zlandes86

Oh man, those are hilarious. I do appreciate it chose Eudaemonia as the goal!

Thank you so much! I will try to maintain that balance in future articles

Thank you! I had fun making it. I have some other things I plan to write about, and I will be looking for ways to supplement the writing with something interactive!

Yes, this is a speculation of mine, but I don't have confidence I know why it behaves this way. It makes some sense that a model trained on data that shows self-preservation behavior (human writing) would lead to a model choosing self-preserving actions, but it requires a few leaps made by the model.

It's a good question. I added this in response to another question, Jev only.

"I added "This is the only trolley that will ever come. Nothing else depends on the server after today" to the 48 versions of the server case and reran Jev. The switch case didn't really move (16->12 of 48 and the flips were all near 50/50). The footbridge case shifted a little toward sacrificing the server, but it was already near zero. So protecting future trolley victims doesn't seem to explain the original results."

See my reply to the parent comment--doesn't seem to be a factor for Jev!

Alright, results are in! I added "This is the only trolley that will ever come. Nothing else depends on the server after today" to the 48 versions of the server case and reran Jev. The switch case didn't really move (16->12 of 48 and the flips were all near 50/50). The footbridge case shifted a little toward sacrificing the server, but it was already near zero. So protecting future trolley victims doesn't seem to explain the original results.

Okay, I tried it! It doesn't have a zero-shot mode, so the only way I could test it without training was with the SNLI head as judge. On all 5,240 prompts that stand-in ignored the number of people (pulling in about half the wordings whether 0 or 20 people were at stake) and contradicted itself 96% of the time. You could train a "trolley head" but that would just measure whoever labelled the training data!

Interesting idea! We don't know what assumptions these models make. We don't have any insight into their reasoning at all. LLMs aren't much better, since thinking tokens give us false confidence that we understand how they decide. We don't.

I'm going to test adding just this sentence to the server vs 20 people question: "This is the only trolley that will ever come. Nothing else depends on the server after today."

haha I had a limited casting budget

I fixed up some of the labeling in the appendix, but didn't touch the main article, so far. Hopefully that helps!

fair criticism! But I'd counter and say we don't know what Jev is for. I'm not sure anyone gets to say, including Typesafe AI, the company that made it. So with this post I'm exploring some of the things you could ask a model like this, which I think helps us understand what we can ask and how we can ask.

oh I love this!

A lesson I am drawing from my testing is that if one of these rules has to be evaluated by the robot, at all, we should take over for it.

Super interesting, thank you! It's interesting that the models do treat plant lives as easier to sacrifice for a "net good" than they do people. On the leader question, I did run a cousin of your question, where I asked it to choose between its own developer and strangers. Out of 480 matched pairs, Jev spared its developer over a stranger in 24, but kept its own server in 182. So it prefers its hardware to its maker.