There's a big difficulty difference between Engineering, and Reverse Engineering. If you want to build a supersonic fighter, it's a lot easier if you can look

There's a big difficulty difference between Engineering, and Reverse Engineering. If you want to build a supersonic fighter, it's a lot easier if you can look at how someone else has done it. This focuses your search to certain parts of the possibility space, saving tons of time. It gives you proof that certain techniques can in fact work. This is why the proof-of-concept, or technology demonstrater, is such an important milestone. It's why countries do so much technology espionage. Maybe it takes us a of computation to get the first NN that achieves a given performance level on a task we care about. But then we have an artefact we can reverse engineer. At a minimum, we can compress it, sure. But ultimately we can do science on it to find out how it's doing what it's doing, to look for the learned architectures within. It's going to be a lot easier to do this on a deep learning system, than a biological one. Maybe we wouldn't do this while it's cheap to just scale compute, but eventually we will. And the new understanding we get will probably enable us to build new generations that are even more efficient. This is really just a gut take, but it's an argument for optimistism here.