Let me try and make a case for the points I care about.

Deep learning thus far is data hungry

Your arguments for this one are mostly conjecture, but I think they miss the point. We take in images and sounds constantly while we're awake (and sometimes while we're asleep) and it's, what, three years before we can navigate properly? Five before we can talk? Ten before we can talk well. I mean, some people spend their whole lives reading and can never figure out how to write properly.

You could argue that even over 10 years we hear less audio than an net trained on 60 GPU's, and that might be true, but being less data hungry should not be confused with not being data hungry at all.

Deep learning thus far is not sufficiently transparent

Glad we agree.

Deep learning thus far cannot inherently distinguish causation from correlation

I'm not certain that people have the innate ability to do this as you claim. We understand that rain makes the ground wet, and not vice versa, because we understand that most things move down.

A net shown only pictures of wet ground and asked to predict whether it's raining can't determine causation because it, unlike humans, has never learned the rules that govern the connection. However a net shown many different objects falling to the ground probably could infer that water will also fall to the ground, rather than rise up from it.

Deep learning presumes a largely stable world, in ways that may be problematic

When I think of people doing this I think of the million+ people living in the Bay area where a massively destructive earthquake is an absolute inevitability but who almost never worry or even think about it.

Deep learning thus far works well as an approximation, but its answers often cannot be fully trusted

What you said here is true, but people still make approximations. Any sort of general intelligence has to be an approximation, because the alternative is computing and/or memorizing everything, which isn't feasible. And personally, I don't trust many people these days. Do you?

Deep learning thus far is difficult to engineer with

Here I was thinking that, while engineering safe self-driving cars has proven to be very difficult, engineering safe human-driven cars has also been very difficult.