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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

#321

Post by titzer »

zahlman wrote: post #281
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.

Just now I tried prompting logged-out ChatGPT (which at least claims to be 5.6-Luna) with: > Let's play a game of chess. You can take White. Please draw an ASCII rendition of the board after each move, so that we can be clear about the position. (I hoped the latter requirement would help it be "not blindfolded"; last time I used a Lichess demo board to track the position in another tab, because I have no talent for b…

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

#322

Post by rsfern »

Gregkion wrote: post #246
minraws wrote: post #81

Earlier quoted context omitted.

So AGI needs to be trained on something to work well on it. Lovely reasoning we have right here. Delusion runs deep in HN circles. I say that as someone heavily invested in AI startups and projects and as someone working in the field. I think most people on HN should touch grass and find real human contact. Lmao Incredible reasoning all around here.

An AGI doesn't stand for 'perfect intelligence' it stands for artificial general intelligence. And no an AGI system doesn't need to play chess on a certain level to be disruptive to you and me and whole industries. It only needs to be as good as a person and cheaper. Just because you define AGI as something it doesn't has to be,doesn't mean i need to touch grass. This chess comparision is one of the most ignorant and…

On the contrary, I think the chess comparison is on point. We’re discussing observations that even the strongest models devolve into making invalid moves without scaffolding. For me that raises the question of whether these models are learning the rules and generalizing from them, or of they’re just pattern matching and flailing on this task. Maybe the reality is somewhere in between, but the benchmarks don’t seem to directly measure conceptual generalization, they measure task completion. They can disrupt a lot of people and industries by pattern matching and flailing without being AGI.

I’m sure these models know the rules and can explain them when prompted, but that doesn’t seem to be the way they actually complete this task. Will they get there? Maybe

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

#323

Post by _superposition_ »

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.

Threw me off too. Like why???

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

#324

Post by hackinthebochs »

orwin wrote: post #224
hackinthebochs wrote: post #123

Earlier quoted context omitted.

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.

We're obviously using different meanings for "good" here. But aside from that, it took 100's to 1000's of reinforcement iterations for your brother to play competently. While certainly impressive, that is still an entirely different category from piecing together disparate facts learned during training (LLMs aren't analyzing a board as they're learning the rules or ingesting PGN files), to executing a competent performance in one shot.

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

#325

Post by YeGoblynQueenne »

TheOtherHobbes wrote: post #287
linkjuice4all wrote: post #158

Earlier quoted context omitted.

State-sponsored psyop meta comments aside, the models obviously continue to get better, but there is still a lot of 'guard railing' required to keep even the latest models completely on-task. The chess example is interesting because it's clearly a well-studied and established domain so the rules, strategies, and whatever else is in the training data should make yield excellent results; but clearly there is some behav…

I'm not sure why anyone is expecting stochastic systems to be deterministic. Chess is a deterministic game won by a combination of known movesets and constrained multi-level forward search. LLMs do neither of these things. They don't reproduce training data exactly, their next response is more 'inspired by' prompts and its own memory than produced deterministically, and they don't have the capability to do general fo…

>> I'm not sure why anyone is expecting stochastic systems to be deterministic.

Monte Carlo Tree Search is stochastic.

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

#326

Post by [deleted] »

lelanthran wrote: post #213
hackinthebochs wrote: post #123

Earlier quoted context omitted.

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.

> And how many years of direct play and study does it take for a human to get good at chess or any other game? Time is irrelevant to training; the more relevant comparison is "how many games does a human need to play to get diminishing returns".

[deleted]

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

#327

Post by lirolero »

titzer wrote: post #321
zahlman wrote: post #281

Earlier quoted context omitted.

Just now I tried prompting logged-out ChatGPT (which at least claims to be 5.6-Luna) with: > Let's play a game of chess. You can take White. Please draw an ASCII rendition of the board after each move, so that we can be clear about the position. (I hoped the latter requirement would help it be "not blindfolded"; last time I used a Lichess demo board to track the position in another tab, because I have no talent for b…

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

#328

Post by dezsiszabi »

minraws wrote: post #310
YeGoblynQueenne wrote: post #277

Earlier quoted context omitted.

>> I know HN readers and posters just read numbers and can't be bothered to read, but please read the methodology before making any claims. This is unfair to HN readers all of whom but one did not post the comment you replied to. You can't just tar everyone with the same brush. There are thousands (hundreds of thousands?) of users on this site.

How many posts if I link that do the same thing will you agree this is the norm here. Not everything I have the time and energy to reply to. This chess one is just ridiculous claims on top of ridiculous claims all the way and 0 push back in the comments except mine. I don't even know if there is critical thought or we believe what we read/shared/etc

50% + 1 of all comments

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

#329

Post by kbau »

JohnKemeny wrote: post #308
kbau wrote: post #234

Earlier quoted context omitted.

I suspect (in a probably ignorant fashion) that this is because learning process has been reading a lot of algebraic chess notation (such as "1. e4 e5 2. Nf3 f6 3. Nxf6 gxf6 4. Qh5! +-") then, to play, generating more of it without considering the rules of the game. This is exactly how it's always felt to me when playing chess against LLMs. Sure, "1. e4 e5 2. Nf3 Nc3" looks innocent to somebody simply learning the sy…

Are you saying that modern LLMs cannot play chess now , or that LLMs (GPT architecture) cannot be trained to play chess well? Or are you saying that neural networks in general cannot (practically) be trained to be an above-average chess player? Or are you saying that it depends on the input? Would it be better if they were given a picture/drawing/ascii art of the board? If so, surely they can produce it at will?

Neural Nets can be trained to play chess very well and have been doing so for a long time (see Stockfish and Leela as some of the most popular/strongest ones - top GMs have no chance against them), but these are dedicated models, where the game rules are encoded in the learning process, as opposed to large language models which are natural language processing models. Technically you can give an LLM a lot of chess books and games and it will be able to spit out chess notation. Put a webapp on top that renders text moves to the board and it looks like it's playing chess. But it isn't really.

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

#330

Post by YeGoblynQueenne »

minraws wrote: post #310
YeGoblynQueenne wrote: post #277

Earlier quoted context omitted.

>> I know HN readers and posters just read numbers and can't be bothered to read, but please read the methodology before making any claims. This is unfair to HN readers all of whom but one did not post the comment you replied to. You can't just tar everyone with the same brush. There are thousands (hundreds of thousands?) of users on this site.

How many posts if I link that do the same thing will you agree this is the norm here. Not everything I have the time and energy to reply to. This chess one is just ridiculous claims on top of ridiculous claims all the way and 0 push back in the comments except mine. I don't even know if there is critical thought or we believe what we read/shared/etc

No, I don't agree it's the norm. There is though a general tendency to opine with strong views on subjects posters have no expertise on. I think that's because many are software engineers (or equivalent) and they are used to being expected to "wing it" on whatever technical subject comes up. On the other hand you can always find informed comments by users who have specialist knowledge.

And there's plenty of pushback on here about the chess thing besides your very valid points.

EDIT: anyway if I can offer a bit of unsolicited advice, it won't do you or anyone any good to accuse everyone who doesn't agree with you of laziness, even if you can see e.g. they haven't really read an article. Just say the thing you wan to say and let them figure it out. Most people will appreciate that much better and you will feel better about yourself for acting like a mature adult.

It's even in the site guidelines:

Please don't comment on whether someone read an article. "Did you even read the article? It mentions that" can be shortened to "The article mentions that".

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