Prepare to be delighted. Full disclosure, my Atari isn’t hooked up and also I don’t have the Video Chess cart even if it was, so this was fetched from Google Images.
I bet that's a slightly unfair representation of what it actually looked like. Graphics back then were purposely designed for how they would look on CRT tvs which add a lot of specific distortions to images. So taking a screenshot of a game running in an emulator without using a high quality crt filter added to the image will be a very untrue representation of what the game actually looked like.
(Don't get me wrong, I'm not saying it actually looked great when displayed correctly, but i am saying it would've looked considerably better than this emulator screenshot)
homesweethomeMrL@lemmy.world
on 10 Jun 00:10
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Can confirm.
And if you play it on expert mode, you can leave for college and get your degree before it’s your turn again.
NotMyOldRedditName@lemmy.world
on 10 Jun 01:40
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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.
spankmonkey@lemmy.world
on 09 Jun 23:02
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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.
pelespirit@sh.itjust.works
on 09 Jun 23:21
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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.
PixelatedSaturn@lemmy.world
on 09 Jun 23:30
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…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.
I think they’re trying to do that. But AI can still fail at that lol
rebelsimile@sh.itjust.works
on 09 Jun 23:49
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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)
I don't pay for ChatGPT and just used the Wolfram GPT. They made the custom GPTs non-paid at some point.
ImplyingImplications@lemmy.ca
on 10 Jun 04:14
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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.
They are starting to do this. Most new models support function calling and can generate code to come up with math answers etc
MajorasMaskForever@lemmy.world
on 10 Jun 05:01
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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.
fmstrat@lemmy.nowsci.com
on 10 Jun 10:57
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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?
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.
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?”
PixelatedSaturn@lemmy.world
on 09 Jun 23:41
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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.
PixelatedSaturn@lemmy.world
on 10 Jun 01:00
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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.
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
PixelatedSaturn@lemmy.world
on 10 Jun 01:07
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Those are two different things.
they are craming ai everywhere because nobody wants to miss the boat and because it plays well in the stock market.
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.
petrol_sniff_king@lemmy.blahaj.zone
on 10 Jun 02:07
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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.
PixelatedSaturn@lemmy.world
on 10 Jun 01:09
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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.
PixelatedSaturn@lemmy.world
on 10 Jun 04:23
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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.
PixelatedSaturn@lemmy.world
on 10 Jun 09:25
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My point is people aren’t expecting AGI. People have already tried them and understand what the general capabilities are. Businesses today even more. I don’t think exaggerating the capabilities is such an overarching issue, that anyone could call the whole thing a scam.
petrol_sniff_king@lemmy.blahaj.zone
on 10 Jun 02:05
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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.
jubilationtcornpone@sh.itjust.works
on 10 Jun 03:24
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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.
suburban_hillbilly@lemmy.ml
on 09 Jun 23:02
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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.
TowardsTheFuture@lemmy.zip
on 09 Jun 23:10
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I think that’s generally the point is most people thing chat GPT is this sentient thing that knows everything and… no.
PixelatedSaturn@lemmy.world
on 10 Jun 04:30
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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.
TowardsTheFuture@lemmy.zip
on 10 Jun 13:53
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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.
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.
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).
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.
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 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.
Eagle0110@lemmy.world
on 10 Jun 04:22
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Regurgitating an impression of, not regurgitating verbatim, that’s the problem here.
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.
A toddler can pretend to be good at chess but anybody with reasonable expectations knows that they are not.
MelodiousFunk@startrek.website
on 10 Jun 07:37
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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.
“If we have to ask every time before stealing a little baby food, our morbidly obese toddler cannot survive”
iAvicenna@lemmy.world
on 10 Jun 07:43
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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”
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.
Agreed, which is why it’s important to have articles out in the wild that show the shortcomings of AI. If all people read is all the positive crap coming out of companies like OpenAI then they will make stupid decisions.
anubis119@lemmy.world
on 09 Jun 22:57
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A strange game. How about a nice game of Global Thermonuclear War?
ada@piefed.blahaj.zone
on 09 Jun 23:06
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No thank you. The only winning move is not to play
Endymion_Mallorn@kbin.melroy.org
on 10 Jun 01:36
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JOSHUA
Nurse_Robot@lemmy.world
on 09 Jun 22:57
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I’m often impressed at how good chatGPT is at generating text, but I’ll admit it’s hilariously terrible at chess. It loves to manifest pieces out of thin air, or make absurd illegal moves, like jumping its king halfway across the board and claiming checkmate
It can be bad at the very thing it's designed to do. It can repeat phrases often, something that isn't great for writing. But why wouldn't it, it's all about probability so common things said will pop up more unless you adjust the variables that determine the randomness.
