Today was the first day of a two-day symposium on the impact of Generative AI in physics. It is hosted by IAIFI and A3D3, two interdisciplinary and inter-institutional entities working on things related to machine learning. I really enjoyed the content today. One example was Anna Scaife (Manchester) telling us that all the different methods they have used for uncertainty quantification in astronomy-meets-ML contexts give different and inconsistent answers. It is very hard to know your uncertainty when you are doing ML. Another example was Simon Batzner (DeepMind) explaining that equivariant methods were absolutely required for the materials-design projects at DeepMind, and that introducing the equivariance absolutely did not bork optimization (as many believe it will). Those materials-design projects have been ridiculously successful. He said the amusing thing “Machine learning is IID, science is OOD”. I couldn't agree more. In a panel at the end of the day I learned that learned ML controllers now beat hand-built controllers in some robotics applications. That's interesting and surprising.
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