Honest newbie question: Why would we even want to do that? Exclusively for academic medical purposes? (ie learning how our body works)
It seems to me, that since evolution is highly imperfect, that trying to mimic living beings, might not be the best idea. While we do mimic them a lot, mostly it seems to be for learning until we can do better. But in this case. Since it's so expensive and not viable to mimic them properly. Shouldn't we just try to come up with our own algorithms instead of trying to copy nature's historically bloated algorithms?
That seems to be a lesson we learned from machine learning. I remember when neural networks were first being talked about seriously a decades or so ago. The goal seemed to be to try to mimic neurons just for mimicing neurons sake. Many critics would say we should probably try other learning algorithms that were obviously more efficient. And nowadays we barely see actual neural networks being used seriously in commercial production simply because we have better algorithms that are not trying to emulate nature, just because.
Wouldn't trying to emulate a virtual insect be repeating the same mistake? If all we want is to design a virtual robot that can look for food and control its wings. I sure as hell don't need thousand something emulated neurons to build that.
>Shouldn't we just try to come up with our own algorithms instead of trying to copy nature's historically bloated algorithms?
People are working on this problem from both sides. AI on one and biological simulation on the other. We don't know which will realize its goals first.
You don't do things in science because they are practical (you just write that on the grant applications), in fact you do many things largely because they are impractical.
It's about wonder and lust for knowledge and all that. It just so happens that the theoretical ground work for all technology piggy backed on this motivation.
It seems to me, that since evolution is highly imperfect, that trying to mimic living beings, might not be the best idea. While we do mimic them a lot, mostly it seems to be for learning until we can do better. But in this case. Since it's so expensive and not viable to mimic them properly. Shouldn't we just try to come up with our own algorithms instead of trying to copy nature's historically bloated algorithms?
That seems to be a lesson we learned from machine learning. I remember when neural networks were first being talked about seriously a decades or so ago. The goal seemed to be to try to mimic neurons just for mimicing neurons sake. Many critics would say we should probably try other learning algorithms that were obviously more efficient. And nowadays we barely see actual neural networks being used seriously in commercial production simply because we have better algorithms that are not trying to emulate nature, just because.
Wouldn't trying to emulate a virtual insect be repeating the same mistake? If all we want is to design a virtual robot that can look for food and control its wings. I sure as hell don't need thousand something emulated neurons to build that.