Interestingly, Hinton is on record as essentially saying that there's a good possibility that what's currently being done is wrong - and that we need to rethink our approach.
Mainly in the idea/concept of back-propagation. It's something that I've thought about myself. For the longest time, I could never understand how it worked, then I went thru Ng's "ML Class" (in 2011, which was based around Octave), and one part was developing a neural network with backprop - and the calcs being done using linear algebra. It suddenly "clicked" for me; I finally understood (maybe not to the detailed level I'd like - but to the general idea) how it all worked.
And while I was excited (and still am) by that revelation, at the same time I thought "this seems really overly complex" and "there's no way this kind of thing is happening in a real brain".
Indeed, as far as we've been able to find (although research continues, and there's been hints and model which may challenge things) - brains (well, neurons) don't do backprop; as far as we know, there's no biological mechanism to allow for backprop to occur.
So how do biological brains learn? Furthermore, how are they able to learn from only a very few examples in most cases (vs the thousands to millions examples needed by deep learning neural networks)?
We've come up with a very well engineering solution to the problem, that works - but it seems overly complex. We've essentially have made an airplane that is part ornithopter, part fixed-wing, part balloon, and part helicopter. Sure it flies - but it's rather overly complex, right?
Humanity cracked the nut when it came to heavier-than-air flight when it finally shed the idea that the wings had to flap. While it was known this was the way forward long before the Wright's or even Langley (and likely even before Lilienthal), a lot of wasted time and effort went into flying machines with flapping wings, because it was thought that "that's the way birds do it, right"?
So - in addition to the idea that backprop may not be all it's cracked up to be - what if we also need to figure out the "fixed wing" solution to artificial intelligence? Instead of trying to emulate and imitate nature so closely, perhaps there's a shortcut that currently we're missing?
I do recall a recent paper that was mentioned here on HN that I don't completely understand - that may be a way forward (the paper was called "Neural Ordinary Differential Equations"). Even so, it too seems way too complex to be a biologically plausible model of what a brain does...