Lifecoach5000@lemmy.world
on 09 Jun 23:45
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Yeah! I’ve loved watching Gothem Chess’ videos on these. Always have been good for a laugh.
muntedcrocodile@lemm.ee
on 09 Jun 23:17
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This isn’t the strength of gpt-o4 the model has been optimised for tool use as an agent. That’s why its so good at image gen relative to other models it uses tools to construct an image piece by piece similar to a human. Also probably poor system prompting. A LLM is not a universal thinking machine its a a universal process machine. An LLM understands the process and uses tools to accomplish the process hence its strengths in writing code (especially as an agent).
Its similar to how a monkey is infinitely better at remembering a sequence of numbers than a human ever could but is totally incapable of even comprehending writing down numbers.
cheese_greater@lemmy.world
on 09 Jun 23:43
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Do you have a source for that re:monkeys memorizing numerical sequences? What do you mean by that?
RememberTheEnding@lemmy.world
on 09 Jun 23:51
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If you're writing a novel simulation for a non-trivial system, it might be best to learn to code so you can identify any issues in the simulation later. It's likely that LLMs do not have the information required to generate good code for this context.
Asswardbackaddict@lemmy.world
on 10 Jun 02:23
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You’re right. I’m not relying on this shit. It’s a tool. Fucking up the gui is fine, but making any changes I don’t research to my simulator core could fuck up my whole project. It’s a tool that likes to cater to you, and you have to work around that - really, not too different from how much pressure you put on a grinder. You gotta learn how to work it. And, you’re sentiment is correct. My lack of programming experience is a big hurdle I have to account for and make safeguards against. It would be a huge help if I started from the basics. But, I mean, I also can’t rub two sticks together to heat my home. Doesn’t mean I can’t use this tool to produce reliable results.
petrol_sniff_king@lemmy.blahaj.zone
on 10 Jun 02:59
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The tough guys and sigma males of yester-year used to say things like “If I were homeless, I would just bathe in the creek using the natural animal fats from the squirrel I caught for dinner as soap, win a new job by explaining my 21-days-in-7 workweek ethos, and buy a new home using my shares in my dad’s furniture warehouse as collateral against the loan. It’s not impossible to get back on your feet.”
But with the advent of AI, which, actually, is supposed to do things for you, it’s completely different now.
I also can’t rub two sticks together to heat my home.
Dude, that fucking sucks. What is wrong with you?
Asswardbackaddict@lemmy.world
on 10 Jun 03:17
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You’re so fucking silly. You gonna study cell theory to see how long you should keep vegetables in your fridge? Go home. Save science for people who understand things.
junkthief@lemmy.blahaj.zone
on 10 Jun 10:51
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Save science for people who understand things.
Does this not strike you as the least bit ironic?
seven_phone@lemmy.world
on 10 Jun 01:29
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You say you produce good oranges but my machine for testing apples gave your oranges a very low score.
wizardbeard@lemmy.dbzer0.com
on 10 Jun 11:27
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No, more like “Your marketing team, sales team, the news media at large, and random hype men all insist your orange machine works amazing on any fruit if you know how to use it right. It didn’t work my strawberries when I gave it all the help I could, and was outperformed by my 40 year old strawberry machine. Please stop selling the idea it works on all fruit.”
This study is specifically a counter to the constant hype that these LLMs will revolutionize absolutely everything, and the constant word choices used in discussion of LLMs that imply they have reasoning capabilities.
NotMyOldRedditName@lemmy.world
on 10 Jun 01:38
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Okay, but could ChatGPT be used to vibe code a chess program that beats the Atari 2600?
GreenKnight23@lemmy.world
on 10 Jun 04:25
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no.
the answer is always, no.
NotMyOldRedditName@lemmy.world
on 10 Jun 05:40
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The answer might be no today, but always seems like a stretch.
Endymion_Mallorn@kbin.melroy.org
on 10 Jun 01:39
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I mean, that 2600 Chess was built from the ground up to play a good game of chess with variable difficulty levels. I bet there's days or games when Fischer couldn't have beaten it. Just because a thing is old and less capable than the modern world does not mean it's bad.
