The problem with that plan is that there are too many valid moral realities, so which one you do get is once again a consequence of alignment efforts.
To be clear, I’m not stating that it’s hard to get the AI to value what we value, but it’s not so brain-dead easy that we can make the AI find moral reality and then all will be well.
Not always, but I’d say often.
I’d also say that at least some of the justification for changing values in philosophers/humans is because they believe the new values are closer to the moral reality/truth, which is an instrumental incentive.
To be clear, I’m not going to state confidently that this will happen (maybe something like instruction following ala @Seth Herd is used instead, such that the pointer is to the human giving the instructions, rather than having values instead), but this is at least reasonably plausible IMO.
Yes, I admittedly want to point to something along the lines of preserving your current values being a plausibly major drive of AIs.
In this case, it would mean the convergence to preserve your current values.
The answer to this is that we’d rely on instrumental convergence to help us out, combined with adding more data/creating error-correcting mechanisms to prevent value drift from being a problem.
Oh, I was responding to something different, my apologies.
Neither of those claims has anything to do with humans being the “winners” of evolution. I don’t think there’s any real alignment-related claim that does. Although, people say all kinds of things, I suppose. So anyway, if there’s really something substantive that this post is responding to, I suggest you try to dig it out.
The evolution analogy of how evolution failed to birth intelligent minds that valued what evolution valued is an intuition pump that does get used in explaining outer/inner alignment failures, and is part of why in some corners there’s a general backdrop of outer/ inner alignment being so hard.
It’s also used in the sharp left turn, where the capabilities of an optimization process like humans outstripped their alignment to evolutionary objectives, and the worry is that an AI could do the same to us, and evolutionary analogies do get used here.
Both Eliezer Yudkowsky and Nate Soares use arguments that rely on evolution failing to get a selection target inside us, thus misaligning us to evolution:
This might actually be a useful idea, thanks for your idea.
I definitely agree that conditioning on AI catastrophe, I think the 4 step chaotic catastrophe is the most likely way an AI catastrophe leads to us being extinct or at least in a very bad position.
I admit the big difference is that I do think that 2 is probably incorrect, as we have some useful knowledge of how models form goals, and I expect this to continue.
That’s actually a pretty good argument, and I actually basically agree that hiding CoT from the users is a bad choice from an alignment perspective now.
The first concern is absolutely critical, and one way to break the circularity issue is to rely on AI control, while another way is to place incentives that favor alignment as an equilibrium and make dishonesty/misalignment unfavorable, in the sense that you can’t have a continuously rewarding path to misalignment.
The second issue is less critical, assuming that AGI #21 hasn’t itself become deceptively aligned, because at that point, we can throw away #22 and restart from a fresh training run.
If that’s no longer an option, we can go to war against the misaligned AGI with our own AGI forces.
In particular, you can still do a whole lot of automated research once you break labor bottlenecks, and while this is a slowdown, this isn’t fatal, so we can work around it.
The third issue is if we have achieved aligned ASI, than we have at that point achieved our goal, and once humans are obsolete in making alignment advances, that’s when we can say the end goal has been achieved.
Some thoughts on this post:
Hiding the CoT from users hides it from the people who most need to know about the deceptive cognition.
I agree in not thinking hiding the CoT is good for alignment.
They already show some willingness to dismiss evidence of deceptive cognition which they gain this way, in the o1 report. This calls into question the canary-in-coalmine benefit.
I definitely agree that OpenAI would dismiss good evidence of deceptive cognition, though I personally don’t find the o1 report damning, because I find the explanation that it confabulates links in the CoT because there is a difference between it’s capability to retrieve links and it’s ability to know links to be pretty convincing (combined with links being a case where perfect linking is far more useful than approximately linking.)
See this post for why even extreme evidence may not get them to undeploy:
At this point, the system becomes quite difficult to “deeply” correct. Deceptive behavior is hard to remove once it creeps in. Attempting to train against deceptive behavior instead steers the deception to be better. I would expect alignment training to similarly fail, so that you get a clever schemer by default.
While I do think aligning a deceptively aligned model is far harder due to adversarial dynamics, I want to note that the paper is not very much evidence for it, so you should still mostly rely on priors/other evidence:
https://www.lesswrong.com/posts/YsFZF3K9tuzbfrLxo/#tchmrbND2cNYui6aM
I definitely agree with this claim in general:
So, as a consequence of this line of thinking, it seems like an important long-term strategy with LLMs (and other AI technologies) is to keep as far away from deceptive behaviors as you can. You want to minimize deceptive behaviors (and its precursor capabilities) throughout all of training, if you can, because it is difficult to get out once it creeps in. You want to try to create and maintain a truth-telling equilibrium, where small moves towards deception are too clumsy to be rewarded.
