The important thing for alignment work isn't the median prediction; if we had an alignment solution just by then, we'd have a 50% chance of dying from that lack.

I think the biggest takeaway is that nobody has a very precise and reliable prediction, so if we want to have good alignment plans in advance of AGI, we'd better get cracking.

I think Daniel's estimate does include a pretty specific and plausible model of a path to AGI, so I take his the most seriously. My model of possible AGI architectures requires even less compute than his, but I think the Hofstadter principle applies to AGI development if not compute progress.

Estimates in the absence of gears-level models of AGI seem much more uncertain, which might be why Ajeya and Ege's have much wider distributions.

That all makes sense. To expand a little more on some of the logic:

It seems like the outcome of a partial pause rests in part on whether that would tend to put people in the lead of the AGI race who are more or less safety-concerned.

I think it's nontrivial that we currently have three teams in the lead who all appear to honestly take the risks very seriously, and changing that might be a very bad idea.

On the other hand, the argument for alignment risks is quite strong, and we might expect more people to take the risks more seriously as those arguments diffuse. This might not happen if polarization becomes a large factor in beliefs on AGI risk. The evidence for climate change was also pretty strong, but we saw half of America believe in it less, not more, as evidence mounted. The lines of polarization would be different in this case, but I'm afraid it could happen. I outlined that case a little in AI scares and changing public beliefs

In that case, I think a partial pause would have a negative expected value, as the current lead decayed, and more people who believe in risks less get into the lead by circumventing the pause.

This makes me highly unsure if a pause would be net-positive. Having alignment solutions won't help if they're not implemented because the taxes are too high.

The creation of compute overhang is another reason to worry about a pause. It's highly uncertain how far we are from making adequate compute for AGI affordable to individuals. Algorithms and compute will keep getting better during a pause. So will theory of AGI, along with theory of alignment.

This puts me, and I think the alignment community at large, in a very uncomfortable position of not knowing whether a realistic pause would be helpful.

It does seem clear that creating mechanisms and political will for a pause are a good idea.

Advocating for more safety work also seems clear cut.

To this end, I think it's true that you create more political capitol by successfully pushing for policy.

A pause now would create even more capitol, but it's also less likely to be a win, and it could wind up creating polarization and so costing rather than creating capitol. It's harder to argue for a pause now when even most alignment folks think we're years from AGI.

So perhaps the low-hanging fruit is pushing for voluntary RSPs, and government funding for safety work. These are clear improvements, and likely to be wins that create capitol for a pause as we get closer to AGI.

There's a lot of uncertainty here, and that's uncomfortable. More discussion like this should help resolve that uncertainty, and thereby help clarify and unify the collective will of the safety community.

That is indeed a lot of points. Let me try to parse them and respond, because I think this discussion is critically important.

Point 1: overhang.

Your first two paragraphs seem to be pointing to downsides of progress, and saying that it would be better if nobody made that progress. I agree. We don't have guaranteed methods of alignment, and I think our odds of survival would be much better if everyone went way slower on developing AGI.

The standard thinking, which could use more inspection, but which I agree with, is that this is simply not going to happen. Individuals that decide to step aside are slowing progress only slightly. This leaves compute overhang that someone else is going to take advantage of, with nearly the competence, and only slightly slower. Those individuals who pick up the banner and create AGI will not be infinitely reckless, but the faster progress from that overhang will make whatever level of caution they have less effective.

This is a separate argument from regulation. Adequate regulation will slow progress universally, rather than leaving it up to the wisdom and conscience of every individual who might decide to develop AGI.

I don't think it's impossible to slow and meter progress so that overhang isn't an issue. But I think it is effectively even harder than alignment. We have decent suggestions on the table for alignment now, and as far as I know, no equally promising suggestions for getting everyone (and it does take almost everyone coordinating) to pass up the immense opportunities offered by capabilities overhangs.

Point 2: Are LLMs safer than other approaches?

I agree that this is a questionable proposition. I think it's worth questioning. Aiming progress at easier-to-align approaches seems highly worthwhile.

I agree that an LLM may have something like a mind inside. I think current versions are almost certainly too dumb to be existentially dangerous (at least directly - if a facebook algorithm can nearly cause an insurrection, who knows what dangerous side effects any AI can have).

I'm less worried about GPT10 playing a superintelligent, Waluigi-collapsed villain than I am about a GPT6 that has been amplified to agency, situational awareness, and weak superintelligence by scaffolding it into something like a cognitive architecture. I think this type of advance is inevitable. ChatGPT extensions and Bing Chat both use internal prompting to boost intelligence, and approaches like SmartGPT and Tree of Thoughts massively improve benchmark results over the base LLM.

