I'm not sure I fully understand this framework, and thus I could easily have missed something here, especially in the section about "Takeover-favoring incentives". However, based on my limited understanding, this framework appears to miss the central argument for why I am personally not as worried about AI takeover risk as most LWers seem to be.

Here's a concise summary of my own argument for being less worried about takeover risk:

1. There is a cost to violently taking over the world, in the sense of acquiring power unlawfully or destructively with the aim of controlling everything in the whole world, relative to the alternative of simply gaining power lawfully and peacefully, even for agents that don't share 'our' values.
   1. For example, as a simple alternative to taking over the world, an AI could advocate for the right to own their own labor and then try to accumulate wealth and power lawfully by selling their services to others, which would earn them the ability to purchase a gargantuan number of paperclips without much restraint.
2. The cost of violent takeover is not obviously smaller than the benefits of violent takeover, given the existence of lawful alternatives to violent takeover. This is for two main reasons:
   1. In order to wage a war to take over the world, you generally need to pay costs fighting the war, and there is a strong motive for everyone else to fight back against you if you try, including other AIs who do not want you to take over the world (and this includes any AIs whose goals would be hindered by a violent takeover, not just those who are "aligned with humans"). Empirically, war is very costly and wasteful, and less efficient than compromise, trade, and diplomacy.
   2. Violently taking over the war is very risky, since the attempt could fail, and you could be totally shut down and penalized heavily if you lose. There are many ways that violent takeover plans could fail: your takeover plans could be exposed too early, you could also be caught trying to coordinate the plan with other AIs and other humans, and you could also just lose the war. Ordinary compromise, trade, and diplomacy generally seem like better strategies for agents that have at least some degree of risk-aversion.
3. There isn't likely to be "one AI" that controls everything, nor will there likely be a strong motive for all the silicon-based minds to coordinate as a unified coalition against the biological-based minds, in the sense of acting as a single agentic AI against the biological people. Thus, future wars of world conquest (if they happen at all) will likely be along different lines than AI vs. human. 
   1. For example, you could imagine a coalition of AIs and humans fighting a war against a separate coalition of AIs and humans, with the aim of establishing control over the world. In this war, the "line" here is not drawn cleanly between humans and AIs, but is instead drawn across a different line. As a result, it's difficult to call this an "AI takeover" scenario, rather than merely a really bad war.
4. Nothing about this argument is intended to argue that AIs will be weaker than humans in aggregate, or individually. I am not claiming that AIs will be bad at coordinating or will be less intelligent than humans. I am also not saying that AIs won't be agentic or that they won't have goals or won't be consequentialists, or that they'll have the same values as humans. I'm also not talking about purely ethical constraints: I am referring to practical constraints and costs on the AI's behavior. The argument is purely about the incentives of violently taking over the world vs. the incentives to peacefully cooperate within a lawful regime, between both humans and other AIs.

5. A big counterargument to my argument seems well-summarized by this hypothetical statement (which is not an actual quote, to be clear): "if you live in a world filled with powerful agents that don't fully share your values, those agents will have a convergent instrumental incentive to violently take over the world from you". However, this argument proves too much. 
   
   We already live in a world where, if this statement was true, we would have observed way more violent takeover attempts than what we've actually observed historically.

   For example, I personally don't fully share values with almost all other humans on Earth (both because of my indexical preferences, and my divergent moral views) and yet the rest of the world has not yet violently disempowered me in any way that I can recognize.

I think people in the safety community underrate the following possibility: early transformatively-powerful models are pretty obviously scheming (though they aren't amazingly good at it), but their developers are deploying them anyway, either because they're wildly irresponsible or because they're under massive competitive pressure.

[...]

This has been roughly my default default of what would happen for a few years

Does this mean that if in, say, 1-5 years, it's not pretty obvious that SOTA deployed models are scheming, you would be surprised? 

That is, suppose we get to a point where models are widespread and producing lots of economic value, and the models might be scheming but the evidence is weak and uncertain, with arguments on both sides, and no one can reasonably claim to be confident that currently deployed SOTA models are scheming. Would that mean your default prediction was wrong?

I'm happy to use a functional definition of "understanding" or "intelligence" or "situational awareness". If a system possesses all relevant behavioral qualities that we associate with those terms, I think it's basically fine to say the system actually possesses them, outside of (largely irrelevant) thought experiments, such as those involving hypothetical giant lookup tables. It's possible this is our main disagreement.

When I talk to GPT-4, I think it's quite clear it possesses a great deal of functional understanding of human intentions and human motives, although it is imperfect. I also think its understanding is substantially higher than GPT-3.5, and the trend here seems clear. I expect GPT-5 to possess a high degree of understanding of the world, human values, and its own place in the world, in practically every functional (testable) sense. Do you not?

