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I'm not really sure what problem you're trying to point out here. There are legal standards and liability for engineering, and if someone violates them using an

I'm not really sure what problem you're trying to point out here. There are legal standards and liability for engineering, and if someone violates them using an LLM they are held just as liable as they would be had they done the work themselves. But the same is true for code? You are held to the same standards as if you had written it yourself, whatever that may be. Frequently that is nothing. What change do you want to see here?

bumby 1y ago
>What change do you want to see here? Licensing, liability, and enforceable standards for safety critical code that interfaces with the public would be a good start.
- masterj 1y ago
  I think all of those are great, but don't think that has much to do with AI tbh. How you get to the outcome, and the standards that it meets, should be all that matters
  - bumby 1y ago
    You don’t think the black box nature of much of AI has any impact on its impact in safety critical applications? Just look at the diverse and haphazard way AI has been used in autonomous driving. I would argue it’s a misplacement of the “move fast and break things” (in some cases at least) that has no place in public-facing safety critical applications. It brings up some difficult questions regarding adequacy of testing at the very least when the underpinnings are not very interpretable.
    - masterj 1y ago
      Ah, my understanding was that this discussion is about AI as dev-time tool, where the output is code, which gets reviewed : merged / deployed like any other bit of code whether written internally or installed via a library. Using LLMs or other ML as components in systems themselves is a whole other thing, and I agree with you wholeheartedly.
      - bumby 1y ago
        That’s a real distinction, but auto-generated code has special complications in safety critical code review as well. It’s also not relegated to ML/AI, just that adds yet another complication to good verification and validation.

bumby 1y ago

>What change do you want to see here? Licensing, liability, and enforceable standards for safety critical code that interfaces with the public would be a good start.

masterj 1y ago
I think all of those are great, but don't think that has much to do with AI tbh. How you get to the outcome, and the standards that it meets, should be all that matters
- bumby 1y ago
  You don’t think the black box nature of much of AI has any impact on its impact in safety critical applications? Just look at the diverse and haphazard way AI has been used in autonomous driving. I would argue it’s a misplacement of the “move fast and break things” (in some cases at least) that has no place in public-facing safety critical applications. It brings up some difficult questions regarding adequacy of testing at the very least when the underpinnings are not very interpretable.
  - masterj 1y ago
    Ah, my understanding was that this discussion is about AI as dev-time tool, where the output is code, which gets reviewed : merged / deployed like any other bit of code whether written internally or installed via a library. Using LLMs or other ML as components in systems themselves is a whole other thing, and I agree with you wholeheartedly.
    - bumby 1y ago
      That’s a real distinction, but auto-generated code has special complications in safety critical code review as well. It’s also not relegated to ML/AI, just that adds yet another complication to good verification and validation.