AI can't cross this line and we don't know why. on HackerTube: recommended by 2 Hacker News commenters across 2 separate months, 2024–2025.
explainer · intermediate · evergreen
A Welch Labs explainer on neural scaling laws and the compute-efficient frontier, asking why model loss seems to hit a line it cannot cross. It draws an analogy to an ideal gas law for AI.
Recommended as a well-made, digestible introduction to scaling laws and why models cluster along a similar efficiency frontier.
“see eg for a well made/easily digestable intro”
5 Hacker News comments link this video. Each one links back to its thread.
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" ▶ this video
in “Break the quadratic wall of Transformer attention: WERSA, paper+code open source” · View on HN
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 ▶ this video for a well made/easily digestable intro
in “Zamba2-7B” · View on HN
Yep we do. There is a 1 year old video on YouTube, which describes this limitation ▶ this video Called efficient compute frontier
in “Anthropic raises $13B Series F” · View on HN
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 ▶ this video
in “GPT-5” · View on HN
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 ▶ this 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 :)
in “Value-Based Deep RL Scales Predictably” · View on HN