Recent work has introduced a correspondence between jets and natural languages. In this talk, I will review how machine learning, with this natural language processing point of view, is changing the way we are thinking about jets. First, I will describe a very effective model for classification and regression tasks. Next, I will introduce a simplified model to aid in machine learning research for jet physics, that captures the essential ingredients of parton shower generators in full physics simulations. I will discuss how this line of research provides new insights into jets substructure and could lead to a systematic fit of a physics model to data.