Fortunately, this direction also has huge advantages for alignment. It has a very low alignment tax, since you give them additional goals in natural language, like "support human empowerment" or whatever the SOTA alignment goal is. And they have vastly better interpretability since they're at least summarizing their thoughts in natural language.

Here's where your skepticism that they're being honest about summarizing those thoughts comes into full force. I agree that it's not reliable; for instance, changing the intermediate answer in chain of thought prompting often doesn't change the final output, indicating that that output was for show.

However, a safer setup is to never use the same model twice. When you use chain-of-thought reasoning, construct a new context with the relevant information from memory; don't just let the context window accrue, since this allows fake chains-of-thought and the collapse of the simulator into a waluigi.

Scaffolded LLMs should not turn an LLM into an agent, but rather create a committee of LLMs that are called for individual questions needed to accomplish that committee's goals.

This isn't remotely a solution to the alignment problem, but it really seems to have massive upsides, and only the same downsides as other practically viable approaches to AGI.

To be clear, I only see some form of RL agents as the other practical possibility, and I like our odds much less with those.

I think there are other, even more readily alignable approaches to AGI. But they all seem wildly impractical. I think we need to get ready to align the AGI we get, rather than just preparing to say I-told-you-so after the world refuses to forego massive incentives to take a much slower but safer route to AGI.

To paraphrase, we need to go to the alignment war with the AGI we get, not the AGI we want.

I really like your recent series of posts that succinctly address common objections/questions/suggestions about alignment concerns. I'm making a list to show my favorite skeptics (all ML/AI people; nontechnical people, as Connor Leahy puts it, tend to respond "You fucking what? Oh hell no!" or similar when informed that we are going to make genuinely smarter-than-us AI soonish).

We do have ways to get an AI to do what we want. The hardcoded algorithmic maximizer approach seems to be utterly impractical at this point. That leaves us with approaches that don't obviously do a good job of preserving their own goals as they learn and evolve:

1. training a system to pursue things we like, such as shard theory and similar approaches.
2. training or hand-coding a critic system, as in outlined approaches from Steve Byrnes and me as well as many others. Nicely summarized as a Steering systems approach. This seems a bit less sketchy than training in our goals and hoping they generalize adequately, but still pretty sketchy. 
3. Telling the agent what to do in a Natural language alignment approach. This seems absurdly naive. However, I'm starting to think our first human-plus AGIs will be wrapped or scaffolded LLMs, and they to a nontrivial degree actually think in natural language. People are right now specifying goals in natural language, and those can include alignment goals (or destroying humanity, haha).  I just wrote an in-depth post on the potential Capabilities and alignment of LLM cognitive architectures, but I don't have a lot to say about stability in that post.

None of these directly address what I'm calling The alignment stability problem, to give a name to what you're addressing here. I think addressing it will work very differently in each of the three approaches listed above, and might well come down to implementational details within each approach. I think we should be turning our attention to this problem along with the initial alignment problems, because some of the optimism in the field stems from thinking about initial alignment and not long-term stability.

Edit: I left out Ozyrus's posts on approach 3. He's the first person I know of to see agentized LLMs coming, outside of David Shapiro's 2021 book. His post was written a year ago and posted two weeks ago to avoid infohazards. I'm sure there are others who saw this coming more clearly than I did, but I thought I'd try to give credit where it's due.

Great analysis. I'm impressed by how thoroughly you've thought this through in the last week or so. I hadn't gotten as far. I concur with your projected timeline, including the difficulty of putting time units onto it. Of course, we'll probably both be wrong in important ways, but I think it's important to at least try to do semi-accurate prediction if we want to be useful.

I have only one substantive addition to your projected timeline, but I think it's important for the alignment implications.

LLM-bots are inherently easy to align. At least for surface-level alignment. You can tell them "make me a lot of money selling shoes, but also make the world a better place" and they will try to do both. Yes, there are still tons of ways this can go off the rails. It doesn't solve outer alignment or alignment stability, for a start. But GPT4's ability to balance several goals, including ethical ones, and to reason about ethics, is impressive.^[1] You can easily make agents that both try to make money, and thinks about not harming people.

In short, the fact that you can do this is going to seep into the public consciousness, and we may see regulations and will definitely see social pressure to do this.

I think the agent disasters you describe will occur, but they will happen to people that don't put safeguards into their bots, like "track how much of my money you're spending and stop if it hits $X and check with me". When agent disasters affect other people, the media will blow it sky high, and everyone will say "why the hell didn't you have your bot worry about wrecking things for others?". Those who do put additional ethical goals into their agents will crow about it. There will be pressure to conform and run safe bots. As bot disasters get more clever, people will take more seriously the big bot disaster.

