Using an LLM as a chess engine is like using a power tool as a table leg. Pretty funny honestly, but it’s obviously not going to be good at it, at least not without scaffolding.
Ah, you used logic. That’s the issue. They don’t do that.
An LLM is a poor computational/predictive paradigm for playing chess.
This just in: a hammer makes a poor screwdriver.
LLMs are more like a leaf blower though
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.
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.)
Actually, a very specific model (chatgpt3.5-turbo-instruct) was pretty good at chess (around 1700 elo if i remember correctly).
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.
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!
Yeah, a lot of them hallucinate illegal moves.
Is anyone actually surprised at that?
Can i fistfight ChatGPT next? I bet I could kick its ass, too :p
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.
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.
It plays okay for a few moves but then the moment it gets in trouble it straight up cheats.
Lol. More comparisons to how AI is currently like a young child.
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.
So they are both masters of troll chess then?
See: King of the Bridge
I think the biggest problem is it’s very low ability to “test time adaptability”. Even when combined with a reasonning model outputting into its context, the weights do not learn out of the immediate context.
I think the solution might be to train a LoRa overlay on the fly against the weights and run inference with that AND the unmodified weights and then have an overseer model self evaluate and recompose the raw outputs.
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.
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.
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.
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/2501.17186Wouldn’t surprise me if an LLM trained on records of chess moves made good chess moves. I just wouldn’t expect the deployed version of ChatGPT to generate coherent chess moves based on the general text it’s been trained on.
I wouldn’t either but that’s exactly what lmsys.org found.
That blog post had ratings between 858 and 1169. Those are slightly higher than the average rating of human users on popular chess sites. Their latest leaderboard shows them doing even better.
https://lmarena.ai/leaderboard has one of the Gemini models with a rating of 1470. That’s pretty good.
I swear every single article critical of current LLMs is like, “The square got BLASTED by the triangle shape when it completely FAILED to go through the triangle shaped hole.”
It’s newsworthy when the sellers of squares are saying that nobody will ever need a triangle again, and the shape-sector of the stock market is hysterically pumping money into companies that make or use squares.
It’s also from a company claiming they’re getting closer to create morphing shape that can match any hole.
And yet the company offers no explanation for how, exactly, they’re going to get wood to do that.
You get 2 triangles in a single square mate…
CHECKMATE!
Touchdown! 3 points!
The press release where OpenAI said we’d never need chess players again
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.
That’s just clickbait in general these days lol
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.
Hardly surprising. Llms aren’t -thinking- they’re just shitting out the next token for any given input of tokens.
LLM are not built for logic.
And yet everybody is selling to write code.
The last time I checked, coding was requiring logic.
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.
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.
The amount of garbage it spits out in autocomplete is distracting. If it’s constantly making me 5-10% less productive the many times it’s wrong, it should save me a lot of time when it is right, and generally, I haven’t found it able to do that.
Yesterday I tried to prompt it to change around 20 call sites for a function where I had changed the signature. Easy, boring and repetitive, something that a junior could easily do. And all the models were absolutely clueless about it (using copilot)
a decent chunk of coding is stupid boilerplate/minutia that varies
…according to a logic, which means LLMs are bad at it.
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.
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.
ChatGPT has been, hands down, the worst AI coding assistant I’ve ever used.
It regularly suggests code that doesn’t compile or isn’t even for the language.
It generally suggests AC of code that is just a copy of the lines I just wrote.
Sometimes it likes to suggest setting the same property like 5 times.
It is absolute garbage and I do not recommend it to anyone.
All AIs are the same. They’re just scraping content from GitHub, stackoverflow etc with a bunch of guardrails slapped on to spew out sentences that conform to their training data but there is no intelligence. They’re super handy for basic code snippets but anyone using them anything remotely complex or nuanced will regret it.
One of my mates generated an entire website using Gemini. It was a React web app that tracks inventory for trading card dealers. It actually did come out functional and well-polished. That being said, the AI really struggled with several aspects of the project that humans would not:
- It left database secrets in the code
- The design of the website meant that it was impossible to operate securely
- The quality of the code itself was hot garbage—unreadable and undocumented nonsense that somehow still worked
- It did not break the code into multiple files. It piled everything into a single file
I’ve used agents for implementing entire APIs and front-ends from the ground up with my own customizations and nuances.
I will say that, for my pedantic needs, it typically only gets about 80-90% of the way there so I still have to put fingers to code, but it definitely saves a boat load of time in those instances.
That’s because it doesn’t know what it’s saying. It’s just blathering out each word as what it estimates to be the likely next word given past examples in its training data. It’s a statistics calculator. It’s marginally better than just smashing the auto fill on your cell repeatedly. It’s literally dumber than a parrot.
