly be superintelligence.

A week. My brother learned and was above 1100 online within 12 hours, after a few hundred games.

Re: Why I'm still bearish on LLMs after Navier-Stokes

#225

Post by orwin »

willmarch wrote: post #82
skydhash wrote: post #72

Earlier quoted context omitted.

Pretty much this. Feed it a book or two on chess, and you should have a decent (or good) player. That's the generic intelligence people have. The aims is not to be supremely talented at something, but being able to read a manual and figure how to use/play something. Mastery can be gained overtime.

If you gave a human a book or two on chess they would not become a decent player (they would be closer to 500-600 than 1100 ELO) and they would only get better after playing hundreds or thousands of games (often making illegal moves and moves that violate the rules of chess as they learn). Your assumptions/intuition about generic human intelligence feels quite incorrect, considering LLMs currently play better than a…

That's quite untrue. I taught my (adult) brother the moves, the only illegal move he ever tried against me (over his 6 first games) was a castle with a rook that already moved twice. Within a few hundred games (less than 500 for sure, he played 3 minutes blitz but always took at least 10 minutes analyzing his games) he was rated 1100 on lichess (which is like 1050 on chess.com and unranked in the real world).

Re: Why I'm still bearish on LLMs after Navier-Stokes

#226

Post by andrepd »

TrackerFF wrote: post #196

The challenge with estimating abilities, is that we don’t know what the models can achieve if we just burn enough money. The navier-stokes shows us what mathematical problem can be solved when $10m worth of compute is thrown at something. It also makes one wonder: What could AI solve if we managed to orchestrate billions worth of agents to take on a specific problem? IMO the very best case scenario / potential for th…

> The navier-stokes shows us what mathematical problem can be solved when $10m worth of compute is thrown at something.

That's the thing, it very much does NOT show us that. What happened was mathematicians at openAI learned of an imminent development on this problem, and the insight that it entailed, then they were able to prompt a system in the correct direction and spend 20 million dollars to write down the final steps.

Which is rather precisely the point that the article is making!

> If you knew AI could be affordable enough in 3-5 years so that even the most underfunded researchers could use it to solve cancer

As the saying goes, if my grandmother had wheels she would have been a truck.

Re: Why I'm still bearish on LLMs after Navier-Stokes

#227

Post by utopiah »

Tired of that trope, I already wrote it before but "Those who can accept failure cheaply: firms that would otherwise hire interns, firms involved in rapid prototyping work, etc." is not correct.

I won't comment on hiring interns as that's not my expertise (even though if you want to teach your staff, obviously I can see a problem there) but I can comment on rapid prototyping, it's what I do.

Rapid prototyping is NOT making a CMS quick. It's not about making a quick mockup of a UI. It's not about making yet another well known... anything. The entire POINT of prototyping is to make something NEVER done before. Typically that means you are reaching the frontier. You are making something with NO documentation to rely on. You are using tools, hardware or software, which do NOT have tons of StackOverflow errors. There is no dataset to crawl, there is no well structured Q&A database to train on. You have to poke and see if the thing actually works as expected, and it often does not.

So sure, if you are using interns as a trick to underpay your staff, or if you are using prototyping as an excuse to build poor quality software fast, maybe it does help. If you are genuinely prototyping, it breaks fast and the supervision overhead makes it pretty pointless, especially since typically it's by actually implementing that you find out not just how the new setup works, but also its limits, and thus the actual needs of the project, not the one the stakeholder imagined would be.

So not, not for rapid prototyping either.

TL;DR: prototyping is a learning process, not a low fidelity output.

PS: this comes up very often from NON prototypists that I wrote a short piece about it https://fabien.benetou.fr/Content/GoodPrototypesAre10LinesLo... so much so that it feels like a pattern "GenAI/LLMs is good for tasks X" while the author actually does not do task X except very superficially.

Re: Why I'm still bearish on LLMs after Navier-Stokes

#228

Post by [deleted] »

21asdffdsa12 wrote: post #205
joefourier wrote: post #31

Earlier quoted context omitted.

> current frontier models > Gemini 2.5 Pro, O3, Claude Sonnet 3.7 and ChatGPT 4.1 The gap in capabilities between those models which they tested, and actual current frontier ones is enormous. I would not trust that any conclusions they made are applicable.

So give me a falsifiable point in time, a model you would claim succeeds at the task. One does not get to hotfix-patch updater out of the pressures of reality. Today is the day.

[deleted]

Re: Why I'm still bearish on LLMs after Navier-Stokes

#229

Post by Lio »

yshklarov wrote: post #209

Great article, but the lack of sentence capitalization makes it unnecessarily difficult to read. Apologies if this comment is off-topic, but it really is quite egregious, and since the article was submitted by the author I presume they are open to the feedback.

Yep, I had the same thought. As simple heuristic, text written in all lowercase is often just hot takes and so not worth taking the time to read.

LC;DR :P

Re: Why I'm still bearish on LLMs after Navier-Stokes

#230

Post by olmo23 »

utopiah wrote: post #227

Tired of that trope, I already wrote it before but "Those who can accept failure cheaply: firms that would otherwise hire interns, firms involved in rapid prototyping work, etc." is not correct. I won't comment on hiring interns as that's not my expertise (even though if you want to teach your staff, obviously I can see a problem there) but I can comment on rapid prototyping, it's what I do. Rapid prototyping is NOT…

I don't understand this comment. We use AI for prototyping all the time. We write ERP software. Client wants to know how process XYZ could be automated? Send them a prototype UI (sans the three A's) so they can play around with it. Takes 30 minutes.
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