AI Alignment Posts
Recent Discussion
(Cross-posted from my website)
I’ve written a report about whether advanced AIs will fake alignment during training in order to get power later – a behavior I call “scheming” (also sometimes called “deceptive alignment”). The report is available on arXiv here. There’s also an audio version here, and I’ve included the introductory section below (with audio for that section here). This section includes a full summary of the report, which covers most of the main points and technical terminology. I’m hoping that the summary will provide much of the context necessary to understand individual sections of the report on their own.
Abstract
...This report examines whether advanced AIs that perform well in training will be doing so in order to gain power later – a behavior I call “scheming” (also sometimes called
Summary
You can’t optimise an allocation of resources if you don’t know what the current one is. Existing maps of alignment research are mostly too old to guide you and the field has nearly no ratchet, no common knowledge of what everyone is doing and why, what is abandoned and why, what is renamed, what relates to what, what is going on.
This post is mostly just a big index: a link-dump for as many currently active AI safety agendas as we could find. But even a linkdump is plenty subjective. It maps work to conceptual clusters 1-1, aiming to answer questions like “I wonder what happened to the exciting idea I heard about at that one conference” and “I just read a post on a surprising new insight and...
(Agreed except that "inference-time safety techiques" feels overly limiting. It's more like purely behavioral (black-box) safety techniques where we can evaluate training by converting it to validation. Then, we imagine we get the worst model that isn't discriminated by our validation set and other measurements. I hope this isn't too incomprehensible, but don't worry if it is, this point isn't that important.)
1This is Section 2.1 of my report “Scheming AIs: Will AIs fake alignment during training in order to get power?”. There’s also a summary of the full report here (audio here). The summary covers most of the main points and technical terms, and I’m hoping that it will provide much of the context necessary to understand individual sections of the report on their own.
Audio version of this section here, or search "Joe Carlsmith Audio" on your podcast app.
What's required for scheming?
Let's turn, now, to examining the probability that baseline ML methods for training advanced AIs will produce schemers. I'll begin with an examination of the prerequisites for scheming. I'll focus on:
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Situational awareness: that is, the model understands that it's a model in a training process, what the training process will reward,
I agree that AIs only optimizing for good human ratings on the episode (what I call "reward-on-the-episode seekers") have incentives to seize control of the reward process, that this is indeed dangerous, and that in some cases it will incentivize AIs to fake alignment in an effort to seize control of the reward process on the episode (I discuss this in the section on "non-schemers with schemer-like traits"). However, I also think that reward-on-the-episode seekers are also substantially less scary than schemers in my sense, for reasons I discuss here (i.e....
ETA: I'm not saying that MIRI thought AIs wouldn't understand human values. If there's only one thing you take away from this post, please don't take away that. Here is Linch's attempted summary of this post, which I largely agree with.
Recently, many people have talked about whether some of the main MIRI people (Eliezer Yudkowsky, Nate Soares, and Rob Bensinger[1]) should update on whether value alignment is easier than they thought given that GPT-4 seems to follow human directions and act within moral constraints pretty well (here are two specific examples of people talking about this: 1, 2). Because these conversations are often hard to follow without much context, I'll just provide a brief caricature of how I think this argument has gone in the places I've...
(For context: My initial reaction to the post was that this is misrepresenting the MIRI-position-as-I-understood-it. And I am one of the people who strongly endorse the view that "it was never about getting the AI to predict human preferences". So when I later saw Yudkowsky's comment and your reaction, it seemed perhaps useful to share my view.)
It seems like you think that human preferences are only being "predicted" by GPT-4, and not "preferred." If so, why do you think that?
My reaction to this is that: Actually, current LLMs do care about out preferences...
Status: Vague, sorry. The point seems almost tautological to me, and yet also seems like the correct answer to the people going around saying “LLMs turned out to be not very want-y, when are the people who expected 'agents' going to update?”, so, here we are.
Okay, so you know how AI today isn't great at certain... let's say "long-horizon" tasks? Like novel large-scale engineering projects, or writing a long book series with lots of foreshadowing?
(Modulo the fact that it can play chess pretty well, which is longer-horizon than some things; this distinction is quantitative rather than qualitative and it’s being eroded, etc.)
And you know how the AI doesn't seem to have all that much "want"- or "desire"-like behavior?
(Modulo, e.g., the fact that it can play chess pretty...
Differences:
- I don't buy the story about long-horizon competence---I don't think there is a compelling argument, and the underlying intuitions seem like they are faring badly. I'd like to see this view turned into some actual predictions, and if it were I expect I'd disagree.
- Calling it a "contradiction" or "extreme surprise" to have any capability without "wanting" looks really wrong to me.
- Nate writes:
...This observable "it keeps reorienting towards some target no matter what obstacle reality throws in its way" behavior is what I mean when I describe an
(Partly re-hashing my response from twitter.)
I'm seeing your main argument here as a version of what I call, in section 4.4, a "speed argument against schemers" -- e.g., basically, that SGD will punish the extra reasoning that schemers need to perform.
(I’m generally happy to talk about this reasoning as a complexity penalty, and/or about the params it requires, and/or about circuit-depth -- what matters is the overall "preference" that SGD ends up with. And thinking of this consideration as a different kind of counting argument *against* schemers see... (read more)