Yes. That's the classic observation about deep learning - you can get to 90+% right, at the cost of a few percent not even close.

We are very likely going to need to revisit a once-popular idea that Hinton seems devoutly to want to crush: the idea of manipulating symbols—computer-internal encodings, like strings of binary bits, that stand for complex ideas.

Maybe. That's "good old-fashioned AI".

I don't know the answer, but I think I know the right question. What we don't have in AI, via either route, is "common sense". Define common sense as getting through the next 30 seconds of life without screwing up badly. This is a concrete definition you can work with.

Now, most of the mammals exhibit some competence in this area. That's significant. It means that language is not essential to this. Symbol manipulation probably isn't. What is? Don't know.

Closely related is robotic manipulation in unstructured environments. People have been trying to do that for fifty years now, with very limited success. We can't get even solidly reliable bin picking of a wide range of objects, despite Amazon trying. Remember the DARPA humanoid challenge fiasco? Rethink Robotics? The fact that deep learning doesn't help with what's a trivial task for a squirrel indicates we are missing something big.

I have no clue how to address this problem. I've tried some things over the years, but they were all dead ends. We're stuck until someone has an insight that gets us unstuck. Or until someone can reverse engineer a mouse brain. If we can get to low-end mammal performance, there's hope.