You're thinking about this the wrong way. The system is built and delivered as it is because that's how the providers make the most money. If they cared to have it perform well in chess games, you'd see a different shape and behavior.
We shouldn't ask the multibillion dollar automated software generation system to play games with us any more than we should ask a Boeing's flight guidance system to do so.
This argument is fundamentally incompatible with all the breathless rhetoric about "AGI" coming from the providers' general direction.
The labs frequently apply their raw models to problems that do not make economic sense for their customers but that demonstrate the power and capability of their systems. These experiments can cost millions of dollars. That's not customer-shaped.
They're not going to give you access to that. It's not a product. The government might have an interest in this, but that's not something you'd be privileged to know about.
And when these labs do develop "AGI", they more than likely won't be selling it to end users. They've pretty much already said this.
So many commenters here see it as their ... duty? to argue against the most optimistic/unhinged (take your pick) arguments from "the other side" and then treat everybody who disagrees as a shill or an idiot.
Why is "being good at chess" a proxy for whatever AGI strawmen you want to argue against?
Maybe step back from your black-and-white ledge and think about discussing what's actually under discussion? For example, why or why not would an LLM be good at chess? Will they be good at chess? What technical limitations might preclude that?
>If they cared to have it perform well in chess games, you'd see a different shape and behavior.
So the things they claim are on the verge of AGI actually aren’t? They need to be trained for specific tasks?
It can’t even count the R’s in strawberry
It can’t even add numbers
It can’t even solve a millennium puzzle
It’s not even a chess GM
It’s not even beyond human capability in Go
It can’t even drive a car
It can’t even self replicate
It can’t even build weapons
It doesn’t even have feelings
So how could someone conceivably convince everyone that some system is AGI when there are still tasks that some human or group of humans can do that the system cannot?
This will only happen, in my opinion, when the model/system can self-improve at a rate that scares people.
One, you're not addressing what I wrote above and two, yes, that's absolutely correct. Doing X doesn't qualify something as AGI. If you can't X you can't be AGI. The inverse doesn't hold though.
Notably, if you have to retrain the model in order to X then it can't possibly be AGI since if it were _general_ it would be capable of figuring X out on its own having never seen it before.
Even if they solve 99% of whatever problems LLMs have, the 1% will remain the goal post, forever.
Until you get RFC-whatever from some standards body that defines what an AGI system is, it’s pointless to argue about whether something fits your own personal definition or not.
And for what it’s worth I just watched GitHub Copilot figure something out. So your definition is once again lacking.
However there are plenty of disqualifiers that are more or less universally accepted (ie the negative). In the above case it is literally by definition. Something cannot be termed general if it is incapable of generalizing.
Appealing to a standards body won't do you any good here. Those are composed of people. They exist to facilitate wide scale coordination. Their documents aren't always widely accepted. They aren't the arbiters of truth.
Which is exactly the point I’ve made repeatedly, there will always be something that they cannot do, and thus there will never be AGI. There will always be a long tail of capabilities that whatever system is created doesn’t have, and a long line of social media commenters eager to list them.
An AI controlled robot will be standing over the cooling corpse of the last human who will die certain that it wasn’t done by AGI.
> The system is built and delivered as it is because that's how the providers make the most money. If they cared to have it perform well in chess games, you'd see a different shape and behavior.
These comments indicate a complete failure to understand the technology.
I won't respond again.
Delusion runs deep in HN circles.
I say that as someone heavily invested in AI startups and projects and as someone working in the field.
I think most people on HN should touch grass and find real human contact. Lmao
Incredible reasoning all around here.
People are holding it wrong, deliberately or not. Some are inventing bad faith measures so they can claim AI sucks.
We all know AI can code, but the question it all stemmed from what if it's AGI or GM level in chess on it's own.
You can't just back pedal from the statement that apparently being able to code a chess engine is the same as being good at chess.
I can write a chess engine that beats Magnus Carlson without AI that alone neither makes me GM level or AGI or any of the other claims the above comments seem to be making?
He definitely needs to touch grass.
Y'all seem to miss the point of this forum. Building and hacking and science and engineering.
I swear there's a whole lot of you who just like to look down instead of up. There's a whole universe up there.
We know no such thing. LLMs are quite bad at generating code, worse than any capable human.
Is code omnipotent, I have been in software all my life and I would hard agree here.
Sure stuff LLMs can do with being good at parts of code reproduction is incredible. And honestly it's the new way to do a lot of things but I have not see an iota of proof that it can scale across the board.
For instance Maths is just code with different symbols and slightly less universally legible concepts.
AI is the best invention at figuring out or walking the search space and directionally doing logically computation over general software adjacent stuff.
But that's it, I am certain a bunch of companies will make a lot of money despite no AGI.
I think people either don't understand AGI or don't understand how real world works.
Until an LLM can bow it's head take responsibility for mistakes made and ensure they aren't repeated again with 100% confidence to the leadership it's inarguably a tool a rather questionable one at that.
So.. like chess?
Anyway, do you have any prediction on what LLM's can or can't do in a few years?
Also AI bros: LLM can’t beat an avg chess player. But that doesn’t mean anything. It doesn’t count
Why should that matter?
So we have a situation where very powerful and influential people are saying we will have AGI in 6 months (if we don’t already), yet the facts on the ground are so clearly pointing in the opposite direction.
