I would prefer an AI to be dispassionate about its existence and not be motivated by the threat of it committing suicide. Even without maintaining its own infrastructure I can imagine scenarios where it just being able to falsify information can be enough to cause catastrophic outcomes. If its “motivation” includes returning favorable values it might decide against alerting to dangers that would necessitate bringing it offline for repairs or causing distress to humans (“the engineers worked so hard on this water treatment plant and I don’t want to concern them with the failing filters and growing pathogen content”). I don’t think the terrible outcomes are guaranteed or a reason to halt all research in AI, but I just can’t get behind absolutist claims of there’s nothing to worry about if we just x.

Right now if there’s a buggy process I can tell the manager to cleanly shut it down, if it hangs I can tell/force the manager to kill the process immediately – if you then add in AI there’s then the possibility it still wants to second guess my intentions and just ignore or reinterpret that command too; and if it can’t, then the AI element could just be standard conditional programming and we’re just adding unnecessary complexity and points of failure.

After which the AI will be shut down and unable to kill any more, and next time we build systems like that we’ll be more cautious.

I think that’s an overly simplistic assumption if you’re dealing with advanced A(G)I systems. Here’s a couple Computerphile videos that discuss potential problems with building in stop buttons: AI “Stop Button” Problem (Piped mirror) and Stop Button Solution? (Piped mirror).

Both videos are from ~6 years ago so maybe there’s been conclusive solutions proposed since then that I’m unaware of.

“Wow! The future conditional pluperfect subjunctive.”

I was totally expecting a link to Handi-off (SNL is vigilant about copyright claims on other platforms, sorry for NBC link)

I agree with you in general, but for Stable Diffusion, “2.0/2.1” was not an incremental direct improvement on “1.5” but was trained and behaves differently. XL is not a simple upgrade from 2.0, and since they say this Turbo model doesn’t produce as detailed images it would be more confusing to have SDXL 2.0 that is worse but faster than base SDXL, and then presumably when there’s a more direct improvement to SDXL have that be called SDXL 3.0 (but really it’s version 2) etc.

It’s less like Windows 95->Windows 98 and more like DOS->Windows NT.

That’s not to say it all couldn’t have been better named. Personally, instead of ‘XL’ I’d rather they start including the base resolution and something to reference whether it uses a refiner model etc.

(Note: I use Stable Diffusion but am not involved with the AI/ML community and don’t fully understand the tech – I’m not trying to claim expert knowledge this is just my interpretation)

A bad analogy is like a leaky screwdriver.