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The issue is that "understanding" is a sliding scale. Obviously today's AI systems (which are virtually all hybrids) understand some things and not others. There is plenty of room for improvement!
operationally: answer questions about who did what where when why and how. what motivations were, what agents intended to achieve, what obstacles they faced, what sequences of events was etc. stuff we expect of any normal reader. discussion: http://rebooting.ai chapter 4
Yes, I agree that those are all things that are beyond the state of the art. The part of the glass that's half full includes lots of queries to google, alexa, etc. that are understood correctly, with significant contributions from deep learning.
we make exactly this distinction in http://rebooting.ai in chapter 4; the half full part of glass is more like text processing than true comprehension, though.
hinton’s dismissal of my quote is not warranted if the facts are as @tdietterich specified.
As you know the AGI community has been discussing understanding for years. In my 2013 book "Approximation Zero" I argue that knowing the grounds for understanding (eg., causes, laws) can be an infinite regress of grounds, but not necessarily == understanding the grounds.
So you define understanding as a QA task, a pretty hard one. It's not like current state of the art models fail completely at it, and it's not like they suceed perfectly either. But so much progress has been made and models are getting very good at QA.
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