No comment.

I worked at OpenAI for three years, from 2021-2024 on the Alignment team, which eventually became the Superalignment team. I worked on scalable oversight, part of the team developing critiques as a technique for using language models to spot mistakes in other language models. I then worked to refine an idea from Nick Cammarata into a method for using language model to generate explanations for features in language models. I was then promoted to managing a team of 4 people which worked on trying to understand language model features in context, leading to the release of an open source "transformer debugger" tool.
I resigned from OpenAI on February 15, 2024.

Re: hidden messages in neuron explanations, yes it seems like a possible problem. A way to try to avoid this is to train the simulator model to imitate what a human would say given the explanation. A human would ignore the coded message, and so the trained simulator model should also ignore the coded message. (this maybe doesn't account for adversarial attacks on the trained simulator model, so might need ordinary adversarial robustness methods).

Does seem like if you ever catch your interpretability assistant trying to hide messages, you should stop and try to figure out what is going on, and that might be sufficient evidence of deception.

From discussion with Logan Riggs (Eleuther) who worked on the tuned lens: the tuned lens suggests that the residual stream at different layers go through some linear transformations and so aren’t directly comparable. This would interfere with a couple of methods for trying to understand neurons based on weights: 1) the embedding space view 2) calculating virtual weights between neurons in different layers.

However, we could try correcting these using the transformations learned by the tuned lens to translate between the residual stream at different layers, and maybe this would make these methods more effective. By default I think the tuned lens learns only the transformation needed to predict the output token but the method could be adapted to retrodict the input token from each layer as well, we’d need both. Code for tuned lens is at https://github.com/alignmentresearch/tuned-lens

So I do think you can get feedback on the related question of "can you write a critique of this action that makes us think we wouldn't be happy with the outcomes" as you can give a reward of 1 if you're unhappy with the outcomes after seeing the critique, 0 otherwise.

And this alone isn't sufficient, e.g. maybe then the AI system says things about good actions that make us think we wouldn't be happy with the outcome, which is then where you'd need to get into recursive evaluation or debate or something. But this feels like "hard but potentially tractable problem" and not "100% doomed". Or at least the failure story needs to involve more steps like "sure critiques will tell us that the fusion power generator will lead to everyone dying, but we ignore that because it can write a critique of any action that makes us believe it's bad" or "the consequences are so complicated the system can't explain them to us in the critique and get high reward for it"

ETA: So I'm assuming the story for feedback on reliably doing things in the world you're referring to is something like "we give the AI feedback by letting it build fusion generators and then giving it a score based on how much power it generates" or something like that, and I agree this is easier than "are we actually happy with the outcome"

If we can't get the AI to answer something like "If we take the action you just proposed, will we be happy with the outcomes?", why can we get it to also answer the question of "how do you design a fusion power generator?" to get a fusion power generator that does anything reliably in the world (including having consequences that kill us), rather than just getting out something that looks to us like a plan for a fusion generator but doesn't actually work?

Image link is broken

Yep, that clarifies.