Regarding 1)
I am not sure if you are not trading "high human efficiency" against increased risk of blowing up at some point. Good luck doing forecasting without thorough understanding of priors and statistics in general.
I am not sure if you are not trading "high human efficiency" against increased risk of blowing up at some point. Good luck doing forecasting without thorough understanding of priors and statistics in general.
I guess this is echoing your point 2, but I would have generally said that "principled" statistical models are less efficient these days than DL (see: HMC being much slower than variational Bayes). Priors are usually overrated but I think the risk is more that basic mistakes are made because people don't understand what assumptions go into "basic" machine learning ideas like train/test splits or model selection. I'm not sure it warrants a lot of panic though.