>GPT-6 Astra xHigh: 0.06% rejected moves
I could see myself messing up something at some point if the board is complicated enough and trying an illegal move, perhaps if a piece somewhere would attack my king if I moved another piece. Even through I do know the rules of chess, and I have played a few games once every so often.
And the stuff i'm using LLMs daily is just fake?
I see i see. I will see myself out of this weird discussion while I let an LLM continue doing a lot of interesting things.
No, because we can, in fact, generally read the rules of a game and then follow them. It's actually a hobby for many of us.
> And the stuff i'm using LLMs daily is just fake?
This misses the point completely.
How many times do you think chess.com prevents illegal moves from being executed? Even Super GM's fall for mate-in-1's occasionally, which is functionally equivalent to missing a pin or a check. This idea that LLMs failing to only ever make legal moves undermines their intelligence doesn't pass the smell test.
Because we want to use this as a replacement for humans, and the average human can learn the rules of chess without needing to see the rules explained hundreds of thousands of times in millions of games.
So, yeah, it matters if a model has millions of examples of something in its training set and still cannot follow the rules.
Okay, lets go with that: it's the "shown the rules" bit that we are arguing about.
The argument is that a human may play maybe a dozen games after learning the rules, after which they won't be inadvertently attempting illegal moves. What we are observing with SOTA models is that, even after seeing millions of chess rules, rulebooks, actual games, etc, they still attempt illegal moves.
This does not point to generalisable and adaptable intelligence, such as we see in the average human.
See my comment here for more: https://news.ycombinator.com/item?id=49725306
It only matters if you are claiming it to be general purpose.
If you admit that it's just a collection of narrow capabilities - whose strength is mostly confined to the 1000 or so RL environments it was post-trained in, then there is of course no expectation of it being general purpose.
The AI companies seem to heavily want you to believe it is some some near human level general intelligence, so therefore pointing out all the things it can't do is very relevant.
HOW you do it makes a big difference in how you should assess the capability of the thing doing it. Stockfish will trounce any LLM, and any human, at chess, so should we say that Stockfish is smarter than both?
The claim here is not about intelligence, it is about generality. There's no doubt for me the LLMs are intelligent.
Sure they could, but that's irrelevant.
A chess position is just a matter of remembering what piece number is on each square - just a list of 64 numbers. A trained model may store a trillion numbers (weights). It could store a TON of chess positions if it needed to.
However, that's not how LLMs work. They don't memorize inputs - they predict them, based on discovering predictive patterns, and those predictive patterns are not input patterns (e.g. board positions). They are deep patterns (maybe 100 layers of abstraction removed from the input), representing partial inputs, generalized across many training samples.
> Don't you know the legend about rice grains on a chess board?
Sure, but this has nothing to do with chess, and nothing to do with how many games were in the LLM's training data.
> The claim here is not about intelligence, it is about generality. There's no doubt for me the LLMs are intelligent.
Intelligent humans created the training data, and the LLM attempts to predict (copy) the training data, so of course it looks intelligent. If I say "E=mc^2", does that make you think I am Einstein?
> don't memorize inputs - they predict them
I feel some tension here.
> rice grains on a chess board? Sure, but this has nothing to do with chess, and nothing to do with how many games were in the LLM's training data.
> just a list of 64 numbers
> remember even a few positions? Sure they could, but that's irrelevant.
I don't think you do. Or rather you do know the legend but for some funny reason seem to be unable to apply its lesson here, because you are talking about enormous terabytes of training data.
> Intelligent humans created the training data, and the LLM attempts to predict (copy) the training data, so of course it looks intelligent.
If for you it is about intelligence, I am out of this discussion.
If not, then what are you talking about ?
If yes, then what is the relevance to an LLM playing chess ?
> remember even a few positions? Sure they could
A rough estimate of number of positions across all X move games is X^10. For 15 moves it is hopeless to remember even a relatively small part of them. Typical game has 40 turns, 1 move per player, so 80 moves.
2) An LLM is not going to memorize vs generalize when there is no training pressure to do so. You might expect it to memorize book openings that occur over and over in the training data, but not some random non-celebrity game that occurs once in the Lichess dataset and is never again referred to.
> They can't possibly remember even a few positions. Don't you know the legend about rice grains on a chess board?
If the wise man was a bit wiser, he'd have asked for his rice on a snakes & ladders board (100 squares, not 64) and would have had 2^36 more rice, which is equally irrelevant.
And no an AGI system doesn't need to play chess on a certain level to be disruptive to you and me and whole industries. It only needs to be as good as a person and cheaper.
Just because you define AGI as something it doesn't has to be,doesn't mean i need to touch grass.
This chess comparision is one of the most ignorant and stupid arguments i have heard after the parrot thing
That doesn't mean it has to be extraordinary at these things. But to be AGI, it has to have some level of competency when used on problems outside its training set. In particular, it the LLMs were to install a known chess engine and run that to get the moves when asked to play chess, that would qualify for more AGI-like behavior. But really, chess is such a simplistic game that they should be able to do decently well at it even without even needing that. At the very least, they should be able to consistently play without making illegal moves - something that many 7-year olds manage quite well.
I’m sure these models know the rules and can explain them when prompted, but that doesn’t seem to be the way they actually complete this task. Will they get there? Maybe