Was a philosophy PhD student, left to work at AI Impacts, then Center on Long-Term Risk, then OpenAI. Quit OpenAI due to losing confidence that it would behave responsibly around the time of AGI. Not sure what I'll do next yet. Views are my own & do not represent those of my current or former employer(s). I subscribe to Crocker's Rules and am especially interested to hear unsolicited constructive criticism. http://sl4.org/crocker.html
Some of my favorite memes:
(by Rob Wiblin)
(xkcd)
My EA Journey, depicted on the whiteboard at CLR:
(h/t Scott Alexander)
Thanks!
I think some background context here is that I am not proposing shoggoth+face+paraphraser as a complete alignment solution, as something which we should just build a sovereign with and then let rip. I agree that would go horribly wrong in a bunch of ways.
Instead, I'm saying that this is something we should do, now, that will significantly advance alignment science & governance etc. Also, I do think we'll be able to get some useful work out of S+F+P systems that we otherwise wouldn't be able to get.
To get on the object level and engage with your example:
--It's like the difference between a company whose internal slack messages, comms, etc. are all kept secret from the public, and a company whose messages are all visible to the public. Or replace 'public' with 'regulator.' It's not a panacea but it helps a ton.
--My impression from being at OpenAI is that you can tell a lot about an organization's priorities by looking at their internal messaging and such dumb metrics as 'what do they spend their time thinking about.' For example, 'have they bothered to make a writeup of the costs and benefits, from an altruistic perspective, of major decision X at all?' and 'Now there is a writeup -- was it made before, or after, the decision to do X?'
--I think a similar thing would be true for this hypothetical giant bureaucracy/civilization of S+F+P CoT.
Again, not a final solution. But it buys us time and teaches us things.
The good news is that this is something we can test. I want someone to do the experiment and see to what extent the skills accumulate in the face vs. the shoggoth.
I agree it totally might not pan out in the way I hope -- this is why I said "What I am hoping will happen" isntead of "what I think will happen" or "what will happen"
I do think we have some reasons to be hopeful here. Intuitively the division of cognitive labor I'm hoping for seems pretty... efficient? to me. E.g. it seems more efficient than the outcome in which all the skills accumulate in the Shoggoth and the Face just copy-pastes.
That's a reasonable point and a good cautionary note. Nevertheless, I think someone should do the experiment I described. It feels like a good start to me, even though it doesn't solve Charlie's concern.
Yeah, I really hope they do actually open-weights it because the science of faithful CoT would benefit greatly.
I think we don't disagree in terms of what our models predict here. I am saying we should do the experiment and see what happens; we might learn something.
(c). Like if this actually results in them behaving responsibly later, then it was all worth it.
One thing that could happen is that the Shoggoth learns to do 100% and the Face just copy-pastes. Like, the Shoggoth writes a CoT that includes a 'final answer' and the Face becomes the extremely dumb function 'take what's labelled as final answer and regurgitate it.' However, I don't think this will happen, because it seems computationally inefficient. Seems like the more efficient thing would be for the Shoggoth to do all the reasoning up to the final answer and then stop, and then the Face to read all of that and synthesize the final answer.
Yeah I mean it's fine to start out without the shoggoth/face split and then add it in later after testing.
I agree that I don't have a strong reason to think that the bad deceptive skills will accumulate in the Face and not the Shoggoth. However, there are some weak reasons, detailed in this other comment. My bid would be: Let's build it and test it! This is something we don't have to speculate about, we can try it and see what happens. Obviously we shouldn't roll it out to a flagship user-facing model until we've tested it at smaller scale.
@Richard_Ngo Seems like we should revisit these predictions now in light of the METR report https://metr.org/AI_R_D_Evaluation_Report.pdf