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by mmoskal
451 days ago
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The bitter lesson is about the balance of human ingenuity and compute being thrown at the problem. We've seen a few years of LLM compute being scaled up 10x every year, but this is hitting limits (fabs), and we will see more human effort, as it becomes compartively cheaper. Also the current crop of models are inherently limited. Even for something as simple as following a JSON schema, models alone are not good enough [0] Of course as the Moore law refuses to die, we'll continue seeing 1.5-2x or so every year, but that's far from 10x. [0] https://openai.com/index/introducing-structured-outputs-in-t... - see plot |
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This is another one of those anec-data throw away sentences that take thousands of words to disprove - with a lot of graphs - that no one reads.
More hot takes: Moores law has been effectively dead since the Pentium 4 on CPUs. It's been dead on GPUs since 2020. Right now we're not seeing a 1.5-2x grow of compute per year. We've seen zero growth for 5. The only way that GPUs have gotten faster is by running ever hotter, and building out a trillion dollars worth of data centers.
No one cares because the current hotness in AI is transformers which are memory bound in both training and inference. If someone manages to get diffusion models to become the next hotness all of a sudden everyone will realize this is a problem since those are compute bound by a huge margin and current gen GPUs are fire hazards when ran at 100% utilization for weeks on end.