alt Hacker NewsI said "if that's relevant" and you jumped to "this guy just makes websites". I do many things.
The octopus you just described sounds to me like an excellent example of what having the ability to more easily do tedious validation is most useful for. If you know that the "environment keeps on working", there must be some way for you to observe that fact. And if it is an octopus, it is likely difficult and/or to change the conditions and observe the correctness with respect to those changes. I find it so much easier to do this exact kind of thing now. Or, "easier" really isn't the right word. It's that the activation energy is low enough now that I'm able to do a lot of things up front that I used to rely on runtime monitoring to validate.
I guess YMMV, and it's not magic, but for me it totally changes the calculation on when it makes sense to automate something (like that chart from the old xkcd about how many times you'll do the thing and how long it takes to automate) in a way that means I'm doing a bunch of things that are useful for quality that just would never have passed the bar in the past.
> I said "if that's relevant" and you jumped to "this guy just makes websites". I do many things.
I didn't. I made a guess. I might be wrong, that's OK. I love to be wrong, because I learn things by being wrong. Also no offense was intended, and I don't consider webdev inferior anything. What I tried to mean is, if AI has more training data for a domain, it does better. If you fire the same model on a niche domain, it falls flat.
> If you know that the "environment keeps on working", there must be some way for you to observe that fact.
Yes.
> And if it is an octopus, it is likely difficult and/or to change the conditions and observe the correctness with respect to those changes.
Nope. On the contrary, because there's so much innate knowledge that is required to know what to do, simulating in mind, deploying and testing on real world is much easier and faster than letting loose an ML model on it. You need real data, real data comes in slow, but you can catch problems early and easily.
Considering it's a niche area, AI also doesn't have much training on that domain, so it's doubly inapplicable for what we do.
> but for me it totally changes the calculation on when it makes sense to automate something ... (snipped for brevity)
It's great that if it works for you, but YMMV part is way more correct than people want to accept and want to learn. AI is a pneumatic hammer, but not everything is a nail which can be driven in with that.
When it works, it works. When it doesn't, well people still pretend it does or insists it shall. We must accept the limitations.