Have you never figured out something by yourself? The way I learned to do Sudoku was: I was given a book of Sudoku puzzles and told “have fun”.
you said it would be impossible to train a chess playing model this century.
I didn’t say it was impossible to train an LLM to play Chess. I said it was impossible for an LLM to teach itself to play a game of similar difficulty to chess if that game is not in it’s training data.
These are two wildly different things.
Obviously LLMs can learn things that are in their training data. That’s what they do. Obviously if you give LLMs detailed step-by-step instructions for a procedure that is small enough to fit in its attention window, LLMs can follow that procedure. Again, that is what LLMs do.
What they do not do is teach themselves things that aren’t in their training data via trial-and-error. Which is the primary way humans learn things.
It seems like this would be because the transformer weights are fixed and we have not built a mechanism for the model to record things it needs to learn to improve performance or an automated way to practice offline to do so.
It’s just missing all this, like a human patient with large sections of their brain surgically removed. Doesn’t seem difficult or long term to add this does it? How many years before one of the competing AI lab adds some form of “performance enhancing fine tuning and self play”?
Have you never figured out something by yourself? The way I learned to do Sudoku was: I was given a book of Sudoku puzzles and told “have fun”.
So few shot + scratchpad ?
I didn’t say it was impossible to train an LLM to play Chess. I said it was impossible for an LLM to teach itself to play a game of similar difficulty to chess if that game is not in it’s training data.
More gut claims.
What they do not do is teach themselves things that aren’t in their training data via trial-and-error. Which is the primary way humans learn things
Setting up the architecture that would allow a pretrained LLM to trial and error whatever you want is relatively trivial. Current state of the art isn’t that competent but the backbone for this sort of work is there. Sudoku, Game of 24 solve rate is much higher with Tree of thought for instance. There’s stuff for Minecraft too.
Setting up the architecture that would allow a pretrained LLM to trial and error whatever you want is relatively trivial.
I agree. Or at least, I don’t see any reason why not.
My point was not that “a relatively simple architecture that contains a Transformer as the core” cannot solve problems via trial and error (in fact I think it’s likely such an architecture exists). My point was that transformers alone cannot do so.
You can call it a “gut claim” if that makes you feel better. But the actual reason is I did some very simple math (about the window size required and given quadratic scaling for transformers) and concluded that practically speaking it was impossible.
Also, importantly, we don’t know what that “relatively simple” architecture looks like. If you look at the various efforts to “extend” transformers to general learning machines, there are a bunch of different approaches: alpha-geometry, diffusion transformers,baby-agi, voyager, dreamer, chain-of-thought, RAG, continuous fine-tuning, V-JEPA. Practically speaking, we have no idea which of these techniques is the “correct” one (if any of them are).
In my opinion saying “Transformers are AGI” is a bit like saying “Deep learning is AGI”. While it is extremely possible that an architecture that heavily relies on Transformers and is AGI exists, we don’t actually know what that architecture is.
Personally, my bet is either on a sort of generalized alpha-geometry approach (where the transformer generates hypothesis and then GOFAI is used to evaluate them) or Diffusion Transformers (where we iteratively de-noise a solution to a problem). But I wouldn’t be at all surprised if a few years from now it is universally agreed that some key insight we’re currently missing marks the dividing line between Transformers and AGI.
You can call it a “gut claim” if that makes you feel better. But the actual reason is I did some very simple math (about the window size required and given quadratic scaling for transformers) and concluded that practically speaking it was impossible.
If you’re talking about this:
Now imagine trying to implement a serious backtracking algorithm. Stockfish checks millions of positions per turn of play. The attention window for your “backtracking transformer” is going to have to be at lease {size of chess board state}*{number of positions evaluated}.
And because of quadratic attention, training it is going to take on the order of {number or parameters}*({chess board state size}*{number of positions evaluated})^2
then that’s just irrelevant. You don’t need to evaluate millions of positions to backtrack (unless you think humans don’t backtrack) or play chess.
My point was not that “a relatively simple architecture that contains a Transformer as the core” cannot solve problems via trial and error (in fact I think it’s likely such an architecture exists). My point was that transformers alone cannot do so.
