Technology

LLMs are more like a leaf blower though

I'm impressed, if that's true! In general, an LLM's training cost vs. an LSTM, RNN, or some other more appropriate DNN algorithm suitable for the ruleset is laughably high.

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.)

Because it doesn't have any understanding of the rules of chess or even an internal model of the game state, it just has the text of chess games in its training data and can reproduce the notation, but nothing to prevent it from making illegal moves, trying to move or capture pieces that don't exist, incorrectly declaring check/checkmate, or any number of nonsensical things.

Hallucinating 100% of the time 👌

ChatGPT versus Deepseek is hilarious. They both cheat like crazy and then one side jedi mind tricks the winner into losing.

It's also from a company claiming they're getting closer to create morphing shape that can match any hole.

The press release where OpenAI said we'd never need chess players again

You get 2 triangles in a single square mate...

CHECKMATE!

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.

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.)