Abstract: Machine learning (ML) methods are having a huge impact across all of the sciences. However, ML has a strong ontology - in which only the data exist - and a strong epistemology - in which a model is considered good if it performs well on held-out training data. These philosophies are in strong conflict with both standard practices and key philosophies in the natural sciences. I identify some locations for ML in the natural sciences at which the ontology and epistemology are valuable. I also show that there are contexts in which the introduction of ML introduces strong, unwanted statistical biases. My partial answers I provide (to the question in my title) come from the particular perspective of physics.

Work in collaboration with Soledad Villar at JHU.

https://cosmo.nyu.edu/hogg/