(Edited out the paragraph that users need to know, since Daniel Kokotajlo convinced me that hiding the CoT is bad, actually.)
I am not exactly sure I understand your proposal. Are you proposing that we radically gut our leading future model by restricting it severely? I don’t think any AI labs will agree to do so, because such future AI is much less useful than probably even current AIs.
Or are you proposing that we use AI monitors our leading future AI models and then we heavily restrict only the monitors?
My proposal is to restrain the AI monitor’s domain only.
I agree this is a reduction in capability from unconstrained AI, but at least in the internal use setting rather than deploying the AI, you probably don’t need, and maybe don’t want it to be able to write fictional stories or telling calming stories, but rather using the AI for specific employment tasks.
Re jailbreaks, I think this is not an example of alignment not being solved, but rather an example of how easy it is to misuse/control LLMs.
Also, a lot of the jailbreak successes rely on the fact that it’s been trained to accept a very wide range of requests for deployment reasons, which suggests narrowing the domain of acceptable questions for internal use could reduce the space of jailbreaks dramatically:
Current jailbreaks of chatbots often work by exploiting the fact that chatbots are trained to indulge a bewilderingly broad variety of requests—interpreting programs, telling you a calming story in character as your grandma, writing fiction, you name it. But a model that’s just monitoring for suspicious behavior can be trained to be much less cooperative with the user—no roleplaying, just analyzing code according to a preset set of questions. This might substantially reduce the attack surface.
To answer this specific question
Do you mean we’re waiting tiil 2026⁄27 for results of thee next scaleup? If this round (GPT5, Claude 4, Gemini 2.0) show diminishing returns, wouldn’t we expect that the next will too?
Yes, assuming Claude 4/Gemini 2.0/GPT-5 don’t release or are disappointing in 2025-2026, this is definitely evidence that things are slowing down.
It doesn’t conclusively disprove it, but it does make progress shakier.
Agree with the rest of the comment.
I think where I get off the train personally probably comes down to the instrumental goals leading to misaligned goals section, combined with me being more skeptical of instrumental goals leading to unbounded power-seeking.
I agree there are definitely zero-sum parts of the science loop, but my worldview is that the parts where the goals are zero sum/competitive receive less weight than the alignment attempts.
I’d say the biggest area of how I’m skeptical so far is that I think there’s a real difference between the useful idea that power is useful for the science loop and the idea that the AI will seize power by any means necessary to advance it’s goals.
I think instrumental convergence will look more like local power-seeking that is more related to the task at hand, and not to serve some of it’s other goals, primarily due to denser feedback constraining the solution space and instrumental convergence more than humans.
That said, this is a very good post, and I’m certainly happier that this much more rigorous post was written than a lot of other takes on scheming.
I’d argue one of the issues with a lot of early social media moderation policies was treating ironic beliefs that were usually banned as not ban-worthy, because as it turned out, ironic belief in some extremism turned out to either have been fake, or turned into the real versions over time.
To be clear, I don’t yet believe that the rumors are true, or that if they are, that they matter.
We will have to wait until 2026-2027 to get real evidence on large training run progress.
One potential answer to how we might break the circularity is the AI control agenda that works in a specific useful capability range, but fail if we assume arbitrarily/infinitely capable AIs.
This might already be enough to do so given somewhat favorable assumptions.
But there is a point here in that absent AI control strategies, we do need a baseline of alignment in general.
Thankfully, I believe this is likely to be the case by default.
See Seth Herd’s comment below for a perspective:
I’m specifically referring to this answer, combined with a comment that convinced me that the o1 deception so far is plausibly just a capabilities issue:
https://www.lesswrong.com/posts/3Auq76LFtBA4Jp5M8/why-is-o1-so-deceptive#L5WsfcTa59FHje5hu
https://www.lesswrong.com/posts/3Auq76LFtBA4Jp5M8/why-is-o1-so-deceptive#xzcKArvsCxfJY2Fyi
I think this is the crux.
To be clear, I am not saying that o1 rules out the ability of more capable models to deceive naturally, but I think 1 thing blunts the blow a lot here:
As I said above, the more likely explanation is that there’s an asymmetry in capabilities that’s causing the results, where just knowing what specific URL the customer wants doesn’t equal the model having the capability to retrieve a working URL, so this is probably at the heart of this behavior.
So for now, what I suspect is that o1′s safety when scaled up mostly remains unknown and untested (but this is still a bit of bad news).
I think the distinction is made to avoid confusing capability and alignment failures here.
I agree that it doesn’t satisfy the user’s request.
Yeah, this is the biggest issue of OpenAI for me, in that they aren’t trying to steer too hard against deception.