11 stories
The impetus for doing this was provided in large part by Charles Renshaw-Whitman.Learning dynamics people suggested to me that noise was essential in the training of neural networks.
Nathan Labenz shares takeaways from The Curve conference regarding frontier AI scaling and compute limits. The episode also features discussions on hardware memory bottlenecks, how agents are changing software, training data sources, and unaddressed AI risks.
Muon: The Orthogonalized Optimizer Explained Through Equations, Intuition, Code and Infographic From SGD, to Adam’s per-coordinate scaling, to the five Newton–Schulz steps that turn a gradient …
A summary of METR's independent, predeployment evaluation of Claude Opus 5.5
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Tom Cunningham and Nate Rush look at public time series of discoveries in cyber, math, and algorithms to see whether LLMs have accelerated the discovery rate.
Welcome to Import AI, a newsletter about AI research. Import AI runs on arXiv, cappuccinos, and feedback from readers. If you’d like to support this, please subscribe.
Tom Cunningham surveys metrics for comparing AI agent capability as performance changes with expenditure and relative to human performance.
We propose a measure of an AI agent’s optimization ability with an "expenditure horizon." We give an empirical illustration from the NanoGPT speedrun.
Welcome to Import AI, a newsletter about AI research. Import AI runs on arXiv, cappuccinos, and feedback from readers. If you’d like to support this, please subscribe.
Classifying human and agent contributions to the NanoGPT speedrun, and what publicly tracked challenges can tell us about AI R&D acceleration.
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