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

why don't they program them to look up math programs and outsource chess to other programs when they're asked for that stuff?

Because the AI doesn't know what it's being asked, it's just a algorithm guessing what the next word in a reply is. It has no understanding of what the words mean.

"Why doesn't the man in the Chinese room just use a calculator for math questions?"

You're not wrong, but keep in mind ChatGPT advocates, including the company itself are referring to it as AI, including in marketing. They're saying it's a complete, self-learning, constantly-evolving Artificial Intelligence that has been improving itself since release... And it loses to a 4KB video game program from 1979 that can only "think" 2 moves ahead.

Fair point.

I liked the "upgraded autocompletion", you know, an completion based on the context, just before the time that they pushed it too much with 20 lines of non sense...

Now I am thinking of a way of doing the thing, then I receive a 20 lines suggestion.

So I am checking if that make sense, losing my momentum, only to realize the suggestion us calling shit that don't exist...

Screw that.

An LLM is a poor computational/predictive paradigm for playing chess.

People already think chatGPT is a general AI. We need more articles like this showing is ineffectiveness at being intelligent. Besides it helps find a limitations of this technology so that we can hopefully use it to argue against every single place

Touchdown! 3 points!

Hallucinating 100% of the time