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
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
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.
ToastedRavioli@midwest.social
on 10 Jun 04:29
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ChatGPT must adhere honorably to the rules that its making up on the spot. Thats Dallas
and still lose to stockfish even after conjuring 3 queens out of thin air lol
Robust_Mirror@aussie.zone
on 10 Jun 08:57
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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.
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.
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. lc0 comes to mind. lc0 placed 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.
AlecSadler@sh.itjust.works
on 10 Jun 05:38
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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.
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.
Mobiuthuselah@lemm.ee
on 10 Jun 07:12
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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?
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.
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…
Etterra@discuss.online
on 10 Jun 09:12
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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.
AnUnusualRelic@lemmy.world
on 10 Jun 10:40
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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
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.
AlecSadler@sh.itjust.works
on 10 Jun 16:12
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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.
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
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.
PushButton@lemmy.world
on 10 Jun 14:49
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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…
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)
lambalicious@lemmy.sdf.org
on 10 Jun 17:15
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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.
An LLM is an ordered series of parameterized / weighted nodes which are fed a bunch of tokens, and millions of calculations later result generates the next token to append and repeat the process. It’s like turning a handle on some complex Babbage-esque machine. LLMs use a tiny bit of randomness (“temperature”) when choosing the next token so the responses are not identical each time.
But it is not thinking. Not even remotely so. It’s a simulacrum. If you want to see this, run ollama with the temperature set to 0 e.g.
ollama run gemma3:4b
>>> /set parameter temperature 0
>>> what is a leaf
You will get the same answer every single time.
stevedice@sh.itjust.works
on 17 Jun 02:31
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I know what an LLM is doing. You don’t know what your brain is doing.
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.
PushButton@lemmy.world
on 10 Jun 12:14
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You get 2 triangles in a single square mate…
CHECKMATE!
Acid_Burn@lemmy.dbzer0.com
on 10 Jun 15:02
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lambalicious@lemmy.sdf.org
on 10 Jun 17:13
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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.
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.
Edit: Given the existing replies and downvotes, I think this comment is being misunderstood. I would like to try clarifying again what I meant here.
First of all, I'd like to ask if this article is satire. That's the only way I can understand the replies I've gotten that critized me on grounds of the marketing aspect of LLMs (when the article never brings up that topic itself, nor did I). Like, if this article is just some tongue in cheek type thing about holding LLMs to the standards they're advertised at, I can understand both the article and the replies I've gotten. But the article never suggests so itself. So my assumption when writing my comment was that this is not the case and it is serious.
The Atari is hardware. It can't play chess on its own. To be able to, you need a game for it which is inserted. Then the Atari can interface with the cartridge and play the game.
ChatGPT is an LLM. Guess what, it also can't play chess on its own. It also needs to interface with a third party tool that enables it to play chess.
Neither the Atari nor ChatGPT can directly, on their own, play chess. This was my core point.
I merely pointed out that it's unfair that one party in this comparison is given the tool it needs (the cartridge), but the other party isn't.
Unless this is satire, I don't see how marketing plays a role here at all.
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.
And neither did the Atari 2600 win against ChatGPT. Whatever game they ran on it did.
That's my point here. The fact that neither Atari 2600 nor ChatGPT are capable of playing chess on their own. They can only do so if you provide them with the necessary tools. Which applies to both of them. Yet only one of them was given those tools here.
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…
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.
nova_ad_vitum@lemmy.ca
on 10 Jun 13:55
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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.
Ultraviolet@lemmy.world
on 10 Jun 16:27
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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.
interdimensionalmeme@lemmy.ml
on 10 Jun 16:39
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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.
JacksonLamb@lemmy.world
on 10 Jun 18:14
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ChatGPT versus Deepseek is hilarious. They both cheat like crazy and then one side jedi mind tricks the winner into losing.
propitiouspanda@lemmy.cafe
on 10 Jun 22:42
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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.
LovableSidekick@lemmy.world
on 10 Jun 17:13
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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.
LovableSidekick@lemmy.world
on 10 Jun 21:13
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Wouldn’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.
lmarena.ai/leaderboard
has one of the Gemini models with a rating of 1470. That’s pretty good.
ICastFist@programming.dev
on 10 Jun 13:36
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So, it fares as well as the average schmuck, proving it is human
/s
finitebanjo@lemmy.world
on 10 Jun 14:01
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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.