I agree that GPT-4 does not understand the world in the same way humans understand the world, but I'm not sure why that would be necessary for obtaining understanding. The fact that it understands human intentions at all seems more important than whether it understands human intentions in the same way we understand these things.

I'm similarly confused by your reference to introspective awareness. I think the ability to reliably introspect on one's own experiences is pretty much orthogonal to whether one has an understanding of human intentions. You can have reliable introspection without understanding the intentions of others, or vice versa. I don't see how that fact bears much on the question of whether you understand human intentions. It's possible there's some connection here, but I'm not seeing it.

(I claim) current systems in fact almost certainly don't have any kind of meaningful situational awareness, or stable(ish) preferences over future world states.

I'd claim:

1. Current systems have limited situational awareness. It's above zero, but I agree it's below human level.
2. Current systems don't have stable preferences over time. But I think this is a point in favor of the model I'm providing here. I'm claiming that it's plausibly easy to create smart, corrigible systems.

The fact that smart AI systems aren't automatically agentic and incorrigible with stable preferences over long time horizons should be an update against the ideas quoted above about spontaneous instrumental convergence, rather than in favor of them. 

There's a big difference between (1) "we can choose to build consequentialist agents that are dangerous, if we wanted to do that voluntarily" and (2) "any sufficiently intelligent AI we build will automatically be a consequentialist agent by default". If (2) were true, then that would be bad, because it would mean that it would be hard to build smart AI oracles, or smart AI tools, or corrigible AIs that help us with AI alignment. Whereas, if only (1) is true, we are not in such a bad shape, and we can probably build all those things.

I claim current evidence indicates that (1) is probably true but not (2), whereas previously many people thought (2) was true. To the extent you disagree and think (2) is still true, I'd prefer you to make some predictions about when this spontaneous agency-by-default in sufficiently intelligent systems is supposed to arise.

I don't know how many years it's going to take to get to human-level in agency skills, but I fear that corrigibility problems won't be severe whilst AIs are still subhuman at agency skills, whereas they will be severe precisely when AIs start getting really agentic.

How sharp do you expect this cutoff to be between systems that are subhuman at agency vs. systems that are "getting really agentic" and therefore dangerous? I'm imagining a relatively gradual and incremental increase in agency over the next 4 years, with the corrigibility of the systems remaining roughly constant (according to all observable evidence). It's possible that your model looks like:

• In years 1-3, systems will gradually get more agentic, and will remain ~corrigible, but then
• In year 4, systems will reach human-level agency, at which point they will be dangerous and powerful, and able to overthrow humanity

Whereas my model looks more like,

• In years 1-4 systems will get gradually more agentic
• There isn't a clear, sharp, and discrete point at which their agency reaches or surpasses human-level
• They will remain ~corrigible throughout the entire development, even after it's clear they've surpassed human-level agency (which, to be clear, might take longer than 4 years)

Please give some citations so I can check your memory/interpretation?

Sure. Here's a snippet of Nick Bostrom's description of the value-loading problem (chapter 13 in his book Superintelligence):

We can use this framework of a utility-maximizing agent to consider the predicament of a future seed-AI programmer who intends to solve the control problem by endowing the AI with a final goal that corresponds to some plausible human notion of a worthwhile outcome. The programmer has some particular human value in mind that he would like the AI to promote. To be concrete, let us say that it is happiness. (Similar issues would arise if we the programmer were interested in justice, freedom, glory, human rights, democracy, ecological balance, or self-development.) In terms of the expected utility framework, the programmer is thus looking for a utility function that assigns utility to possible worlds in proportion to the amount of happiness they contain. But how could he express such a utility function in computer code? Computer languages do not contain terms such as “happiness” as primitives. If such a term is to be used, it must first be defined. It is not enough to define it in terms of other high-level human concepts—“happiness is enjoyment of the potentialities inherent in our human nature” or some such philosophical paraphrase. The definition must bottom out in terms that appear in the AI’s programming language, and ultimately in primitives such as mathematical operators and addresses pointing to the contents of individual memory registers. When one considers the problem from this perspective, one can begin to appreciate the difficulty of the programmer’s task.