Parrots are actually intelligent though.
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
I find it really hit and miss. Easy, standard operations are fine but if you have an issue with code you wrote and ask it to fix it, you can forget it
I like tab coding, writing small blocks of code that it thinks I need. Its On point almost all the time. This speeds me up.
Bingo. If anything what you’re finding is the people bitching are the same people that if given a bike wouldn’t know how to ride it, which is fair. Some people understand quicker how to use the tools they are given.
Edit - a poor carpenter blames his tools.
I’ve found Claude 3.7 and 4.0 and sometimes Gemini variants still leagues better than ChatGPT/Copilot.
Still not perfect, but night and day difference.
I feel like ChatGPT didn’t focus on coding and instead focused on mainstream, but I am not an expert.
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.
It’s the ideal help for people who shouldn’t be employed as programmers to start with.
I had to explain hexadecimal to somebody the other day. It’s honestly depressing.
my favorite thing is to constantly be implementing libraries that don’t exist
You’re right. That library was removed in ToolName [PriorVersion]. Please try this instead.
*makes up entirely new fictitious library name*
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…
It’s even worse when AI soaks up some project whose APIs are constantly changing. Try using AI to code against jetty for example and you’ll be weeping.
I’ve had success with splitting a function into 2 and planning out an overview, though that’s more like talking to myself
I wouldn’t use it to generate stuff though
I don’t use it for coding. I use it sparingly really, but want to learn to use it more efficiently. Are there any areas in which you think it excels? Are there others that you’d recommend instead?
Use Gemini (2.5) or Claude (3.7 and up). OpenAI is a shitshow
Tbf, the article should probably mention the fact that machine learning programs designed to play chess blow everything else out of the water.
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
I forgot which airline it is but one of the onboard games in the back of a headrest TV was a game called “Beginners Chess” which was notoriously difficult to beat so it was tested against other chess engines and it ranked in like the top five most powerful chess engines ever
Yeah its like judging how great a fish is at climbing a tree. But it does show that it’s not real intelligence or reasoning
Don’t call my fish stupid.
Well, can it climb trees?
It does
It does not. Where?
Did the author thinks ChatGPT is in fact an AGI? It’s a chatbot. Why would it be good at chess? It’s like saying an Atari 2600 running a dedicated chess program can beat Google Maps at chess.
OpenAI has been talking about AGI for years, implying that they are getting closer to it with their products.
https://openai.com/index/planning-for-agi-and-beyond/
https://openai.com/index/elon-musk-wanted-an-openai-for-profit/
Not to even mention all the hype created by the techbros around it.
Hey I didn’t say anywhere that corporations don’t lie to promote their product did I?
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.
That’s totally fair, the company is obviously lying, excuse me “marketing”, to promote their product, that’s absolutely true.
AI including ChatGPT is being marketed as super awesome at everything, which is why that and similar AI is being forced into absolutely everything and being sold as a replacement for people.
Something marketed as AGI should be treated as AGI when proving it isn’t AGI.
Not to help the AI companies, but why don’t they program them to look up math programs and outsource chess to other programs when they’re asked for that stuff? It’s obvious they’re shit at it, why do they answer anyway? It’s because they’re programmed by know-it-all programmers, isn’t it.
why don’t they program them
AI models aren’t programmed traditionally. They’re generated by machine learning. Essentially the model is given test prompts and then given a rating on its answer. The model’s calculations will be adjusted so that its answer to the test prompt will be closer to the expected answer. You repeat this a few billion times with a few billion prompts and you will have generated a model that scores very high on all test prompts.
Then someone asks it how many R’s are in strawberry and it gets the wrong answer. The only way to fix this is to add that as a test prompt and redo the machine learning process which takes an enormous amount of time and computational power each time it’s done, only for people to once again quickly find some kind of prompt it doesn’t answer well.
There are already AI models that play chess incredibly well. Using machine learning to solve a complexe problem isn’t the issue. It’s trying to get one model to be good at absolutely everything.
This is where MCP comes in. It’s a protocol for LLMs to call standard tools. Basically the LLM would figure out the tool to use from the context, then figure out the order of parameters from those the MCP server says is available, send the JSON, and parse the response.
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?”
Because they’re fucking terrible at designing tools to solve problems, they are obviously less and less good at pretending this is an omnitool that can do everything with perfect coherency (and if it isn’t working right it’s because you’re not believing or paying hard enough)
Or they keep telling you that you just have to wait it out. It’s going to get better and better!
why don’t they program them to look up math programs and outsource chess to other programs when they’re asked for that stuff?