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
on 10 Jun 18:57
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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!
Takapapatapaka@lemmy.world
on 11 Jun 14:38
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Here is the main blog post that i remembered : it has a follow up, a more scientific version, and uses two other articles as a basis, so you might want to dig around what they mention in the introduction.
It is indeed a quite technical discovery, and it still lacks complete and wider analysis, but it is very interesting for the fact that it kinda invalidates the common gut feeling that llms are pure lucky random.
sugar_in_your_tea@sh.itjust.works
on 10 Jun 16:49
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Yeah, a lot of them hallucinate illegal moves.
surph_ninja@lemmy.world
on 10 Jun 17:35
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This just in: a hammer makes a poor screwdriver.
WhyJiffie@sh.itjust.works
on 10 Jun 17:50
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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.)
cley_faye@lemmy.world
on 10 Jun 17:27
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Ah, you used logic. That’s the issue. They don’t do that.
Sidhean@lemmy.blahaj.zone
on 10 Jun 18:07
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Can i fistfight ChatGPT next? I bet I could kick its ass, too :p
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.
Then again, our corporate lords and masters are trying to replace all manner of skilled workers with those same LLM “AI” tools.
And clearly that will backfire on them and they’ll eventually scramble to find people with the needed skills, but in the meantime tons of people will have lost their source of income.
If you believe LLMs are not good at anything then there should be relatively little to worry about in the long-term, but I am more concerned.
It’s not obvious to me that it will backfire for them, because I believe LLMs are good at some things (that is, when they are used correctly, for the correct tasks). Currently they’re being applied to far more use cases than they are likely to be good at – either because they’re overhyped or our corporate lords and masters are just experimenting to find out what they’re good at and what not. Some of these cases will be like chess, but others will be like code*.
(* not saying LLMs are good at code in general, but for some coding applications I believe they are vastly more efficient than humans, even if a human expert can currently write higher-quality less-buggy code.)
yeah, we agree on this point. In the short term it’s a disaster. In the long-term, assuming AI’s capabilities don’t continue to improve at the rate they have been, our corporate overlords will only replace people for whom it’s actually worth it to them to replace with AI.
FourWaveforms@lemm.ee
on 11 Jun 19:44
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If you don’t play chess, the Atari is probably going to beat you as well.
LLMs are only good at things to the extent that they have been well-trained in the relevant areas. Not just learning to predict text string sequences, but reinforcement learning after that, where a human or some other agent says “this answer is better than that one” enough times in enough of the right contexts. It mimics the way humans learn, which is through repeated and diverse exposure.
If they set up a system to train it against some chess program, or (much simpler) simply gave it a tool call, it would do much better. Tool calling already exists and would be by far the easiest way.
It could also be instructed to write a chess solver program and then run it, at which point it would be on par with the Atari, but it wouldn’t compete well with a serious chess solver.
stevedice@sh.itjust.works
on 11 Jun 21:52
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2025 Mazda MX-5 Miata ‘got absolutely wrecked’ by Inflatable Boat in beginner’s boat racing match — Mazda’s newest model bamboozled by 1930s technology.
untakenusername@sh.itjust.works
on 12 Jun 03:46
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this is because an LLM is not made for playing chess
threaded - newest
This made my day
Get your booty on the floor tonight.
There was a chess game for the Atari 2600? :O
I wanna see them W I D E pieces.
I wasn’t aware of that either, now I’m kinda curious to try to find it in my 512 Atari 2600 ROMs archive…
Prepare to be delighted. Full disclosure, my Atari isn’t hooked up and also I don’t have the Video Chess cart even if it was, so this was fetched from Google Images.
<img alt="" src="https://lemmy.world/pictrs/image/52e1821e-bce6-4e4e-a723-55bee2fcd109.png">
Those are some funky looking knights lol
Never seen a snake/horse hybrid before?
<img alt="" src="https://lemmy.world/pictrs/image/635c8142-00c4-4a4b-bc52-bb84e1ca909e.png">
Snorse
There's some very odd pieces on high dollar physical chess sets too.
I bet that's a slightly unfair representation of what it actually looked like. Graphics back then were purposely designed for how they would look on CRT tvs which add a lot of specific distortions to images. So taking a screenshot of a game running in an emulator without using a high quality crt filter added to the image will be a very untrue representation of what the game actually looked like.
