A lot of DL is about teaching computers to solve problems that are easy for humans (like driving and recognizing your grandmother) but for which humans have a tough time explaining how they do it.
The holy grail of neural nets has always been to build a simulation of the brain, figure out how it works, and apply that knowledge to how the human brain might work.
We're not there yet but progress has been made. Eventually we'll understand NNs well enough to explain not only themselves but also human brains. In any case we have no choice because we cannot deploy NNs in life critical situations until we understand how they work, because that's the only way to understand how they fail.
> The holy grail of neural nets has always been to build a simulation of the brain, figure out how it works, and apply that knowledge to how the human brain might work.
I'd say that's a goal for some people -- for those whose goal is to figure out how the brain works, rather than constructing a more ideal and powerful GI. Remember the brain is great at some things, but laughable at others -- such as a "7 +/- 2" items in short term memory, inability to immediately retain rote knowledge after one instance and in great numbers, etc. It's the merging of the fuzzy, goal-directed behavior of the mind, in conjunction with its ability to effect the "real world", and the super-human memory and computational capabilities of computers that makes possible future GAIs that are so powerful and possibly scary.
The holy grail of neural nets has always been to build a simulation of the brain, figure out how it works, and apply that knowledge to how the human brain might work.
We're not there yet but progress has been made. Eventually we'll understand NNs well enough to explain not only themselves but also human brains. In any case we have no choice because we cannot deploy NNs in life critical situations until we understand how they work, because that's the only way to understand how they fail.