Yes but in a computer every processing unit can be used for widely different tasks. In contrast, the neurons in a brain are used for the same task over and over again.
I don't think these parallels between the computer and the brain are valid though. The brain is a highly parallel but slow machine, we have very fast and not-so-parallel computers (just a few cores). I think a lot of what the brain does can be expressed on computers without changing the architecture much, i.e. you don't need to compare the architectures, just the outcomes of computation.
Don't forget humans are one of the most expensive resources and nothing can really replace them for even a vast majority of non-physical jobs. So if we could build computing devices that potentially solve some of these problems, needless to say, the productivity boosts would be plentiful.
Secondly, we aren't even close to being able to model anything relatively close to the computational capabilities of the human brain because we don't even understand the human brain. So your comments on highly parallel but slow and fast but no so parallel don't make a lot of sense.
For example take MapBox's new vision SDK. It's able to perform semi-decent feature extraction on the road while people drive via a camera. Well guess what I would absolute stomp the vision SDK on accuracy for every feature it thinks it identified, not only that, I am capable of identifying an order of magnitude more features than it can, not only that, but I'm able to identify new features on the fly and even guess with greater accuracy what they are.
So yeah, there are a plethora of functional yields that we have yet to achieve with some of the most powerful computers in the world that are achieved by the human brain every day. Which could be indicative of maybe both a resource, but also an architecture problem.
I suspect our recognition abilities may in fact be worse than that of a good SDK but we have the advantage of a general real world experience. Bit of a simplified example, in order to recognize a dog you should have seen a lot of various animals, have a basic knowledge of anatomy, animal behaviour etc. A lot of the times recognizing the context helps too: e.g. something on a lead ahead of a walking human in the street, likely a dog - you need only a very quick confirmation to say it's definitely a dog, etc.
I also think the brain does a lot of tree searches with optimizations (which are never perfect), and it's where the brain's parallel architecture proves to be beneficial.
Intuitively though, modern computers are still not powerful enough to perform the same tasks albeit mostly sequentially. I believe we'll get there and I think AGI in a simplified virtual/gaming environment is the best place to test our approaches.
How does this square with the fact that people can get large portions of their brain scooped out and still retain many of their previous abilities?
This is a naive assumption, but if there were purely a 1 to 1 correspondence between a neuron and any given task, I’d assume you wouldn’t be able to recover from massive brain damage. But it seems like people can retrain the functional parts of their brain after damage to other parts to pick up the slack somehow.