41–44 of 44 posts
We've discovered the language. It changes the economics of computing. As in, this entire cloud buildout is unnecessary because it becomes like using a calculator. Reach out to chat.
If you look at convolutional neural nets used in image processing, it's super common for the first layer or so to learn a family of wavelet basis functions. Later layers then do recognition in wavelet space, without that space ever being explained or communicated to the training algorithm. This work here is obviously more complex than that, but suggests something similar is going on with early layers transforming to…
Maybe in the same way fourier/wavelet basis is just the most natural way to work with certain signals or images, there's a certain representation for language and a representation for "thinking" that's natural. Maths could itself be said to be an abstract representation of the latter, and even if the idea of a "universal grammar" is dead, maybe there's still some natural space to work with all human languages owing to the shared biological priors we have as humans.
This was a little dense for me to grok. Are these well known results or is there an abstract-like summary? The RYS (repeat yourself) hypothesis that duplicating (the right) layers is enough to improve performance (sorry for not reading closely enough, it's really just stacking the relevant layers?). The ERD (encoding, reasoning, decoding) layer structure is a relatively robust observation? That the middle layers of t…
I find the RYS result far more surprising than the ERD result. Encode-Reasoning-Decode is after all a very popular way to design neural networks (even an autoencoder is just that without the reasoning step), the same structure emerging from optimization isn't that surprising. But the methodology to measure it and put numbers on which layers are most involved in encoding/decoding and where the reasoning takes place is…
But the fact that the intermediary circuits are generic and robust enough that you can just loop them is unexpected. I mean maybe it sort of makes sense in retrospect, the above and other papers showed the middle layers of an LLM behave more like "iterative refinement", so to use a signal processing analogy maybe you just keep applying filters and suppress the noise.
But by that same analogy, I'd predict that you can't just keep repeating layers, at some point you'll suppress the signal as well. Not sure if there was an experiment conducted with how many times you can repeat RYS layers before performance goes back down.