(Don't get me wrong, I'm not saying it actually looked great when displayed correctly, but i am saying it would've looked considerably better than this emulator screenshot)
Can confirm.
And if you play it on expert mode, you can leave for college and get your degree before it’s your turn again.
I’m annoyed the pieces are bottom adjusted…
Here you go (online emulator): www.retrogames.cz/play_716-Atari2600.php
WTF? I just played that just long enough for my queen to take over their queen, and it turned my queen into a rook?
Is that even a legit rule in any variation of chess rules?
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.
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.
…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.
I think they’re trying to do that. But AI can still fail at that lol
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!
Because the LLMs are now being used to vibe code themselves.
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.
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.
They are starting to do this. Most new models support function calling and can generate code to come up with math answers etc
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.
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.
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.
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?”
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.
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.
they are craming ai everywhere because nobody wants to miss the boat and because it plays well in the stock market.
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.
Pfft, okay.
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.
My point is people aren’t expecting AGI. People have already tried them and understand what the general capabilities are. Businesses today even more. I don’t think exaggerating the capabilities is such an overarching issue, that anyone could call the whole thing a scam.
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.
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”
.
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.
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.
In all fairness. Machine learning in chess engines is actually pretty strong.
www.chess.com/terms/alphazero-chess-engine
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.
Sure, but machine learning like that is very different to how LLMs are trained and their output.
I mean, open AI seem to forget it isn’t.
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 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.
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.
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.
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.
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.
OpenAI has been talking about AGI for years, implying that they are getting closer to it with their products.
openai.com/index/planning-for-agi-and-beyond/
openai.com/…/elon-musk-wanted-an-openai-for-profi…
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?
The Atari chess program can play chess better than the Boeing 747 too. And better than the North Pole. Amazing!
Are either of those marketed as powerful AI?
Neither of those things are marketed as being artificially intelligent.
Marketers aren’t intelligent either, so I see no reason to listen to them.
You’re not going to slimeball investors out of three hundred billion dollars with that attitude, mister.
I suppose it’s an interesting experiment, but it’s not that surprising that a word prediction machine can’t play chess.
Because people want to feel superior because they
don’t know how to use a ChatBotcan count the number of "r"s in the word “strawberry”, lolYeah, just because I can’t count the number of r’s in the word strawberry doesn’t mean I shouldn’t be put in charge of the US nuclear arsenal!
That is more a failure of the person who made that decision than a failing of ChatBots, lol
Anyone who puts a chatbot anywhere is definitely a failure, yeah.
Agreed, which is why it’s important to have articles out in the wild that show the shortcomings of AI. If all people read is all the positive crap coming out of companies like OpenAI then they will make stupid decisions.
A strange game. How about a nice game of Global Thermonuclear War?
No thank you. The only winning move is not to play
I've heard the only way to win is to lock down your shelter and strike first.
Lmao! 🤣 that made me spit!!
Frak off, toaster
<img alt="" src="https://lemmy.world/pictrs/image/e2aec304-fee3-42a9-85bc-b96c5912e328.gif">
JOSHUA
I’m often impressed at how good chatGPT is at generating text, but I’ll admit it’s hilariously terrible at chess. It loves to manifest pieces out of thin air, or make absurd illegal moves, like jumping its king halfway across the board and claiming checkmate
It can be bad at the very thing it's designed to do. It can repeat phrases often, something that isn't great for writing. But why wouldn't it, it's all about probability so common things said will pop up more unless you adjust the variables that determine the randomness.
Yeah! I’ve loved watching Gothem Chess’ videos on these. Always have been good for a laugh.
ChatGPT is playing Anarchy Chess
They used ChatGPT 4o, instead of using o1 or o3.
Obviously it was going to fail.
Other studies (not all chess based or against this old chess AI) show similar lackluster results when using reasoning models.
Edit: When comparing reasoning models to existing algorithmic solutions.
Next, pit ChatGPT against 1K ZX Chess in a ZX81.
This isn’t the strength of gpt-o4 the model has been optimised for tool use as an agent. That’s why its so good at image gen relative to other models it uses tools to construct an image piece by piece similar to a human. Also probably poor system prompting. A LLM is not a universal thinking machine its a a universal process machine. An LLM understands the process and uses tools to accomplish the process hence its strengths in writing code (especially as an agent).
Its similar to how a monkey is infinitely better at remembering a sequence of numbers than a human ever could but is totally incapable of even comprehending writing down numbers.
