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I spent the weekend debating ai timelines, among other things, at the curve conference Lambert highlights that while you can prompt a model to use tools like search or code execution, getting the model to reliably learn when and how to use them through rl is much harder. This translates as spending the weekend thinking about the trajectory of ai progress with a mix of dc and sf types
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Thoughts on the curve conference, where prominent figures debated about ai progress, and why automating research engineers is plausible within years — the conference and the trajectory. One of the most fascinating threads is the challenge of incorporating tool use into rl frameworks Among other thoughts on a great conference
The curve is a new style of mini ai conference to debate ai progress
Here i reflect on it and explain why the argument that ai will fully replace human research engineers, and then scientists, is far fetched in the years of compute scarcity. Nathan is a machine learning researcher who works on building, understanding, and advocating for open language models and other responsible autonomous systems Papers → google scholar, my cv, semantic scholar, see phd thesis Talks → youtube channel or cv.
Most of my work now is just that — i'm prioritizing the times to write when i feel my thoughts coming together and i sit down to finish them off Without prioritizing writing, it'll often feel like you're trying to put together puzzle pieces where the edges have been bent or torn. Notes and predictions for the next phase of chinese (open) ai models From the obvious names to those to keep an eye on
The cutting edge of ai, from inside the frontier ai labs, minus the hype
Read by leading engineers, researchers, and investors.
