The fact that NNs can generalize from simulated data to real data is very interesting. We can generate tons of simulated data of stuff like driving in simulator

The fact that NNs can generalize from simulated data to real data is very interesting. We can generate tons of simulated data of stuff like driving in simulators or even video games like GTA. And do experiments that would be way to dangerous or costly to perform in real life. It's not good when the first iteration of a reinforcement learner crashes the car at 60 miles per hour! You can then add tons of randomization to the simulation to make sure it doesn't overfit to the particulars of the simulated data. Like random filters on the input, moving the camera around and vibrating it, making cars and pedestrians behave unrealistically erratically, etc, or having sensors fail. If it can learn to handle these extreme situations in the simulators, hopefully it would be generalize even to rare scenarios that occur in real driving.