Do you have a source for that re:monkeys memorizing numerical sequences? What do you mean by that?
www.youtube.com/watch?v=MKvX9PPmI-Q
That threw me as well.
.
If you're writing a novel simulation for a non-trivial system, it might be best to learn to code so you can identify any issues in the simulation later. It's likely that LLMs do not have the information required to generate good code for this context.
You’re right. I’m not relying on this shit. It’s a tool. Fucking up the gui is fine, but making any changes I don’t research to my simulator core could fuck up my whole project. It’s a tool that likes to cater to you, and you have to work around that - really, not too different from how much pressure you put on a grinder. You gotta learn how to work it. And, you’re sentiment is correct. My lack of programming experience is a big hurdle I have to account for and make safeguards against. It would be a huge help if I started from the basics. But, I mean, I also can’t rub two sticks together to heat my home. Doesn’t mean I can’t use this tool to produce reliable results.
The tough guys and sigma males of yester-year used to say things like “If I were homeless, I would just bathe in the creek using the natural animal fats from the squirrel I caught for dinner as soap, win a new job by explaining my 21-days-in-7 workweek ethos, and buy a new home using my shares in my dad’s furniture warehouse as collateral against the loan. It’s not impossible to get back on your feet.”
But with the advent of AI, which, actually, is supposed to do things for you, it’s completely different now.
Dude, that fucking sucks. What is wrong with you?
You’re so fucking silly. You gonna study cell theory to see how long you should keep vegetables in your fridge? Go home. Save science for people who understand things.
Does this not strike you as the least bit ironic?
You say you produce good oranges but my machine for testing apples gave your oranges a very low score.
No, more like “Your marketing team, sales team, the news media at large, and random hype men all insist your orange machine works amazing on any fruit if you know how to use it right. It didn’t work my strawberries when I gave it all the help I could, and was outperformed by my 40 year old strawberry machine. Please stop selling the idea it works on all fruit.”
This study is specifically a counter to the constant hype that these LLMs will revolutionize absolutely everything, and the constant word choices used in discussion of LLMs that imply they have reasoning capabilities.
Okay, but could ChatGPT be used to vibe code a chess program that beats the Atari 2600?
no.
the answer is always, no.
The answer might be no today, but always seems like a stretch.
I mean, that 2600 Chess was built from the ground up to play a good game of chess with variable difficulty levels. I bet there's days or games when Fischer couldn't have beaten it. Just because a thing is old and less capable than the modern world does not mean it's bad.
It’s not that hard to beat dumb 6 year old who’s only purpose is mine your privacy to sell you ads or product place some shit for you in future.
Tbf, the article should probably mention the fact that machine learning programs designed to play chess blow everything else out of the water.
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?
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
It does
It does not. Where?
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
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.
ChatGPT must adhere honorably to the rules that its making up on the spot. Thats Dallas
and still lose to stockfish even after conjuring 3 queens out of thin air lol
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.
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.
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.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.
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’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.
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 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
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*
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.
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 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
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.
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.
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:
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
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)
…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.
Hardly surprising. Llms aren’t -thinking- they’re just shitting out the next token for any given input of tokens.
That’s exactly what thinking is, though.
An LLM is an ordered series of parameterized / weighted nodes which are fed a bunch of tokens, and millions of calculations later result generates the next token to append and repeat the process. It’s like turning a handle on some complex Babbage-esque machine. LLMs use a tiny bit of randomness (“temperature”) when choosing the next token so the responses are not identical each time.
But it is not thinking. Not even remotely so. It’s a simulacrum. If you want to see this, run ollama with the temperature set to 0 e.g.
You will get the same answer every single time.
I know what an LLM is doing. You don’t know what your brain is doing.
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.
You get 2 triangles in a single square mate…
CHECKMATE!
Touchdown! 3 points!
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.
The press release where OpenAI said we’d never need chess players again
That’s just clickbait in general these days lol
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.
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.
Edit: Given the existing replies and downvotes, I think this comment is being misunderstood. I would like to try clarifying again what I meant here.
First of all, I'd like to ask if this article is satire. That's the only way I can understand the replies I've gotten that critized me on grounds of the marketing aspect of LLMs (when the article never brings up that topic itself, nor did I). Like, if this article is just some tongue in cheek type thing about holding LLMs to the standards they're advertised at, I can understand both the article and the replies I've gotten. But the article never suggests so itself. So my assumption when writing my comment was that this is not the case and it is serious.