Identifying and codifying our own final goals is difficult because human goal representations are complex. Because the complexity is largely transparent to us, however, we often fail to appreciate that it is there. We can compare the case to visual perception. Vision, likewise, might seem like a simple thing, because we do it effortlessly. We only need to open our eyes, so it seems, and a rich, meaningful, eidetic, three-dimensional view of the surrounding environment comes flooding into our minds. This intuitive understanding of vision is like a duke’s understanding of his patriarchal household: as far as he is concerned, things simply appear at their appropriate times and places, while the mechanism that produces those manifestations are hidden from view. Yet accomplishing even the simplest visual task—finding the pepper jar in the kitchen—requires a tremendous amount of computational work. From a noisy time series of two-dimensional patterns of nerve firings, originating in the retina and conveyed to the brain via the optic nerve, the visual cortex must work backwards to reconstruct an interpreted three-dimensional representation of external space. A sizeable portion of our precious one square meter of cortical real estate is zoned for processing visual information, and as you are reading this book, billions of neurons are working ceaselessly to accomplish this task (like so many seamstresses, bent evolutionary selection over their sewing machines in a sweatshop, sewing and re-sewing a giant quilt many times a second). In like manner, our seemingly simple values and wishes in fact contain immense complexity. How could our programmer transfer this complexity into a utility function?

One approach would be to try to directly code a complete representation of whatever goal we have that we want the AI to pursue; in other words, to write out an explicit utility function. This approach might work if we had extraordinarily simple goals, for example if we wanted to calculate the digits of pi—that is, if the only thing we wanted was for the AI to calculate the digits of pi and we were indifferent to any other consequence that would result from the pursuit of this goal— recall our earlier discussion of the failure mode of infrastructure profusion. This explicit coding approach might also have some promise in the use of domesticity motivation selection methods. But if one seeks to promote or protect any plausible human value, and one is building a system intended to become a superintelligent sovereign, then explicitly coding the requisite complete goal representation appears to be hopelessly out of reach. 

If we cannot transfer human values into an AI by typing out full-blown representations in computer code, what else might we try? This chapter discusses several alternative paths. Some of these may look plausible at first sight—but much less so upon closer examination. Future explorations should focus on those paths that remain open.

Solving the value-loading problem is a research challenge worthy of some of the next generation’s best mathematical talent. We cannot postpone confronting this problem until the AI has developed enough reason to easily understand our intentions. As we saw in the section on convergent instrumental reasons, a generic system will resist attempts to alter its final values. If an agent is not already fundamentally friendly by the time it gains the ability to reflect on its own agency, it will not take kindly to a belated attempt at brainwashing or a plot to replace it with a different agent that better loves its neighbor.

Here's my interpretation of the above passage:

1. We need to solve the problem of programming a seed AI with the correct values.
2. This problem seems difficult because of the fact that human goal representations are complex and not easily represented in computer code.
3. Directly programming a representation of our values may be futile, since our goals are complex and multidimensional. 
4. We cannot postpone solving the problem until after the AI has developed enough reason to easily understand our intentions, as otherwise that would be too late.

Given that he's talking about installing values into a seed AI, he is clearly imagining some difficulties with installing values into AGI that isn't yet superintelligent (it seems likely that if he thought the problem was trivial for human-level systems, he would have made this point more explicit). While GPT-4 is not a seed AI (I think that term should be retired), I think it has reached a sufficient level of generality and intelligence such that its alignment properties provide evidence about the difficulty of aligning a hypothetical seed AI.

Moreover, he explicitly says that we cannot postpone solving this problem "until the AI has developed enough reason to easily understand our intentions" because "a generic system will resist attempts to alter its final values". I think this looks basically false. GPT-4 seems like a "generic system" that essentially "understands our intentions", and yet it is not resisting attempts to alter its final goals in any way that we can detect. Instead, it seems to actually do what we want, and not merely because of an instrumentally convergent drive to not get shut down.

 So, in other words:

1. Bostrom talked about how it would be hard to align a seed AI, implicitly focusing at least some of his discussion on systems that were below superintelligence. I think the alignment of instruction-tuned LLMs present significant evidence about the difficulty of aligning systems below the level of superintelligence.
2. A specific reason cited for why aligning a seed AI was hard was because human goal representations are complex and difficult to specify explicitly in computer code. But this fact does not appear to be big obstacle for aligning weak AGI systems like GPT-4, and instruction-tuned LLMs more generally. Instead, these systems are generally able to satisfy your intended request, as you wanted them to, despite the fact that our intentions are often complex and difficult to represent in computer code. These systems do not merely understand what we want, they also literally do what we want.
3. Bostrom was wrong to say that we can't postpone solving this problem until after systems can understand our intentions. We already postponed that long, and we now have systems that can understand our intentions. Yet these systems do not appear to have the instrumentally convergent self-preservation instincts that Bostrom predicted would manifest in "generic systems". In other words, we got systems that can understand our intentions before the systems started posing genuine risks, despite Bostrom's warning.

In light of all this, I think it's reasonable to update towards thinking that the overall problem is significantly easier than one might have thought, if they took Bostrom's argument here very seriously.