They will, when it makes sense for what the AI is designed to do. For example, ChatGPT can outsource image generation to an AI dedicated to that. It also used to calculate math using python for me, but that doesn’t seem to happen anymore, probably due to security issues with letting the AI run arbitrary python code.
ChatGPT however was not designed to play chess, so I don’t see why OpenAI should invest resources into connecting it to a chess API.
I think especially since adding custom GPTs, adding this kind of stuff has become kind of unnecessary for base ChatGPT. If you want a chess engine, get a GPT which implements a Stockfish API (there seem to be several GPTs that do). For math, get the Wolfram GPT which uses Wolfram Alpha’s API, or a different powerful math GPT.
They are starting to do this. Most new models support function calling and can generate code to come up with math answers etc
If you pay for chatgpt you can connect it with wolfrenalpha and it’s relays the maths to it
I don’t pay for ChatGPT and just used the Wolfram GPT. They made the custom GPTs non-paid at some point.
…or a simple counter to count the r in strawberry. Because that’s more difficult than one might think and they are starting to do this now.
Because the LLMs are now being used to vibe code themselves.
I think they’re trying to do that. But AI can still fail at that lol
From a technology standpoint, nothing is stopping them. From a business standpoint: hubris.
To put time and effort into creating traditional logic based algorithms to compensate for this generic math model would be to admit what mathematicians and scientists have known for centuries. That models are good at finding patterns but they do not explain why a relationship exists (if it exists at all). The technology is fundamentally flawed for the use cases that OpenAI is trying to claim it can be used in, and programming around it would be to acknowledge that.
I don’t think ai is being marketed as awesome at everything. It’s got obvious flaws. Right now its not good for stuff like chess, probably not even tic tac toe. It’s a language model, its hard for it to calculate the playing field. But ai is in development, it might not need much to start playing chess.
What the tech is being marketed as and what it’s capable of are not the same, and likely never will be. In fact all things are very rarely marketed how they truly behave, intentionally.
Everyone is still trying to figure out what these Large Reasoning Models and Large Language Models are even capable of; Apple, one of the largest companies in the world just released a white paper this past week describing the “illusion of reasoning”. If it takes a scientific paper to understand what these models are and are not capable of, I assure you they’ll be selling snake oil for years after we fully understand every nuance of their capabilities.
TL;DR Rich folks want them to be everything, so they’ll be sold as capable of everything until we repeatedly refute they are able to do so.
I think in many cases people intentionally or unintentionally disregard the time component here. Ai is in development. I think what is being marketed here, just like in the stock market, is a piece of the future. I don’t expect the models I use to be perfect and not make mistakes, so I use them accordingly. They are useful for what I use them for and I wouldn’t use them for chess. I don’t expect that laundry detergent to be just as perfect in the commercial either.
Marketing does not mean functionality. AI is absolutely being sold to the public and enterprises as something that can solve everything. Obviously it can’t, but it’s being sold that way. I would bet the average person would be surprised by this headline solely on what they’ve heard about the capabilities of AI.
I don’t think anyone is so stupid to believe current ai can solve everything.
And honestly, I didn’t see any marketing material that would claim that.
You are both completely over estimating the intelligence level of “anyone” and not living in the same AI marketed universe as the rest of us. People are stupid. Really stupid.
I don’t understand why this is so important, marketing is all about exaggerating, why expect something different here.
It’s not important. You said AI isn’t being marketed to be able to do everything. I said yes it is. That’s it.
The Zoom CEO, that is the video calling software, wanted to train AIs on your work emails and chat messages to create AI personalities you could send to the meetings you’re paid to sit through while you drink Corona on the beach and receive a “summary” later.
The Zoom CEO, that is the video calling software, seems like a pretty stupid guy?
Yeah. Yeah, he really does. Really… fuckin’… dumb.
Same genius who forced all his own employees back into the office. An incomprehensibly stupid maneuver by an organization that literally owes its success to people working from home.
Really then why are they cramming AI into every app and every device and replacing jobs with it and claiming they’re saving so much time and money and they’re the best now the hardest working most efficient company and this is the future and they have a director of AI vision that’s right a director of AI vision a true visionary to lead us into the promised land where we will make money automatically please bro just let this be the automatic money cheat oh god I’m about to
Those are two different things.
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they are craming ai everywhere because nobody wants to miss the boat and because it plays well in the stock market.
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the people claiming it’s awesome and that they are doing I don’t know what with it, replacing people are mostly influencers and a few deluded people.
Ai can help people in many different roles today, so it makes sense to use it. Even in roles that is not particularly useful, it makes sense to prepare for when it is.
it makes sense to prepare for when it is.
