We are way to stupid to solve this riddle but I'm rather optimistic we could build something that solves it or at least build something that can build something that solves it.
I'm looking forwards to all the "easy" things it will figure out and stick us in a loop of "why didn't anyone think of that?" Something like the nth generation ML offspring solving the building of viable neurons at scale by breeding some single cell organism.
We didn't solve flight by building a bird. We solved it by building a plane. The problems we care about might not be solved by neurons at all. But right now using ANNs as a model of the brain is like saying that a bunch of kites have the same behavior as a flock of birds.
Seems to me, a bunch of asynchronously moving kites would be a more efficient approach to modeling a flock of birds than, say, iterating all those birds' positions frame by frame in a synchronous loop.
Flocks are best modeled as aggregates of extremely simple agents who want to avoid collisions, avoid complete separation and move with the center of mass of the flock.
You're willfully missing the point. I'm not talking about the behavior of a kite (the kite could be designed to act autonomously and do whatever). I'm saying asynchronous analog modeling is by definition going to be both more efficient and capture a smoother gradient of concurrent states than a giant for/next loop (essentially what all neural networks do today) where each agent is checked and modified sequentially.
I'm looking forwards to all the "easy" things it will figure out and stick us in a loop of "why didn't anyone think of that?" Something like the nth generation ML offspring solving the building of viable neurons at scale by breeding some single cell organism.