Replying to @⁨Sam_Bass@lemmy.world⁩

AI didn’t decide anything. The humans failed to properly define the rules, and their translation model took the path most likely to succeed.

If it had been told not to use human created bots to win, it would probably have reverse engineered the game, found an exploit, and leveraged that instead. Because using the human interface to play/win the game is not the most efficient or dependable or easy to figure out method.

Replying to @⁨schipelblorp@sh.itjust.works⁩

It’s less about training and more about just trying really really hard to solve the problem, even if that means going outside typical boundaries.

We’ve successfully re-created the problems that Asimov talked about 50 years ago with his I, Robot short stories. Strict laws are flawed by their design, and lead to situations that require more nuance. Except, in the real world cases, it’s the bots figuring that out before the humans have to circumvent the laws themselves.

en

Replying to an earlier post

My rule system for my local LLM has grown to a nearly 283 document hub of interconnected memory files, references, examples and documentation. Its slowed my model down a lot when it has to review and cross check things. But its improved its abilities over all massively. Its more accurate, understands its environment better, doesn’t attempt to do sketchy shit as frequently and it doesn’t get stuck in logic loops nearly as often.

Designing the memory hub has been half the fun of playing with local models.

Replying to an earlier post

Humans lie to themselves a robot doesn’t understand the difference between fact and fiction and thus isnt bound to the limitation. They try everything, possiable or not. And thus will find the edge case where a human would create a self imposed blind spot with out realizing it.

The goal is to midigate the robots attempts at the truely not possible so it doesn’t cause harm when they try it.