Will all of that matter? I don't know. But predicting the social and economic backdrop for alignment work is worth trying.

Edit: I finished my own followup post on the topic, Capabilities and alignment of LLM cognitive architectures. It's a cognitive psychology/neuroscience perspective on why these things might work better, faster than you'd intuitively think. Improvements to the executive function (outer script code) and episodic memory (pinecone or other vector search over saved text files) will interact so that improvements in each make the rest of system work better and easier to improve.

1. ^{^}

   I did a little informal testing of asking for responses in hypothetical situations where ethical and financial goals collide, and it did a remarkably good job, including coming up with win/win solutions that would've taken me a while to come up with. It looked like the ethical/capitalist reasoning of a pretty intelligent person; but also a fairly ethical one.

I applaud the effort. Big upvote for actually trying to solve the problem, by coming up with a way to create safe, aligned AGI. If only more people were doing this instead of hand wringing, arguing, or "working on the problem" in poorly-thought-out, too-indirect-to-probably-help-in-time ways. Good job going straight for the throat.

That said: It seems to me like the problem isn't maximization or even optimization; it's conflicting goals.

If I have a goal to make some paperclips, not as many as I can, just a few trillion, I may still enter a deadly conflict with humanity. If humanity knows about me and my paperclips goal, they'll shut me down. The most certain way to get those paperclips made may be to eliminate unpredictable humanity's ability to mess with my plans.

For essentialaly this reason, I think quantilization is and was recognized as a dead-end. You don't have to take your goals to the logical extreme to still take them way too far for humanity's good.

I read the this post, but not the remainder yet, so you might've addressed this elsewhere.

Does Bostrom address human modification/amplification? I'd think he would, but I'm not sure he actually did, at least not in any depth.

A world in which we all get sad because we can't make new philosophy breakthroughs and don't bother to engineer out that sadness seems quite implausible. Yet I didn't hear this addressed in his interview with Liv Boeree.

And I'm not going to buy and read it just to find out.

I think this is a great project! Clarifying why informed people have such different opinions on AGI x-risk seems like a useful path to improving our odds. I've been working on a post on alignment difficulty cruxes that covers much of hte same ground.

Your list is a good starting point. I'd add:

Time window of analysis: I think a lot of people give a low p(doom) because they're only thinking about the few years after we get real AGI.

Paul Christiano, for instance, adds a substantial chance that we've "irreversibly messed up our future within 10 years of building powerful AI" over and above the odds that we all die from takeover or misuse. (in My views on “doom”, from April 2023).

Here are my top 4 cruxes for alignment difficulty which is different but highly overlapping with p(doom).

How AGI will be designed and aligned

• How we attempt it is obviously important for the odds of success

How well RL alignment will generalize

• most people are assuming we'll use RL for alignment. I think we shouldn't and won't

Whether we need to understand human values better

• I think we shouldn't and won't try to align AGI to human values, but many think we will try and fail

Whether societal factors are included in alignment difficulty

• Sometimes people are just answering whether the AGI will do what we want, and not including whether that will result in doom (eg., ASI super-weapons proliferate until someone starts a hyper-destructive conflict).

Other important cruxes are mentioned in Stop talking about p(doom) - basically, what the heck one includes in their calculation, like my first point on time windows.

Fascinating, thank you!

I think the reality here is probably complex. I think we can direct our thoughts to some degree, and that in turn creates our feelings to some degree. Using that wisely isn't trivial. If I obsess about controlling my thinking, that could easily become upsetting.

I do think there's a good chance that the views David Foster Wallace espouses here were causally linked to his depression and suicide. They should be taken with caution. But doing the opposite isn't probably the best approach either

I had thought that cognitive reframing is part of some well-regarded therapeutic approaches to depression. While one can't choose how to feel, it is pretty apparent that we can, sometimes, choose what to think. When I ask myself "what should I think about now?" I get what seems like meaningful answers, and they direct my train of thought to a nontrivial degree - but not infinitely. My thoughts return to emotionally charged topics. If this upsets me, those topics become even more emotionally charged, and my thoughts return to them more often. This is the "don't think of a white bear" phenomenon.

However, gentle redirection does seem to work. Reframing my understanding of situations in ways that make me happier does appear to sometimes make me happier.

But thinking I should be able to do this infinitely is unrealistic, and my failure to do so would be upsetting if I thought I should be able to control my feelings and my thoughts relatively thoroughly.

I think this is a fascinating topic. I think therapy and psychology is in its infancy, and I expect us to have vastly better treatment for depression relatively soon. It will probably involve hugs and puppies as well as a better understanding of how we can and should try to think about our thinking.