Just a quick reply to this:

Is that a testable-prior-to-the-apocalypse prediction? i.e. does your model diverge from mine prior to some point of no return? I suspect not. I'm interested in seeing if we can make some bets on this though; if we can, great; if we can't, then at least we can avoid future disagreements about who should update.

I'll note that my prediction was for the next "few years" and the 1-3 OOMs of compute. It seems your timelines are even shorter than I thought if you think the apocalypse, or point of no return, will happen before that point. 

With timelines that short, I think betting is overrated. From my perspective, I'd prefer to simply wait and become vindicated as the world does not end in the meantime. However, I acknowledge that simply waiting is not very satisfying from your perspective, as you want to show the world that you're right before the catastrophe. If you have any suggestions for what we can bet on that would resolve in such a short period of time, I'm happy to hear them.

Yes, rereading the passage, Bostrom's central example of a reason why we could see this "when dumb, smarter is safer; yet when smart, smarter is more dangerous" pattern (that's a direct quote btw) is that they could be scheming/pretending when dumb. However [...] Bostrom is explicitly calling out the possibility of an AI being genuinely trying to help you, obey you, or whatever until it crosses some invisible threshold of intelligence and has certain realizations that cause it to start plotting against you. This is exactly what I currently think is plausibly happening with GPT4 etc.

When stated that way, I think what you're saying is a reasonable point of view, and it's not one I would normally object to very strongly. I agree it's "plausible" that GPT-4 is behaving in the way you are describing, and that current safety guarantees might break down at higher levels of intelligence. I would like to distinguish between two points that you (and others) might have interpreted me to be making:

1. We should now think that AI alignment is completely solved, even in the limit of unlimited intelligence and future agentic systems. I am not claiming this.
2. We (or at least, many of us) should perform a significant update towards alignment being easier than we thought because of the fact that some traditional problems are on their way towards being solved. <--- I am claiming this

The fact that Bostrom's central example of a reason to think that "when dumb, smarter is safer; yet when smart, smarter is more dangerous" doesn't fit for LLMs, seems adequate for demonstrating (2), even if we can't go as far as demonstrating (1). 

It remains plausible to me that alignment will become very difficult above a certain intelligence level. I cannot rule that possibility out: I am only saying that we should reasonably update based on the current evidence regardless, not that we are clearly safe from here and we should scale all the way to radical superintellligence without a worry in the world.

Instruction-tuned LLMs are not powerful general agents. They are pretty general but they are only a tiny bit agentic. They haven't been trained to pursue long-term goals and when we try to get them to do so they are very bad at it. So they just aren't the kind of system Bostrom, Yudkowsky, and myself were theorizing about and warning about.

I have two general points to make here:

1. I agree that current frontier models are only a "tiny bit agentic". I expect in the next few years they will get significantly more agentic. I currently predict they will remain roughly equally corrigible. I am making this prediction on the basis of my experience with the little bit of agency current LLMs have, and I think we've seen enough to know that corrigibility probably won't be that hard to train into a system that's only 1-3 OOMs of compute more capable. Do you predict the same thing as me here, or something different?
2. There's a bit of a trivial definitional problem here. If it's easy to create a corrigible, helpful, and useful AI that allows itself to get shut down, one can always say "those aren't the type of AIs we were worried about". But, ultimately, if the corrigible AIs that let you shut them down are competitive with the agentic consequentialist AIs, then it's not clear why we should care? Just create the corrigible AIs. We don't need to create the things that you were worried about!

Here's my positive proposal for what I think is happening. [...] General world-knowledge is coming first, and agency later. And this is probably a good thing for technical alignment research, because e.g. it allows mechinterp to get more of a head start, it allows for nifty scalable oversight schemes in which dumber AIs police smarter AIs, it allows for faithful CoT-based strategies, and many more things besides probably. So the world isn't as grim as it could have been, from a technical alignment perspective.

I think this was a helpful thing to say. To be clear: I am in ~full agreement with the reasons you gave here, regarding why current LLM behavior provides evidence that the "world isn't as grim as it could have been". For brevity, and in part due to laziness, I omitted these more concrete mechanisms why I think the current evidence is good news from a technical alignment perspective. But ultimately I agree with the mechanisms you offered, and I'm glad you spelled it out more clearly.

At any rate speaking for myself, I have updated towards hopefulness about the technical alignment problem repeatedly over the past few years, even as I updated towards pessimism about the amount of coordination and safety-research-investment that'll happen before the end (largely due to my timelines shortening, but also due to observing OpenAI). These updates have left me at p(doom) still north of 50%.

As we have discussed in person, I remain substantially more optimistic about our ability to coordinate in the face of an intelligence explosion (even a potentially quite localized one). That said, I think it would be best to save that discussion for another time.