this post was submitted on 24 Jul 2025
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End-to-end ML can be much better than hybrid (or fully rules-based) systems. But there's no guarantee and you have to actually measure the difference to be sure.
For safety-critical systems, I would also not want to commit fully to an e2e system because the worse explainability means it's much harder to be confident that there is no strange failure mode that you haven't spotted but may be, or may become, unacceptable common. In that case, you would want to be able to revert to a rules-based fallaback that may once have looked worse-performing but which has turned out to be better. That means that you can't just delete and stop maintaining that rules-based code if you have any type of long-term thinking. Hmm.
I've thought about it in the past... what if there was a bug in an update and under some specific conditions the car will just vere to the side and crash. There's a possibility that every self-driving Tesla travelling west into a sunset suddenly slams on the brakes causing a pile up. Who knows what kind of edge cases could exist?
Even worse, what if someone hacks the wireless update and does something like this intentionally?
yeah i wanna see what the fuck metrics made them think this was a good idea. what is their mean average precision. did they recall@1 for humans on the road