The trick to accurate interpretability is to decouple accuracy from explanations. Just like an International Master commentator can explain most of the moves o

The trick to accurate interpretability is to decouple accuracy from explanations. Just like an International Master commentator can explain most of the moves of a Super GM, so can an interpretable simple model explain the predictions of a very complex black box model. The work by Caruana referenced in this article actually culminated in a method to get both very accurate models and still retain interpretability. https://vimeo.com/125940125 https://vimeo.com/125940125 http://www.cs.cornell.edu/~yinlou/projects/gam/ http://www.cs.cornell.edu/~yinlou/projects/gam/ More recently there was LIME: https://homes.cs.washington.edu/~marcotcr/blog/lime/ https://homes.cs.washington.edu/~marcotcr/blog/lime/ And there are workshops: http://www.blackboxworkshop.org/pdf/Turner2015_MES.pdf http://www.blackboxworkshop.org/pdf/Turner2015_MES.pdf We will get there. 'Permanent' is a very long time and in the grand scale of things, deep learning is relatively new.