I mean .. I disagree? It's certainly simple to give "a sense" of how deep learning works - 1) translate real-world states and behaviors into data structures (

I mean .. I disagree? It's certainly simple to give "a sense" of how deep learning works - 1) translate real-world states and behaviors into data structures (typically multi-dimensional arrays); 2) use (relatively straightforward) math to find patterns in the large matrices; 3) assume those patterns are representative of the real-world states and behaviors it translated; 4) produces a model which "works in reverse" i.e. uses the patterns it found to generate real-world states and behaviors, particularly states and behaviors it has never translated or seen before. And therefore what it's good at - math, finding patterns - are simple to get a sense of, what it's bad at - assuming the patterns it's found are representative - is simple to understand, and why it feels like magic - the speed at which it can acquire a skill, that it's trained solely through observation - and why it doesn't feel like magic - it's just math and pattern-matching, things we teach in preschool. Every new breakthrough in AI/DL research is either "more math", "same math but faster", "more patterns", "better patterns" ... Always good to read and see how very smart people solved problems, but the underlying principles are really simple. What's way harder to wrap your head around is the distributed compute and data pipelines and hardware architectures required to support the scales at which these simple mathematical pattern-matching algorithms need to operate to be useful.