I spent the day at wise.io headquarters, hacking with Joey Richards. We worked on our insane robot model paper. Over lunch, we had a very productive discussion about the future of machine learning and statistical methods in astrophysics. Various points that came up include: Fundamentally, astronomy is an unsupervised problem, because the goal is to discover new things and new data are always different from old data. It is exceedingly valuable to have a causal model because then you can incorporate the things that you know about data generation (especially noise models) into your inference. It is important to keep nuisance parts of the model as flexible as possible because you don't want to impose structure that isn't there, nor do you want to prevent the machine from finding hidden or unknown structure. The causal-model objective and the flexible-model objective conflict a tiny bit, because the causal requirement usually limits your freedom. If Richards and I disagree on anything, it is where to set the boundary between freedom and enforced causality. Richards loves my idea of generating a completely data-driven stellar spectroscopy model that is good enough to do chemical tagging. We promised to start that discussion after this week. Great lunch and a great day!

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