The great thing about Stan is it is geared toward practitioners doing real work in science and social science, but still manages to push the boundary of stats r

The great thing about Stan is it is geared toward practitioners doing real work in science and social science, but still manages to push the boundary of stats research (NUTS, ADVI, penalized ML). Other probabilistic programming languages are more expressive, but are typically much harder to use for day-to-day work. That being said, there's still a huge gulf between practitioners in science and social science that are building parametric Bayesian models and practitioners in the deep learning / ML community that are focused on building more general, scalable, machine learning algorithms for tasks like machine translation and question answering. It would be amazing if somebody could reconcile these communities by showing deep learning models can be expressed and fit effectively side-by-side with more parametric models.