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.

Thanks for doing this! It's looking like we may need major economic changes to keep up with job automation (assuming we don't get an outright AGI takeover). So, getting started on thinking this stuff through may have immense benefit. Like the alignment problem, it's embarassing as a species that we haven't thought about this more when the train appears to be barreling down the tracks. So, kudos and keep it up!

Now, the critique: doing this analysis for only the richest country in the world seems obviously inadequate and not even a good starting point; something like the median country would be more useful. OTOH, I see why you're doing this; I'm a US citizen and numbers are easier to get here.

So in sum, I think the bigger issue is the second one you mention: global tax reform that can actually capture the profits made from various AI companies and the much larger base of AI-enabled companies that don't pay nearly as much for AI as they would for labor, but reap massive profits. They will often be "based" in whatever country gives them the lowest tax rates. So we have another thorny global coordination problem.

I was also going to mention not accounting for the tech changes this is accounting for. So I recommend you add that this is part 1 in the intro to head off that frustration among readers.

This is evidence of nothing but your (rather odd) lack of noticing. If anything, it might be easier to not notice stimulant meds if you benefit from them, but I'm not sure about that either.

Because they're relatively short duration, some people take Ritalin to get focused work done (when it's not interesting enough to generate hyper focus), and not at other times.

You need to have bunches of people use it for it to be any good, no matter how good the algorithm.

Quick summary: it's super easy and useful to learn a little speedreading. Just move your finger a bit faster than your eyes are comfortable moving and force yourself to keep up as best you can. Just a little of this can go a long way when combined with a skimming-for-important-bits mindset with nonfiction and academic articles.

Explicit answers:

1. With Regard To brain function. It's vague, just this matches my understanding of how the brain works.
2. I don't remember. I think it was just a matter of forcing yourself to go faster than you could subvocalize. And to try to notice when you were subvocalizing or not. The core technique in learning speed reading was to move your finger along the lines, and keep going slightly faster. I learned this from the very old book How to Read a Book.
3. I'm pretty sure it both a) literally hasn't and more importantly b) effectively has increased my learning rate for semantic knowledge. Fundamentally it doesn't. It doesn't allow you to think faster (or at least not much), so if you're reading complex stuff quickly, you're just not going to understand or remember it. BUT it allows you to quickly skim to find the semantic knowledge you find worth learning and remembering. So your effect rate is higher. Learning to skim is absolutely crucial for academia, and speedreading is very useful for skimming quickly. You sort of get a vague idea of what you're reading, and then spend time on the stuff that might be important.
4. That mentioned some of the downsides. It's what you were guessing: you can't really take things in faster, so it's a quantity/quality tradeoff. Here's another manifestation. I rarely bother to speedread fiction, because I can't imagine the setting and action if I do. Come to think of it, maybe I could a bit better if I practiced it more. But I usually just skip ahead or better yet, put the book down if I'm tempted to skim. There are lots of great books to read for pleasure, and if it's not fun word by word and worth imagining, I don't really see the point. But a friend of mine speedreads tons of fiction, so there is a point; he says he also can't imagine it in detail, but I guess he's enjoying taking in the story in broader strokes.
5. 6. I have no idea what my WPM was or is. It's abundantly clear that I learned to read far faster.
6. Probably like level 20? Depends if it's a nonlinear curve.
7. Here's the interesting bit: it was very, very easy to learn some useful speedreading, just by using my finger to force my eyes to move faster on the page (and maybe some lesser techniques I've now forgotten). I think I probably spent 20 minutes to an hour doing that explictly, then was able to push my reading speed as high as I want. I think with more practice, I could probably take things in and imagine scenes a little better at high speed, but it seemed like diminishing returns, and I'm not the type to just sit and practice skills. To be fair, I spent my childhood reading instead of doing former schooling, so I might've had a deeper skill base to work from.