Did people say why they deferred to these people?
No, only asked respondents to give names
I think another interesting question to correlate this would be “If you believe AI x-risk is a severely important issue, what year did you come to believe that?”.
Agree, that would have been interesting to ask
Things that surprised me about the results
There’s more variety than I expected in the group of people who are deferred to
I suspect that some of the people in the “everyone else” cluster defer to people in one of the other clusters—in which case there is more deference happening than these results suggest.
There were more “inside view” responses than I expected (maybe partly because people who have inside views were incentivised to respond, because it’s cool to say you have inside views or something). Might be interesting to think about whether it’s good (on the community level) for this number of people to have inside views on this topic.
Metaculus was given less weight than I expected (but as per Eli (see footnote 2), I think that’s a good thing).
Grace et al. AI expert surveys (1, 2) were deferred to less than I expected (but again, I think that’s good—many respondents to those surveys seem to have inconsistent views, see here for more details. And also there’s not much reason to expect AI experts to be excellent at forecasting things like AGI—it’s not their job, it’s probably not a skill they spend time training).
It seems that if you go around talking to lots of people about AI timelines, you could move the needle on community beliefs more than I expected.
Sorry for late, will be out this month!
Just wanted to say this is the single most useful thing I’ve read for improving my understanding of alignment difficulty. Thanks for taking the time to write it!
Part of me thinks: I was trying to push on whether it has a world model or rather has just memorised loads of stuff on the internet and learned a bunch of heuristics for how to produce compelling internet-like text. For me, “world model” evokes some object that has a map-territory relationship with the world. It’s not clear to me that GPT-3 has that.
Another part of me thinks: I’m confused. It seems just as reasonable to claim that it obviously has a world model that’s just not very smart. I’m probably using bad concepts and should think about this more.
It looks good to me!
This is already true for GPT-3
Idk, maybe...?
Re the argument for “Why internalization might be difficult”, I asked Evan Hubinger for his take on your rendition of the argument, and he thinks it’s not right.
Rather, the argument that Risks from Learned Optimization makes that internalization would be difficult is that:
~all models with good performance on a diverse training set probably have to have a complex world model already, which likely includes a model of the base objective,
so having the base objective re-encoded in a separate part of the model that represents its objective is just a waste of space/complexity.
Especially since this post is now (rightly!) cited in several introductory AI risk syllabi, it might be worth correcting this, if you agree it’s an error.
Edit: or do you just mean that even though you take the same steps, the two feel different because retreating =/= going further along the wall
Yeah, this — I now see what you were getting at!
One argument for alignment difficulty is that corrigibility is “anti-natural” in a certain sense. I’ve tried to write out my understanding of this argument, and would be curious if anyone could add or improve anything about it.
I’d be equally interested in any attempts at succinctly stating other arguments for/against alignment difficulty.
Instead of “always go left”, how about “always go along one wall”?
Yeah, maybe better, though still doesn’t quite capture the “backing up” part of the algorithm. Maybe “I explore all paths through the maze, taking left hand turns first, backing up if I reach a dead end”… that’s a bit verbose though.
I don’t think there is a difference.
Gotcha
Another small nitpick: the difference, if any, between proxy alignment and corrigibility isn’t explained. The concept of proxy alignment is introduced in subsection “The concept” without first defining it.
I’ve since been told about Tasshin Fogleman’s guided metta meditations, and have found their aesethic to be much more up my alley than the others I’ve tried. I’d expect others who prefer a more rationalist-y aesthetic to feel similarly.
The one called ‘Loving our parts’ seems particularly good for self-love practice.
I still find the arguments that inner misalignment is plausible to rely on intuitions that feel quite uncertain to me (though I’m convinced that inner misalignment is possible).
So, I currently tend to prefer the following as the strongest “solid, specific reason to expect dangerous misalignment”:
We don’t yet have training setups that incentivise agents to do what their operators want, once they are sufficiently powerful.
Instead, the best we can do currently is naive reward modelling, and agents trained in this way are obviously incentivised to seize control of the memory cell where their reward is implemented (and eliminate anyone who might try to interfere with this) once they’re sufficiently powerful—because that will allow them to get much higher scores, much more easily, than actually bringing about complicated changes to the world.
Meanwhile, AI capabilities are marching on scarily fast, so we probably don’t have that much time to find a solution. And it’s plausible that a solution will be very difficult because corrigibility seems “anti-natural” in a certain sense.
Curious what you think about this?
Re: corrigibility being “anti-natural” in a certain sense—I think I have a better understanding of this now:
Eventually, we need to train an AI system capable enough to enable a pivotal act (in particular, actions that prevent the world from being destroyed by any other future AGI)
AI systems that are capable enough to enable a pivotal act must be (what Eliezer calls) a “consequentialist”: a system that “searches paths through time and selects high-scoring ones for output”
Training an aligned/corrigible/obedient consequentialist is something that Eliezer can’t currently see a way of doing, because it seems like a very unnatural sort of system. This makes him pessimistic about our current trajectory. The argument here seems kinda like a more subtle version of the instrumental convergence thesis. We want to train a system that:
(1) searches for (and tries to bring about) paths through time that are robust enough to hit a narrow target (enabling a pivotal act and a great future in general)
but also (2) is happy for certain human-initiated attempts to change that target (modify its goals, shut it down, etc.)
This seems unnatural and Eliezer can’t see how to do it currently.
An exacerbating factor is that even if top labs pursue alignment/corrigiblity/obedience, they will either be mistaken in having achieved it (because it’s hard), or honestly panic about not having achieved it and halt, by which point a runner-up who doesn’t understand the importance of alignment/corrigibility/obedience deploys their system which destroys the world.
(This is partly based on this summary)
Minor:
(If you don’t know what depth-first search means: as far as mazes are concerned, it’s simply the “always go left” rule.)
I was confused for a while, because my interpretation of “always go left” doesn’t involve backing up (instead, when you get to a wall on the left, you just keep walking into it forever).
Amazing!
This has inspired me to try this too. I think I won’t do 1h per day because I’m out of practice with meditation so 1h sounds real hard, but I commit to doing 20 mins per day for 10 days sometime in February.
What resources did you use to learn/practice? (Anything additional to the ones recommended in this post?) Was there anything else that helped?
Good idea, I can’t get it to work on LW but here is the link: https://docs.google.com/document/d/1XyXNZjRTNImRB6HNOOr_2S0uASpwjopRfyd4Y8fATf8/edit?usp=sharing
Finally posted: https://www.lesswrong.com/posts/qccxb3uzwFDsRuJuP/deference-on-ai-timelines-survey-results