Not that it's wasteful to study birds. It's just somewhat ironic that studying birds teaches us very little about practical flight.
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I think it's important to learn the human mechanism for learning. It's clearly not backprop, any more than a propeller compares to bird wings. Understanding human learning would have huge implications for the field of education, rhetoric, marketing, UI design and more.
Adtech will never stop; it will spawn baby universes replete with simulated human beings complete with bad breath and failing hair follicles if they're convinced they can make one more ad dollar.
My observation of the field 25 years later is that it's still dominated by a lot of people good at math that are at this point generating results that are definitely very interesting yet not fully understood. We've gotten very good at wiring together matrix computations in interesting ways and training them to do useful stuff. But just like with real brains, we struggle to understand how the resulting networks actually work or how to design them properly.
I've seen high school kids work with tensor flow and they can definitely pull some magic tricks with that but they'd probably struggle to explain how the magic works as it's basically a black box component for them. And I suspect that is the case for large parts of the machine learning community as well. Math makes all of the lower level stuff work obviously but it's not a great tool for the more complex behavior that is basically emergent complexity of these networks. We can put them together and tinker with them but we seem to not have a deep understanding of how they work or even why they work. Very much like real brains.
As to whether biophysical learning has anything to teach us is an entirely different question which I don't discuss in the post.