The smattering of new techniques seem to have made the difference between success on toy problems vs. being able to match or exceed human performance on many di

The smattering of new techniques seem to have made the difference between success on toy problems vs. being able to match or exceed human performance on many difficult tasks. So while naysayers are correct that "the math hasn't changed since the 90s!", enough has changed to make calling DL a paradigm shift accurate. For reference, I can now get an intern to images for a few hours, then train a black box algorithm to automate their efforts in another few hours. This algorithm is sensitive, brittle, and may have perfomance issues, but it's still already orders of magnitudes better than what took days or months of effort prior. That to me is a revolution, regardless of the math.