Most machine learning algorithms are by design resilient to random noise. They can even be learned to be resilient to systematic noise (due to say.. variable hardware performance in mass production).
In those cases; a low power analog device with lower reliability guarantees is 'ok'.
I suspect the "decision engines" (the computation enforcing complex logic based on sensor readings) will probably continue to be on CPU's for some time.
It would surprise me if even unfiltered analog noise in the sensors significantly changed recall. I suppose I don't know off-hand know the magnitude of noise in typical analog systems.
As a proof of concept they did a GPS chip , with 66 times higher energy efficiency.
There are others working on analog neural compute , some released good research results .
Given all these constraints I am actually quite excited to hear more of the dissenting view. Can you describe some of the research you mentioned in the last sentence? I have not heard of it and if true it would be at least interesting engineering, even if right now I doubt it would actually work.