[fxtentacle]

My prediction is that dropping the real-world interactions will severely slow down their progress in other areas. But then again, I'm super biased because my current work is to make AI training easier by building specialized hardware.

Reinforcement learning can work quite well if you produce the hardware, so that your simulation model perfectly matches the real-world deployment system. On the other hand, training purely on virtual data has never really worked for us because the real world is always messier/dirtier than even your most realistic CGI simulations. And nobody wants an AI that cannot deal with everyday stuff like fog, water, shiny floors, rain, and dust.

In my opinion, most recent AI breakthroughs have come from restating the problem in a way that you can brute-force it with ever-increasing compute power and ever-larger data sets. "end to end trainable" is the magic keyword here. That means the keys to the future are in better data set creation. And the cheapest way to collect lots of data about how the world works is to send a robot and let it play, just like how kids learn.