There's a perception in the DL field that encoding things into rules is bad, and that symbolic AI as a whole is bad. Probably because of backlash following the failure of symbolic AI. IMO the ideal is somewhere in the middle. There are things you want neural networks for, and there are also things you probably want rules for. The big advantage of a rule-based system is that it's much more predictable and easier to make sense of.
It's going to be very hard to engineer robust automated systems if we have no way to introspect what's going on inside and everything comes down to the neural networks opinion and behavior on a large suite of individual tests.
> The issue today is that all these rules are hard-coded, and the programs need to be rewritten and redeployed every time the laws change.
The programs are probably not being rewritten from scratch. I would argue that: the laws are, or basically should be, unambiguous code, as much as possible. If they can't be effectively translated into code, that signals ambiguity, a potential bug.
It's going to be very hard to engineer robust automated systems if we have no way to introspect what's going on inside and everything comes down to the neural networks opinion and behavior on a large suite of individual tests.
> The issue today is that all these rules are hard-coded, and the programs need to be rewritten and redeployed every time the laws change.
The programs are probably not being rewritten from scratch. I would argue that: the laws are, or basically should be, unambiguous code, as much as possible. If they can't be effectively translated into code, that signals ambiguity, a potential bug.