One thing that stood out to me is that they train it entirely on simulated data and deploy it without needing any fine-tuning on real-world data.
I took one of Andrew Ng's deep learning courses a few years ago and remember him saying that this approach typically doesn't work. It sounds like that's no longer the case which is really cool because it's relatively cheap/easy to generate simulated data.
Is this a more common approach these days? Are there any other interesting examples to point to?
I took one of Andrew Ng's deep learning courses a few years ago and remember him saying that this approach typically doesn't work. It sounds like that's no longer the case which is really cool because it's relatively cheap/easy to generate simulated data.
Is this a more common approach these days? Are there any other interesting examples to point to?