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> 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.




Hope you don't need surgery then! Suture training kits like these are quite popular for surgeons to train on. https://a.co/d/3cAotZ0 I don't know about you, but I'm not a rubbery rectangular slab of plastic, so obviously this kit can't help them learn.


This is a reason I opted to have a plastic surgeon come in when I went to the ER with an injury.

I could've had the nurse close me up and leave me with a scar, which she admitted would happen with her practice, or I could have someone with extensive experience treating wounds so that they'd heal in cosmetically appealing way do it. I opted for the latter.


The difference being that you have to do a little more than that to become a board-certified surgeon. If a VC gives you a billion dollars to buy and practice on every available surgery practice kit in the world, you will still fail to become a surgeon. And we enforce such standards because if we don't then people die needlessly.


How a model learns doesn’t really matter. What works works.

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.


What about a doctor who used a mix of training both on live patients as well as cadavers and models?


Is this doctor able to learn new information and work through novel problems on the fly, or will their actions always be based on the studying they did in the past on old information?

Similarly, when this doctor sees something new, will they just write it off as something they've seen before and confidently work from that assumption?




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