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

CHECKMATE!

And yet everybody is selling to write code.

The last time I checked, coding was requiring logic.

There are custom GPTs which claim to play at a stockfish level or be literally stockfish under the hood (I assume the former is still the latter just not explicitly). Haven't tested them, but if they work, I'd say yes. An LLM itself will never be able to play chess or do anything similar, unless they outsource that task to another tool that can. And there seem to be GPTs that do exactly that.

As for why we need ChatGPT then when the result comes from Stockfish anyway, it's for the natural language prompts and responses.

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

Isn't the Atari just a game console, not a chess engine?

Like, Wikipedia doesn't mention anything about the Atari 2600 having a built-in chess engine.

If they were willing to run a chess game on the Atari 2600, why did they not apply the same to ChatGPT? There are custom GPTs which claim to use a stockfish API or play at a similar level.

Like this, it's just unfair. Both platforms are not designed to deal with the task by themselves, but one of them is given the necessary tooling, the other one isn't. No matter what you think of ChatGPT, that's not a fair comparison.

Llms useless confirmed once again

Sure, but machine learning like that is very different to how LLMs are trained and their output.

Machine learning has existed for many years, now. The issue is with these funding-hungry new companies taking their LLMs, repackaging them as "AI" and attributing every ML win ever to "AI".

ML programs designed and trained specifically to identify tumors in medical imaging have become good diagnostic tools. But if you read in news that "AI helps cure cancer", it makes it sound like it was a lone researcher who spent a few minutes engineering the right prompt for Copilot.

Yes a specifically-designed and finely tuned ML program can now beat the best human chess player, but calling it "AI" and bundling it together with the latest Gemini or Claude iteration's "reasoning capabilities" is intentionally misleading. That's why articles like this one are needed. ML is a useful tool but far from the "super-human general intelligence" that is meant to replace half of human workers by the power of wishful prompting

Sometimes it seems like most of these AI articles are written by AIs with bad prompts.

Human journalists would hopefully do a little research. A quick search would reveal that researches have been publishing about this for over a year so there's no need to sensationalize it. Perhaps the human journalist could have spent a little time talking about why LLMs are bad at chess and how researchers are approaching the problem.

LLMs on the other hand, are very good at producing clickbait articles with low information content.

So, it fares as well as the average schmuck, proving it is human

/s

And yet the company offers no explanation for how, exactly, they're going to get wood to do that.

Gemini will get basic C++, probably the best documented language for beginners out there, right about half of the time.

I think that might even be the problem, honestly, a bunch of new coders post bad code and it's fixed in comments but the LLM CAN'T realize that.

Okay I maybe exaggerated a bit, but a lot of people think it actually knows things, or is actually smart. Which… it’s not… at all. It’s just pattern recognition. Which was I assume the point of showing it can’t even beat the goddamn Atari because it cannot think or reason, it’s all just copy pasta and pattern recognition.

GPTs which claim to use a stockfish API

Then the actual chess isn't LLM. If you are going stockfish, then the LLM doesn't add anything, stockfish is doing everything.

The whole point is the marketing rage is that LLMs can do all kinds of stuff, doubling down on this with the branding of some approaches as "reasoning" models, which are roughly "similar to 'pre-reasoning', but forcing use of more tokens on disposable intermediate generation steps". With this facet of LLM marketing, the promise would be that the LLM can "reason" itself through a chess game without particular enablement. In practice, people trying to feed in gobs of chess data to an LLM end up with an LLM that doesn't even comply to the rules of the game, let alone provide reasonable competitive responses to an oppone.

Gotham chess has a video of making chatgpt play chess against stockfish. Spoiler: chatgpt does not do well. It plays okay for a few moves but then the moment it gets in trouble it straight up cheats. Telling it to follow the rules of chess doesn't help.

This sort of gets to the heart of LLM-based "AI". That one example to me really shows that there's no actual reasoning happening inside. It's producing answers that statistically look like answers that might be given based on that input.

For some things it even works. But calling this intelligence is dubious at best.

To be fair, a decent chunk of coding is stupid boilerplate/minutia that varies environment to environment, language to language, library to library.

So LLM can do some code completion, filling out a bunch of boilerplate that is blatantly obvious, generating the redundant text mandated by certain patterns, and keeping straight details between languages like "does this language want join as a method on a list with a string argument, or vice versa?"

Problem is this can be sometimes more annoying than it's worth, as miscompletions are annoying.

All these comments asking "why don't they just have chatgpt go and look up the correct answer".

That's not how it works, you buffoons, it trains off of datasets long before it releases. It doesn't think. It doesn't learn after release, it won't remember things you try to teach it.

Really lowering my faith in humanity when even the AI skeptics don't understand that it generates statistical representations of an answer based on answers given in the past.

Oh man, I feel this. A couple of times I've had to field questions about some REST API I support and they ask why they get errors when they supply a specific attribute. Now that attribute never existed, not in our code, not in our documentation, we never thought of it. So I say "Well, that attribute is invalid, I'm not sure where you saw to do that". They get insistent that the code is generated by a very good LLM, so we must be missing something...

That's just clickbait in general these days lol