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No, it is a financially different problem for the exact same reason that hardware is different than software. Software has infinite do-overs at malleable speed. Hardware has to work in reality. Sure, after enough refining, ML will be able to manufacture a complete car. But how many attempts would it have to undergo first? Even ignoring the iterations on the manufacturing hardware itself, how much money would you have to spend on materials and energy in your tens of thousands of attempts to teach the ML how to manufacture a car? And then there is the political cost. What defect rate will people be willing to put up with from entirely autonomous robotic manufacturing? Almost certainly, it will be a lower rate than what we put up with from humans. Especially if it is from a black box like current ML.
It depends on how you’re doing the iteration. It seems perfectly plausible to do 99% of the ML in a good physics engine (where doing as many iterations as you want are essentially free) and only switching to the real world once you have a system that is pretty good at making cars.
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No. High-fidelity simulations in MuJoCo and such suffice for the most part, and other kinks will be ironed out with learning on fleet data.
There is no need to solve end-to-end manufacturing first, we already have hardware overhang with robots, they will walk and indeed run soon after ML grants them decent cerebellums.
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