Actually, a very specific model (chatgpt3.5-turbo-instruct) was pretty good at chess (around 1700 elo if i remember correctly).
NeilBru@lemmy.world 4 days ago
An LLM is a poor computational paradigm for playing chess.
Takapapatapaka@lemmy.world 4 days ago
NeilBru@lemmy.world 4 days ago
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.
Takapapatapaka@lemmy.world 4 days ago
Oh yes, cost of training are ofc a great loss here, it’s not optimized at all, and it’s stuck at an average level.
Interestingly, i believe some people did research on it and found some parameters in the model that seemed to represent the state of the chess board (as in, they seem to reflect the current state of the board, and when artificially modified, the model takes modification into account in its playing). It was used by a french youtuber to show how LLMs can somehow have a kinda representation of the world. I can try to get the sources back if you’re interested.
NeilBru@lemmy.world 4 days ago
Absolutely interested. Thank you if you’re willing to share that.
My career path in neural networks began as a researcher for cancerous tissue object detection 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.
Bleys@lemmy.world 4 days ago
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.
NeilBru@lemmy.world 4 days ago
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 models 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 explain to laymen and, dare I say, clients. It helps me make make I don’t come off as too pompous when talking about this subject; forgive my tedium.
sugar_in_your_tea@sh.itjust.works 4 days ago
Yeah, a lot of them hallucinate moves.
surph_ninja@lemmy.world 4 days ago
This just in: a hammer makes a poor screwdriver.
WhyJiffie@sh.itjust.works 4 days ago
LLMs are more like a leaf blower though