Today was day 2 of Machine Learning for Science: Bridging Data-driven and Mechanistic Modeling at Schloss Dagstuhl. Many great things happened. Here are two highlights:
Bernhard Schölkopf (MPI-IS), in a discussion session, asked what the key questions were for machine learning as a field. I love this question! Astronomy and physics do, I think, have key questions, which guide research and contextualize choices. Machine learning does not really, or if it does, the questions are implicit. I want to work on this.
Philipp Hennig (Tübingen) gave an energizing talk about the relationship between simulations of the world and observations of (or data about) the world. He argued (convincingly!) that we should not think of these as totally different things, and that learning from data and simulating a process could or even should always be integrated and done together. He demonstrated this with a simple model of infectious disease, but the point is extremely general.
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