Defense driving is all about making these sort of judgments --- and choosing the least bad option when there are no good ones.

It could be argued that there are already ML systems that do what you describe (i.e. make instant effective judgements/decisions about something it hasn't been exposed to during training), at least for some contexts. For more information: https://en.wikipedia.org/wiki/Zero-shot_learning

Current methods are far from perfect, but not for the reasons Marcus believes. Consider that we /should/ be able to derive hypotheses for better system behavior from a given critique, test those proposed system improvements, and then (if the hypothesis is correct) see some improvement. Marcus' crew hasn't produced any useful improvements; this is either because they are not technically competent or their hypotheses are wrong. Given that people have been hammering at these systems for ~10 years now, I doubt the failure to build better logic-based systems (or hybrid systems) is for lack of trying.

Meanwhile, the non-Marcus groups have been improving systems by leaps and bounds, through exactly the kind of iterative, hypothesis-driven improvement I described above. Certainly nothing is perfect, but we can see real progress happening.

(Finally; Tesla FSD is a strawman. It's Waymo you need to argue with, and they're doing great, AFAICT.)

Let's disperse a few thousand of them at random locations across the country for everyday use and see what happens.

AFAICT, no one has actually done this yet.

There's a more interesting conversation to be had here than the one you're offering, in any case. And you haven't addressed the total failure of the Marcus crew to actually produce working solutions...