This shows the downside of using AI to write up your project. I see the eloquent sentences, but don't get the message.
> This works, but the actual execution happened outside the model. The model specified the computation, then waited for an external system to carry it out.
> Our transformer also emits a program, but instead of pausing for an external tool, it executes that program itself, step by step, within the same transformer.
What's the benefit? Is it speed? Where are the benchmarks? Is it that you can backprop through this computation? Do you do so?
Why is it good that it's "inside" the model? Just making it more elegant and nice? The tool was already "inside" the overall hybrid system. What's the actual problem?
>This shows the downside of using AI to write up your project. I see the eloquent sentences, but don't get the message.
Not really sure what this obsession with calling things you don't like AI generated is but it's poor form. If you have something to say about the text then say it. Otherwise leave baseless accusations out of it.
>What's the benefit? Is it speed? Where are the benchmarks? Is it that you can backprop through this computation? Do you do so?....
It's pretty clearly an ideological thing. Some people are firmly on the 'some sort of symbolic logic is necessary' camp. From the article, 'A system that cannot compute cannot truly internalize what computation is.'
Some things are just interesting for the sake of it. This is one of those things. I don't agree with the authors on the above and I'm still glad they shared. It's a very interesting read regardless.
Honestly, the most interesting thing here is definitely that just 2D heads are enough to do useful computation (at least they are enough to simulate an interpreter) and that there is an O(log n) algorithm to compute argmax attention with 2D heads. It seems that you could make an efficient pseudosymbolic LLM with some frozen layers that perform certain deterministic operations, but also other layers that are learned.
Interesting... But why? What is the benefit, other than increasing our understanding of model architectures?
Our brains can also simulate turing machines, slowly. We automated that with computers that are faster and more reliable. So why not allow a model to use external much faster and reliable tools, just as we do?
This seems a really interesting path for interpretability, specially if a big chunk of a model's behavior occurs pseudo-symbolically. This is an idea I had thought about, integrating tools into the main computation path of a model, but I never imagined that it could be done efficiently with just a vanilla transformer.
I'd like to see this combined with reinforcement learning to optimize models to think computationally. Generating ideas with hypothetical results and then running them in the same thought. Their solution sounded like a lot of tokens though.
one of the most interesting pieces I've read recently. Not sure I agree with all the statements there (e.g. without execution the system has no comprehension) - but extremely cool
This shows the downside of using AI to write up your project. I see the eloquent sentences, but don't get the message.
> This works, but the actual execution happened outside the model. The model specified the computation, then waited for an external system to carry it out. > Our transformer also emits a program, but instead of pausing for an external tool, it executes that program itself, step by step, within the same transformer.
What's the benefit? Is it speed? Where are the benchmarks? Is it that you can backprop through this computation? Do you do so?
Why is it good that it's "inside" the model? Just making it more elegant and nice? The tool was already "inside" the overall hybrid system. What's the actual problem?
>This shows the downside of using AI to write up your project. I see the eloquent sentences, but don't get the message.
Not really sure what this obsession with calling things you don't like AI generated is but it's poor form. If you have something to say about the text then say it. Otherwise leave baseless accusations out of it.
>What's the benefit? Is it speed? Where are the benchmarks? Is it that you can backprop through this computation? Do you do so?....
It's pretty clearly an ideological thing. Some people are firmly on the 'some sort of symbolic logic is necessary' camp. From the article, 'A system that cannot compute cannot truly internalize what computation is.'
Some things are just interesting for the sake of it. This is one of those things. I don't agree with the authors on the above and I'm still glad they shared. It's a very interesting read regardless.
Honestly, the most interesting thing here is definitely that just 2D heads are enough to do useful computation (at least they are enough to simulate an interpreter) and that there is an O(log n) algorithm to compute argmax attention with 2D heads. It seems that you could make an efficient pseudosymbolic LLM with some frozen layers that perform certain deterministic operations, but also other layers that are learned.
Interesting... But why? What is the benefit, other than increasing our understanding of model architectures?
Our brains can also simulate turing machines, slowly. We automated that with computers that are faster and more reliable. So why not allow a model to use external much faster and reliable tools, just as we do?
This seems a really interesting path for interpretability, specially if a big chunk of a model's behavior occurs pseudo-symbolically. This is an idea I had thought about, integrating tools into the main computation path of a model, but I never imagined that it could be done efficiently with just a vanilla transformer.
Truly, attention is all you need (I guess).
This is brilliant, game changing level.
Hey, give it also access to the dump of its weights and way to propose updates so it can see and tinker its brain directly.
Is this genius? Or just a new binary executable format? Can't tell.
I'd like to see this combined with reinforcement learning to optimize models to think computationally. Generating ideas with hypothetical results and then running them in the same thought. Their solution sounded like a lot of tokens though.
big question is how efficient is this compare to executing assembly on CPU
one of the most interesting pieces I've read recently. Not sure I agree with all the statements there (e.g. without execution the system has no comprehension) - but extremely cool
It makes sense that a next token predictor could execute assembly code. This is fascinating work, especially with the memory implementation.
what!