Absolutely interested. Thank you for your time to share that.

My career path in neural networks began as a researcher for cancerous tissue object detection in medical diagnostic imaging. Now it is switched to generative models for CAD (architecture, product design, game assets, etc.). I don't really mess about with fine-tuning LLMs.

However, I do self-host my own LLMs as code assistants. Thus, I'm only tangentially involved with the current LLM craze.

But it does interest me, nonetheless!

Well, can it climb trees?

The underlying neural network tech is the same as what the best chess AIs (AlphaZero, Leela) use. The problem is, as you said, that ChatGPT is designed specifically as an LLM so it’s been optimized strictly to write semi-coherent text first, and then any problem solving beyond that is ancillary. Which should say a lot about how inconsistent ChatGPT is at solving problems, given that it’s not actually optimized for any specific use cases.

Wouldn't surprise me if an LLM trained on records of chess moves made good chess moves. I just wouldn't expect the deployed version of ChatGPT to generate coherent chess moves based on the general text it's been trained on.

Yes, I agree wholeheartedly with your clarification.

My career path, as I stated in a different comment in regards to neural networks, is focused on generative DNNs for CAD applications and parametric 3D modeling. Before that, I began as a researcher in cancerous tissue classification and object detection in medical diagnostic imaging.

Thus, large language models are well out of my area of expertise in terms of the architecture of their models.

However, fundamentally it boils down to the fact that the specific large language model used was designed to predict text and not necessarily solve problems/play games to "win"/"survive".

(I admit that I'm just parroting what you stated and maybe rehashing what I stated even before that, but I like repeating and refining in simple terms to practice explaining to laymen and, dare I say, clients. It helps me feel as if I don't come off too pompously when talking about this subject to others; forgive my tedium.)

Is anyone actually surprised at that?

Using an LLM as a chess engine is like using a power tool as a table leg. Pretty funny honestly, but it's obviously not going to be good at it, at least not without scaffolding.

I wouldn't either but that's exactly what lmsys.org found.

That blog post had ratings between 858 and 1169. Those are slightly higher than the average rating of human users on popular chess sites. Their latest leaderboard shows them doing even better.

https://lmarena.ai/leaderboard
has one of the Gemini models with a rating of 1470. That's pretty good.

It plays okay for a few moves but then the moment it gets in trouble it straight up cheats.

Lol. More comparisons to how AI is currently like a young child.

Here is the main blog post that i remembered : it has a follow up, a more scientific version, and uses two other articles as a basis, so you might want to dig around what they mention in the introduction.

It is indeed a quite technical discovery, and it still lacks complete and wider analysis, but it is very interesting for the fact that it kinda invalidates the common gut feeling that llms are pure lucky random.

is like using a power tool as a table leg.

Then again, our corporate lords and masters are trying to replace all manner of skilled workers with those same LLM "AI" tools.

And clearly that will backfire on them and they'll eventually scramble to find people with the needed skills, but in the meantime tons of people will have lost their source of income.

If you don't play chess, the Atari is probably going to beat you as well.

LLMs are only good at things to the extent that they have been well-trained in the relevant areas. Not just learning to predict text string sequences, but reinforcement learning after that, where a human or some other agent says "this answer is better than that one" enough times in enough of the right contexts. It mimics the way humans learn, which is through repeated and diverse exposure.

If they set up a system to train it against some chess program, or (much simpler) simply gave it a tool call, it would do much better. Tool calling already exists and would be by far the easiest way.

It could also be instructed to write a chess solver program and then run it, at which point it would be on par with the Atari, but it wouldn't compete well with a serious chess solver.

If you believe LLMs are not good at anything then there should be relatively little to worry about in the long-term, but I am more concerned.

It's not obvious to me that it will backfire for them, because I believe LLMs are good at some things (that is, when they are used correctly, for the correct tasks). Currently they're being applied to far more use cases than they are likely to be good at -- either because they're overhyped or our corporate lords and masters are just experimenting to find out what they're good at and what not. Some of these cases will be like chess, but others will be like code*.

(* not saying LLMs are good at code in general, but for some coding applications I believe they are vastly more efficient than humans, even if a human expert can currently write higher-quality less-buggy code.)

That's exactly what thinking is, though.

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this is because an LLM is not made for playing chess

I believe LLMs are good at some things

The problem is that they're being used for all the things, including a large number of tasks that thwy are not well suited to.

yeah, we agree on this point. In the short term it's a disaster. In the long-term, assuming AI's capabilities don't continue to improve at the rate they have been, our corporate overlords will only replace people for whom it's actually worth it to them to replace with AI.

An LLM is an ordered series of parameterized / weighted nodes which are fed a bunch of tokens, and millions of calculations later result generates the next token to append and repeat the process. It's like turning a handle on some complex Babbage-esque machine. LLMs use a tiny bit of randomness ("temperature") when choosing the next token so the responses are not identical each time.

But it is not thinking. Not even remotely so. It's a simulacrum. If you want to see this, run ollama with the temperature set to 0 e.g.

ollama run gemma3:4b
>>> /set parameter temperature 0
>>> what is a leaf

You will get the same answer every single time.