> Because a CPU inside a language model clearly wasn't cursed enough, I decided the prudent thing would be to give it a filesystem too.
Love the spirit :)
Or the cli version if preferable: https://nicobrenner.github.io/lossy-git/
Not only can it solve the halting problem, but also the ignition problem!
[1]: https://github.com/anadim/AdderBoard [2]: hn discussion https://news.ycombinator.com/item?id=47170030&utm_source=per...
Anyways, what I’m trying to say is I want evil qwen to be my CPU.
I wonder how this squares with the recent "NNs are already clean" interpretability finding, which may have also been the impetus for this kind of research. Similar to their work on CNNs, maybe residual-steering is overkill, there might be just a single neuron that "cleanly" corresponds to "evilness" that you can turn up/down?
In a few words, they found a way to map residual stream activations (so, a vector) into a few interpretable sparse concepts. They also had the converse maps, that translate concepts into activations.
Golden Gate claude was obtained by artificially increasing the activations in the direction of the 'Golden Gate' concept. It was not neuron surgery.
They implemented wasm in transformer with cpu decoding for the wasm part.
I’ve never wanted to learn how LLMs work more than reading this. Fascinating.
Does divagr get paid to do wild things with LLMs? Just curious if they’re being compensated for these wild experiments
If you are actually good at linear algebra, it's doable in an afternoon.
They are fantastically simple in structure, all the intelligence is in the massive piles of data in them, not their structure. Then there is of course a lot of room in optimization once the basics are done.
If interested, maybe start here: https://www.deeplearning.ai/courses/how-transformer-llms-wor...
So like... can't you use a computer as a computer without doing all that roundabout stuff? Like if you want to integrate a LLM with hard computation there's probably a more efficient way to do that.
Or is this building towards "but can it run DOOM?" :)
Given that models learn to use residual connections optimally, it can’t be hard for them to steer around the fixed computation layer when it’s not needed.
I’m sure giant models invest a few layers in reproducing these functions, but surely smaller models can benefit?
A fun and important thought experiment about the the space of nerual networks vs the image of current training methods.
Most discourse conflates neural networks and trainable neural networks.
there's no free lunch here ever hence the cursed
Or maybe ill join your discord and hopefully we can riff it out to get there faster
This reminds me of C++ Template Metaprogramming (TMP) (it's Turing-complete so people wrote "games" that run at compile time). The same with TypeScript types, I think someone got DOOM running entirely in the TypeScript type system. An interesting tech demo but entirely superfluous.
We have gone down this path already in [0] and this is the same thing but overly complicated. This reason is "because LLMs".
Why?
No it isn't.
Realistically the opposite is true in practice and LLMs are really non-deterministic beyond simplistic experiments such as this in useful activities in production. Even with hacks such as "setting temp=0" (which no-one seriously does) to "achieve determinism", was one of the most common misconceptions ever propagated by "AI engineers".
So using tiny slop experiments aren't really useful as a "strong counter-example". In fact this is a very weak one.