There’s nothing the former can do that the latter can’t. “architecture” is really overselling it but i couldn’t think of a better word. It’s just function calling.
Not really. The majority of your experiences and interactions are forgotten and discarded, the few that aren’t are recalled and triggered by the right input when necessary and not just sitting there in your awareness at all times. Those memories are also modified at every recall.
And that’s really just beside the point. However you want to spin it, evaluating that many positions is not necessary for backtracking or playing chess. If that’s the base of your “impossible” rhetoric then it’s a poor one.
Have you never figured out something by yourself? The way I learned to do Sudoku was: I was given a book of Sudoku puzzles and told “have fun”.
I didn’t say it was impossible to train an LLM to play Chess. I said it was impossible for an LLM to teach itself to play a game of similar difficulty to chess if that game is not in it’s training data.
These are two wildly different things.
Obviously LLMs can learn things that are in their training data. That’s what they do. Obviously if you give LLMs detailed step-by-step instructions for a procedure that is small enough to fit in its attention window, LLMs can follow that procedure. Again, that is what LLMs do.
What they do not do is teach themselves things that aren’t in their training data via trial-and-error. Which is the primary way humans learn things.
It seems like this would be because the transformer weights are fixed and we have not built a mechanism for the model to record things it needs to learn to improve performance or an automated way to practice offline to do so.
It’s just missing all this, like a human patient with large sections of their brain surgically removed. Doesn’t seem difficult or long term to add this does it? How many years before one of the competing AI lab adds some form of “performance enhancing fine tuning and self play”?
Less than a year. They probably already have toy models with periodically or continuously updating weights.
So few shot + scratchpad ?
More gut claims.
Setting up the architecture that would allow a pretrained LLM to trial and error whatever you want is relatively trivial. Current state of the art isn’t that competent but the backbone for this sort of work is there. Sudoku, Game of 24 solve rate is much higher with Tree of thought for instance. There’s stuff for Minecraft too.
I agree. Or at least, I don’t see any reason why not.
My point was not that “a relatively simple architecture that contains a Transformer as the core” cannot solve problems via trial and error (in fact I think it’s likely such an architecture exists). My point was that transformers alone cannot do so.
You can call it a “gut claim” if that makes you feel better. But the actual reason is I did some very simple math (about the window size required and given quadratic scaling for transformers) and concluded that practically speaking it was impossible.
Also, importantly, we don’t know what that “relatively simple” architecture looks like. If you look at the various efforts to “extend” transformers to general learning machines, there are a bunch of different approaches: alpha-geometry, diffusion transformers, baby-agi, voyager, dreamer, chain-of-thought, RAG, continuous fine-tuning, V-JEPA. Practically speaking, we have no idea which of these techniques is the “correct” one (if any of them are).
In my opinion saying “Transformers are AGI” is a bit like saying “Deep learning is AGI”. While it is extremely possible that an architecture that heavily relies on Transformers and is AGI exists, we don’t actually know what that architecture is.
Personally, my bet is either on a sort of generalized alpha-geometry approach (where the transformer generates hypothesis and then GOFAI is used to evaluate them) or Diffusion Transformers (where we iteratively de-noise a solution to a problem). But I wouldn’t be at all surprised if a few years from now it is universally agreed that some key insight we’re currently missing marks the dividing line between Transformers and AGI.
If you’re talking about this:
then that’s just irrelevant. You don’t need to evaluate millions of positions to backtrack (unless you think humans don’t backtrack) or play chess.
There’s nothing the former can do that the latter can’t. “architecture” is really overselling it but i couldn’t think of a better word. It’s just function calling.
Humans are not transformers. The “context window” for a human is literally their entire life.
Not really. The majority of your experiences and interactions are forgotten and discarded, the few that aren’t are recalled and triggered by the right input when necessary and not just sitting there in your awareness at all times. Those memories are also modified at every recall.
And that’s really just beside the point. However you want to spin it, evaluating that many positions is not necessary for backtracking or playing chess. If that’s the base of your “impossible” rhetoric then it’s a poor one.