That's an excellent post with which I have a few minor quibbles - I started out in physics, moved to systems biology, then settled on machine learning. There are some areas of physics that are *very* biology like, and areas of biology that are very *physics like*.
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For example, almost all of the stuff from theory of elasticity is phenomenological (Cartwright is super interesting on the phenomenological nature of physical laws, though she goes too far in some places) - and a lot of systems biology IS basically statistical mechanics.
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As a software developer, it seems like our experiments are usually pretty sloppy but it could be done either way. Sometimes you do get good insights by studying performance anomalies, but we can often improve performance substantially while ignoring them.
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