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

http://www.cs.cornell.edu/~yinlou/projects/gam/

More recently there was LIME:

https://homes.cs.washington.edu/~marcotcr/blog/lime/

And there are workshops:

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.