LessWrong 2.0 Reader

I watched and read a ton of Lab Muffin Beauty Science when I got into skincare. Apart from Sunscreen, I think a lot of it is trial and error with what has good short-term effects. I'm not sure about long-term effects at all tbh. Lab Muffin Beauty Science is helpful for figuring out your skin type, leads for which products to try first, and how to use them. (There's a fair number of products you wanna ramp up slowly and even by the end only use on some days.)

Also related: Yudkowsky on making Solvable Mysteries:

If you have not called upon your readers explicitly to halt and pay attention, they are already reading the next sentence. Even if you do explicitly ask them to pay attention, they are already reading the next sentence. If you have your character think, “Hm… there’s something funny about that story, I should stop and think about that?” guess what your reader does next? That’s right, your reader goes on to read the next sentence immediately, to see what the character thinks about it.

Can you confirm or deny whether you signed any NDA related to you leaving OpenAI? (I will indeed assume a "no comment" or something to that degree implies a "yes" with reasonably high probability. Also, you might be interested in this link about the U.S. labor board deciding that NDA's offered during severance agreements that cover the existence of the NDA itself have been ruled unlawful by the National Labor Relations Board)

Oh yeah, food banks for sure!

No comment.

Thank you for your work there. Curious what specifically prompted you to post this now, presumably you leaving OpenAI and wanting to communicate that somehow?

I think it's good to have more of an excerpt. Would you be up for leaving a comment on my crosspost, and then I will delete the two duplicates? 

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.

Oh no, I did too [LW · GW]

I think this is quite similar to my proposal in Capabilities and alignment of LLM cognitive architectures [LW · GW].

I think people will add cognitive capabilities to LLMs to create fully capable AGIs. One such important capability is executive function. That function is loosely defined in cognitive psychology, but it is crucial for planning among other things.

I do envision such planning looking loosely like a search algorithm, as it does for humans. But it's a loose search algorithm, working in the space of statements made by the LLM about possible future states and action outcomes. So it's more like a tree of thought or graph of thought than any existing search algorithm, because the state space isn't well defined independently of the algorithm.

That all keeps things more dependent on the LLM black box, as in your final possibility.

At least I think that's the analogy between the proposals? I'm not sure.

I think the pushback to both of these is roughly: this is safer how?

I don't think there's any way to strictly formalize not harming humans. My answer is halfway between that and your "sentiment analysis in each step of planning". I think we'll define rules of behavior in natural language, including not harming humans but probably much more elaborate, and implement both internal review [AF · GW], like your sentiment analysis but more elaborate, and external review [LW · GW] by humans aided by tool AI (doing something like sentiment analysis), in a form of scalable oversight.

I'm curious if I'm interpreting your proposal correctly. It's stated very succinctly, so I'm not sure.