Agreed. It's been stated time and time again that the next frontier of AI will come from a paradigm shift. Deep learning is great, but it is, at the end of the day statistical model fitting. Can it get us to the next frontier? Sure, as much as you can simulate a turing machine on a calculator and then simulate an R/B tree on that. Using ML to reach the next stage (of making algorithms capable of generic learning without being finely tuned) might be the hard way, and we may be approaching the mirror at mach speed. When, how, what, or why this paradigm shift will happen - no one knows, but we all know the alternative amounts to luck of the draw.

I've largely been of the camp that ML/DL will be the herald of the next AI winter, and while GPT-3 is impressive, it has no new constraints that we haven't already seen before, and doesn't break any that needed to be broken to change the field (NLP notwithstanding). Soon enough, the limitations of ML/DL will be apparent, and while there will be a breadth of practical applications to explore and analyze, we will be able to see clearly the boundary of how far ML/DL can take us, and will have to be content with what we have until the shift occurs.