AI can't cross this line and we don't know why.

Please someone correct me if I’m wrong, but my understanding of ML/LLMs is that this kind of hand crafting has been tried, but it is easier to train/less finicky to let behavior like this emerge from more data, see [1] “Bitter Lesson” and [2] “Scaling Laws”. MAMBA as an architecture claims to have some significant gains performance wise, but to my knowledge there haven't been any really large models (>~100B params) with open weights/leaked MAMBA architecture been disclosed other than this (7B). As mentioned by other comments, another dimension not to forget is the training data quality. Not only quantity but also quality really matters, is what we are learning more and more with LLMs.. [1] [LINK] [2] see eg [VIDEO] for a well made/easily digestable intro

My attempt at a summary: authors characterize the data-compute pareto front (aka how bitter is the lesson, exactly?) For a different perspective, error vs compute, see [VIDEO] and comments (I particularly liked the one about string theorists rediscovering a fundamental theorem in GR decades too late-- rediscovering how to integrate happens in every field, it's nothing to be ashamed of :)

I'm not deep enough in the ecosystem to ingest that paper but it's title reminded me of an interesting video I saw recently: "AI can't cross this line and we don't know why" [VIDEO]

Because they are hitting Compute Efficient Frontier. Models can't be much bigger, there is no more original data on the internet, so all models will eventually cluster to similar CEF as was described in this video 10 months ago [VIDEO]

Yep we do. There is a 1 year old video on YouTube, which describes this limitation [VIDEO] Called efficient compute frontier