Earlier quoted context omitted.
Would you trust a ML self-driving algorithm trained on a "digital twin" of a city? I would. I view synthetic training data like a digital twin in which it can provider further control or specified noise to understand from.
> Would you trust a ML self-driving algorithm trained on a "digital twin" of a city? I would. No, just as I wouldn't trust a surgeon who studied medicine by playing Operation. A gross approximation is not a substitute for real life.
How it is tested and validated is what matters.
There are lots of ways to train on synthetic data, and synthetic data can have advantages as well as disadvantages over natural data.
Creative use of synthetic data is going to lead to many cases where we find it is good enough. Or even better than natural data.