We Can't Trust Deep Learning Alone

Its roughly the 65th anniversary of the proposal of AI.   Time to rethink the broad idea.   More comments on a book I have been reading: Rebooting AI: Building Artificial Intelligence we can Trust by Gary Marcus.  I am a practitioner in the space, who has built many systems of this type, but remain a proponent of the fact that we must combine Deep Learning with logic processing (or classical) AI. 

We used learning in such systems, but it was not deep, but did contain and update knowledge needed to make decisions.   How can we make AI both broad and robust?  Today we have other ideas that can help us build logical models of things, like Business Process Models and RPA.  Minsky's Society of Mind is mentioned as a broad template.

Here interview in Technology Review on the idea:

We can’t trust AI systems built on deep learning alone

Gary Marcus, a leader in the field, discusses how we could achieve general intelligence—and why that might make machines safer.   by Karen Hao  in MIT Technology Review

Gary Marcus is not impressed by the hype around deep learning. While the NYU professor believes that the technique has played an important role in advancing AI, he also thinks the field’s current overemphasis on it may well lead to its demise. ..."

Finished, I like the thoughts provided.   The book sets the stage.  Read it. My only disappointment is though the book provides an excellent argument for why, it does not provide a good recommendation of how we should proceed.   Always thought there were hints in elements of the context of 'causality' that might help.  Now reading Judea Pearl's  "The Book of Why: The New Science of Cause and Effect" on that topic.