The Atari is hardware. It can't play chess on its own. To be able to, you need a game for it which is inserted. Then the Atari can interface with the cartridge and play the game.
ChatGPT is an LLM. Guess what, it also can't play chess on its own. It also needs to interface with a third party tool that enables it to play chess.
Neither the Atari nor ChatGPT can directly, on their own, play chess. This was my core point.
I merely pointed out that it's unfair that one party in this comparison is given the tool it needs (the cartridge), but the other party isn't.
Unless this is satire, I don't see how marketing plays a role here at all.
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.
And neither did the Atari 2600 win against ChatGPT. Whatever game they ran on it did.
That's my point here. The fact that neither Atari 2600 nor ChatGPT are capable of playing chess on their own. They can only do so if you provide them with the necessary tools. Which applies to both of them. Yet only one of them was given those tools here.
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…
The Atari 2600 is just hardware. The software came on plug-in cartridges. Video Chess was released for it in 1979.
Llms useless confirmed once again
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.
Hallucinating 100% of the time 👌
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.
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.
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.
So they are both masters of troll chess then?
See: King of the Bridge
Lol. More comparisons to how AI is currently like a young child.
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 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. …stackexchange.com/…/how-strong-is-each-level-of-…
Random LLMs were nearly as good 2 years ago. lmsys.org/blog/2023-05-03-arena/
LLMs that are actually trained for chess have done much better. arxiv.org/abs/2501.17186
Wouldn’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.
lmarena.ai/leaderboard has one of the Gemini models with a rating of 1470. That’s pretty good.
So, it fares as well as the average schmuck, proving it is human
/s
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.
An LLM is a poor computational/predictive paradigm for playing chess.
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!
Here is the main blog post that i remembered : it has a follow up, a more scientific version, and uses two other articles as a basis, so you might want to dig around what they mention in the introduction.
It is indeed a quite technical discovery, and it still lacks complete and wider analysis, but it is very interesting for the fact that it kinda invalidates the common gut feeling that llms are pure lucky random.
Yeah, a lot of them hallucinate illegal moves.
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.)
Ah, you used logic. That’s the issue. They don’t do that.
Can i fistfight ChatGPT next? I bet I could kick its ass, too :p
Is anyone actually surprised at that?
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.
Then again, our corporate lords and masters are trying to replace all manner of skilled workers with those same LLM “AI” tools.
And clearly that will backfire on them and they’ll eventually scramble to find people with the needed skills, but in the meantime tons of people will have lost their source of income.
If you believe LLMs are not good at anything then there should be relatively little to worry about in the long-term, but I am more concerned.
It’s not obvious to me that it will backfire for them, because I believe LLMs are good at some things (that is, when they are used correctly, for the correct tasks). Currently they’re being applied to far more use cases than they are likely to be good at – either because they’re overhyped or our corporate lords and masters are just experimenting to find out what they’re good at and what not. Some of these cases will be like chess, but others will be like code*.
(* not saying LLMs are good at code in general, but for some coding applications I believe they are vastly more efficient than humans, even if a human expert can currently write higher-quality less-buggy code.)
The problem is that they’re being used for all the things, including a large number of tasks that thwy are not well suited to.
yeah, we agree on this point. In the short term it’s a disaster. In the long-term, assuming AI’s capabilities don’t continue to improve at the rate they have been, our corporate overlords will only replace people for whom it’s actually worth it to them to replace with AI.
If you don’t play chess, the Atari is probably going to beat you as well.
LLMs are only good at things to the extent that they have been well-trained in the relevant areas. Not just learning to predict text string sequences, but reinforcement learning after that, where a human or some other agent says “this answer is better than that one” enough times in enough of the right contexts. It mimics the way humans learn, which is through repeated and diverse exposure.
If they set up a system to train it against some chess program, or (much simpler) simply gave it a tool call, it would do much better. Tool calling already exists and would be by far the easiest way.
It could also be instructed to write a chess solver program and then run it, at which point it would be on par with the Atari, but it wouldn’t compete well with a serious chess solver.
2025 Mazda MX-5 Miata ‘got absolutely wrecked’ by Inflatable Boat in beginner’s boat racing match — Mazda’s newest model bamboozled by 1930s technology.
this is because an LLM is not made for playing chess