Pfft, okay.
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Google Maps doesn’t pretend to be good at chess. ChatGPT does.
A toddler can pretend to be good at chess but anybody with reasonable expectations knows that they are not.
Plot twist: the toddler has a multi-year marketing push worth tens if not hundreds of millions, which convinced a lot of people who don’t know the first thing about chess that it really is very impressive, and all those chess-types are just jealous.
Have you tried feeding the toddler gallons of baby-food? Maybe then it can play chess
They’ve been feeding the toddler everybody else’s baby food and claiming they have the right to.
“If we have to ask every time before stealing a little baby food, our morbidly obese toddler cannot survive”
well so much hype has been generated around chatgpt being close to AGI that now it makes sense to ask questions like “can chatgpt prove the Riemann hypothesis”
Even the models that pretend to be AGI are not. It’s been proven.
Most people do. It’s just called AI in the media everywhere and marketing works. I think online folks forget that something as simple as getting a Lemmy account by yourself puts you into the top quintile of tech literacy.
Yet even on Lemmy people can’t seem to make sense of these terms and are saying things like “LLM’s are not AI”
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I agree with your general statement, but in theory since all ChatGPT does is regurgitate information back and a lot of chess is memorization of historical games and types, it might actually perform well. No, it can’t think, but it can remember everything so at some point that might tip the results in it’s favor.
I mean it may be possible but the complexity would be so many orders of magnitude greater. It’d be like learning chess by just memorizing all the moves great players made but without any context or understanding of the underlying strategy.
Regurgitating an impression of, not regurgitating verbatim, that’s the problem here.
Chess is 100% deterministic, so it falls flat.
I’m guessing it’s not even hard to get it to “confidently” violate the rules.
Articles like this are good because it exposes the flaws with the ai and that it can’t be trusted with complex multi step tasks.
Helps people see that think AI is close to a human that its not and its missing critical functionality
The problem is though that this perpetuates the idea that ChatGPT is actually an AI.
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
I think that’s generally the point is most people thing chat GPT is this sentient thing that knows everything and… no.
Do they though? No one I talked to, not my coworkers that use it for work, not my friends, not my 72 year old mother think they are sentient.
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.
In all fairness. Machine learning in chess engines is actually pretty strong.
AlphaZero was developed by the artificial intelligence and research company DeepMind, which was acquired by Google. It is a computer program that reached a virtually unthinkable level of play using only reinforcement learning and self-play in order to train its neural networks. In other words, it was only given the rules of the game and then played against itself many millions of times (44 million games in the first nine hours, according to DeepMind).
Sure, but machine learning like that is very different to how LLMs are trained and their output.
Oh absolutely you can apply machine learning to game strategy. But you can’t expect a generalized chatbot to do well at strategic decision making for a specific game.
I like referring to LLMs as VI (Virtual Intelligence from Mass Effect) since they merely give the impression of intelligence but are little more than search engines. In the end all one is doing is displaying expected results based on a popularity algorithm. However they do this inconsistently due to bad data in and limited caching.
I mean, open AI seem to forget it isn’t.
Can ChatGPT actually play chess now? Last I checked, it couldn’t remember more than 5 moves of history so it wouldn’t be able to see the true board state and would make illegal moves, take it’s own pieces, materialize pieces out of thin air, etc.
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 not an LLM, but Stockfish does use AI under the hood and has been since 2020. Stockfish uses a classical alpha-beta search strategy (if I recall correctly) combined with a neural network for smarter pruning.
There are some engines of comparable strength that are primarily neural-network based.
lc0comes to mind.lc0placed 2nd in the Top Chess Engine Championships in 9 out of the past 10 seasons. By comparison, Stockfish is currently on a 10-season win streak in the TCEC.
It can’t, but that didn’t stop a bunch of gushing articles a while back about how it had an ELO of 2400 and other such nonsense. Turns out you could get it to have an ELO of 2400 under a very very specific set of circumstances, that include correcting it every time it hallucinated pieces or attempted to make illegal moves.
and still lose to stockfish even after conjuring 3 queens out of thin air lol
ChatGPT must adhere honorably to the rules that its making up on the spot. Thats Dallas
It could always play it if you reminded it of the board state every move. Not well, but at least generally legally. And while I know elites can play chess blind, the average person can’t, so it was always kind of harsh to hold it to that standard and criticise it not being able to remember more than 5 moves when most people can’t do that themselves.
Besides that, it was never designed to play chess. It would be like insulting Watson the Jeopardy bot for losing against the Atari chess bot, it’s not what it was designed to do.
So, it fares as well as the average schmuck, proving it is human
/s
