The Atari 2600 is just hardware. The software came on plug-in cartridges. Video Chess was released for it in 1979.

Even the models that pretend to be AGI are not. It's been proven.

Fine, a chess engine that is capable of running with affordable even for the time 1970s electronics will best what marketing folks would have you think is an arbitrarily capable "reasoning" model running on top of the line 2025 hardware.

You can split hairs about "well actually, the 2600 is hardware and a chess engine is the software" but everyone gets the point.

As to assertions that no one should expect an LLM to be a chess engine, well tell that to the industry that is asserting the LLMs are now "reasoning" and provides a basis to replace most of the labor pool. We need stories like this to calibrate expectations in a way common people can understand..

Well, the first and obvious thing to do to show that AI is bad is to show that AI is bad. If it provides that much of a low-hanging fruit for the demonstration... that just further emphasizes the point.

In this case it's not even bad prompts, it's a problem domain ChatGPT wasn't designed to be good at. It's like saying modern medicine is clearly bullshit because a doctor loses a basketball game.

a decent chunk of coding is stupid boilerplate/minutia that varies

...according to a logic, which means LLMs are bad at it.

Ah, you used logic. That's the issue. They don't do that.

I'd say that those details that vary tend not to vary within a language and ecosystem, so a fairly dumb correlative relationship is enough to generally be fine. There's no way to use logic to infer that it's obvious that in language X you need to do mylist.join(string) but in language Y you need to do string.join(mylist), but it's super easy to recognize tokens that suggest those things and a correlation to the vocabulary that matches the context.

Rinse and repeat for things like do I need to specify type and what is the vocabulary for the best type for a numeric value, This variable that makes sense is missing a declaration, does this look to actually be a new distinct variable or just a typo of one that was declared.

But again, I'm thinking mostly in what kind of sort of can work, my experience personally is that it's wrong so often as to be annoying and get in the way of more traditional completion behaviors that play it safe, though with less help particularly for languages like python or javascript.

Yeah, but not when it comes to understanding human speech. There's a reason that repeating words without really understanding them is called parroting. Gray parrots are the smartest and some can actually understand language a little bit, making them smarter than chat, which is just high tech guessing without comprehension

This just in: a hammer makes a poor screwdriver.

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.

Can i fistfight ChatGPT next? I bet I could kick its ass, too

Like humans are way better at answering stuff when it’s a collaboration of more than one person. I suspect the same is true of LLMs.

It is.

It's really common for non-language implementations of neural networks. If you have an NN that's right some percentage of the time, you can often run it through a bunch of copies of the NNs and take the average and that average is correct a higher percentage of the time.

Aider is an open source AI coding assistant that lets you use one model to plan the coding and a second one to do the actual coding. It works better than doing it in a single pass, even if you assign the the same model to planing and coding.

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

I imagine the "author" did something like, "Search http://google.scholar.com/ find a publication where AI failed at something and write a paragraph about it."

It's not even as bad as the article claims.

Atari isn't great at chess. https://chess.stackexchange.com/questions/24952/how-strong-is-each-level-of-atari-2600s-video-chess
Random LLMs were nearly as good 2 years ago. https://lmsys.org/blog/2023-05-03-arena/
LLMs that are actually trained for chess have done much better. https://arxiv.org/abs/2501.17186

So they are both masters of troll chess then?

See: King of the Bridge

A lot of writing code is relatively standard patterns and variations on them. For most but the really interesting parts, you could probably write a sufficiently detailed description and get an LLM to produce functional code that does the thing.

Basically for a bunch of common structures and use cases, the logic already exists and is well known and replicated by enough people in enough places in enough languages that an LLM can replicate it well enough, like literally anyone else who has ever written anything in that language.

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.

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!