From a grad student's point of view who currently uses "deep learning" to solve problems, we're nowhere near that kind of general intelligence. Neural networks

From a grad student's point of view who currently uses "deep learning" to solve problems, we're nowhere near that kind of general intelligence. Neural networks are very good at approximating various kinds of functions given lots of redundant training data, but they have never shown the flexibility of actual human brains (as in creating new neural connections with only sparse experiences, along with deductive/symbolic reasoning). To really mimic human brains, we first need to know quantitatively how our neurons communicate with each other and form connections in our brains (more exactly, the differential equations in which voltage levels change among neurons, as well as the general rule of how new connections are created/destroyed.) Even after figuring out the bio-mechanics, it's going to be a hard task to implement/approximate this in silicon, and would probably need huge breakthroughs in material science and computer architecture. Copying state from a human brain to silicon would be as much as hard, since that will need incredibly high-definition non-destructive 3d scanning technology. I'll bet we're at least 100+ years for humanity to even try attempting the things you're talking about. We also have to consider that in the next few decades, the world in general will be far more busy trying to deal with the drawbacks of globalized capitalism (as well as dealing with climate change).