All of the gears to ascension's Comments + Replies

well, the fact that I don't have an answer ready is itself a significant component of an answer to my question, isn't it?

A friend on an alignment chat said something to the effect of:

i think they are just sorely underestimating again and again the difference between a cute gang of sincere EA red teamers and the internet. the internet is where [...] lives for gods sake.

And so I figured I'd come here and ask about it. This eval seems super shallow, only checking if the model is, on its own, trying to destroy the world. Seems rather shallow and uncreative - it barely touched on any of the jailbreaks or ways to pressure or trick the model into misbehaving.

3Daniel Kokotajlo6mo
It is well understood within OpenAI that 'the internet' (tens of millions of people interacting with the models) is a more powerful red-teamer than anything we can do in-house. What's your point?
What improvements do you suggest?

I do think there's real risk there even with base models, but it's important to be clear where it's coming from - simulators can be addictive when trying to escape the real world. Your agency needs to somehow aim away from the simulator, and use the simulator as an instrumental tool.

I think you just have to select for / rely on people who care more about solving alignment than escapism, or at least that are able to aim at alignment in conjunction with having fun. I think fun can be instrumental. As I wrote in my testimony, I often explored the frontier of my thinking in the context of stories. My intuition is that most people who go into cyborgism with the intent of making progress on alignment will not make themselves useless by wireheading, in part because the experience is not only fun, it's very disturbing, and reminds you constantly why solving alignment is a real and pressing concern.

my impression is that by simulator and simulacra this post is not intending to claim that the thing it is simulating is realphysics but rather that it learns a general "textphysics engine", the model, which runs textphysics environments. it's essentially just a reframing of the prediction objective to describe deployment time - not a claim that the model actually learns a strong causal simplification of the full variety of real physics.

That's correct. Even if it did learn microscopic physics, the knowledge wouldn't be of use for most text predictions because the input doesn't specify/determine microscopic state information. It is forced by the partially observed state to simulate at a higher level of abstraction than microphysics -- it must treat the input as probabilistic evidence for unobserved variables that affect time evolution. See this comment for slightly more elaboration.