Conversation on technology forecasting and gradualism

Quoth Paul:

seems like you have to make the wright flyer much better before it’s important, and that it becomes more like an industry as that happens, and that this is intimately related to why so few people were working on it

Is this basically saying ‘the Wright brothers didn’t personally capture much value by inventing heavier-than-air flying machines, and this was foreseeable, which is why there wasn’t a huge industry effort already underway to try to build such machines as fast as possible.’ ?

My maybe-wrong model of Eliezer says here ‘the Wright brothers knew a (Thielian) secret’, while my maybe-wrong model of Paul instead says:

• They didn’t know a secret — it was obvious to tons of people that you could do something sorta like what the Wright brothers did and thereby invent airplanes; the Wright brothers just had unusual non-monetary goals that made them passionate to do a thing most people didn’t care about.
• Or maybe it’s better to say: they knew some specific secrets about physics/engineering, but only because other people correctly saw ‘there are secrets to be found here, but they’re stamp-collecting secrets of little economic value to me, so I won’t bother to learn the secrets’. ~Everyone knows where the treasure is located, and ~everyone knows the treasure won’t make you rich.

I mean, I think they knew a bit of stuff, but it generally takes a lot of stuff to make something valuable, and the more people have been looking around in an area the more confident you can be that it’s going to take a lot of stuff to do much better, and it starts to look like an extremely strong regularity for big industries like ML or semiconductors

it’s pretty rare to find small ideas that don’t take a bunch of work to have big impacts

I don’t know exactly what a thielian secret is (haven’t read the reference and just have a vibe)

straightening it out a bit, I have 2 beliefs that combine disjunctively: (i) generally it takes a lot of work to do stuff, as a strong empirical fact about technology, (ii) generally if the returns are bigger there are more people working on it, as a slightly-less-strong fact about sociology

secrets = important undiscovered information (or information that’s been discovered but isn’t widely known), that you can use to get an edge in something. https://www.lesswrong.com/posts/ReB7yoF22GuerNfhH/thiel-on-secrets-and-indefiniteness

There seems to be a Paul/Eliezer disagreement about how common these are in general. And maybe a disagreement about how much more efficiently humanity discovers and propagates secrets as you scale up the secret’s value?

My guess is that everyone agrees with claims 1, 2, and 3 here (please let me know if I’m wrong!):

1. The history of humanity looks less like Long Series of Cheat Codes World, and more like Well-Designed Game World.

In Long Series of Cheat Codes World, human history looks like this, over and over: Some guy found a cheat code that totally outclasses everyone else and makes him God or Emperor, until everyone else starts using the cheat code too (if the Emperor allows it). After which things are maybe normal for another 50 years, until a new Cheat Code arises that makes its first adopters invincible gods relative to the previous tech generation, and then the cycle repeats.

In Well-Designed Game World, you can sometimes eke out a small advantage, and the balance isn’t perfect, but it’s pretty good and the leveling-up tends to be gradual. A level 100 character totally outclasses a level 1 character, and some level transitions are a bigger deal than others, but there’s no level that makes you a god relative to the people one level below you.

2. General intelligence took over the world once. Someone who updated on that fact but otherwise hasn’t thought much about the topic should not consider it ‘bonkers’ that machine general intelligence could take over the world too, even though they should still consider it ‘bonkers’ that eg a coffee startup could take over the world.

(Because beverages have never taken over the world before, whereas general intelligence has; and because our inside-view models of coffee and of general intelligence make it a lot harder to imagine plausible mechanisms by which coffee could make someone emperor, kill all humans, etc., compared to general intelligence.)

(In the game analogy, the situation is a bit like ‘I’ve never found a crazy cheat code or exploit in this game, but I haven’t ruled out that there is one, and I heard of a character once who did a lot of crazy stuff that’s at least suggestive that she might have had a cheat code.’)

3. AGI is arising in a world where agents with science and civilization already exist, whereas humans didn’t arise in such a world. This is one reason to think AGI might not take over the world, but it’s not a strong enough consideration on its own to make the scenario ‘bonkers’ (because AGIs are likely to differ from humans in many respects, and it wouldn’t obviously be bonkers if the first AGIs turned out to be qualitatively way smarter, cheaper to run, etc.).

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If folks agree with the above, then I’m confused about how one updates from the above epistemic state to ‘bonkers’.

It was to a large extent physics facts that determined how easy it was to understand the feasibility of nukes without (say) decades of very niche specialized study. Likewise, it was physics facts that determined you need rare materials, many scientists, and a large engineering+infrastructure project to build a nuke. In a world where the physics of nukes resulted in it being some PhD’s quiet ‘nobody thinks this will work’ project like Andrew Wiles secretly working on a proof of Fermat’s Last Theorem for seven years, that would have happened.

If an alien came to me in 1800 and told me that totally new physics would let future humans build city-destroying superbombs, then I don’t see why I should have considered it bonkers that it might be lone mad scientists rather than nations who built the first superbomb. The ‘lone mad scientist’ scenario sounds more conjunctive to me (assumes the mad scientist knows something that isn’t widely known, AND has the ability to act on that knowledge without tons of resources), so I guess it should have gotten less probability, but maybe not dramatically less?

‘Mad scientist builds city-destroying weapon in basement’ sounds wild to me, but I feel like almost all of the actual unlikeliness comes from the ‘city-destroying weapons exist at all’ part, and then the other parts only moderately lower the probability.

Likewise, I feel like the prima-facie craziness of basement AGI mostly comes from ‘generally intelligence is a crazy thing, it’s wild that anything could be that high-impact’, and a much smaller amount comes from ‘it’s wild that something important could happen in some person’s basement’.

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It does structurally make sense to me that Paul might know things I don’t about GPT-3 and/or humans that make it obvious to him that we roughly know the roadmap to AGI and it’s this.

If the entire ‘it’s bonkers that some niche part of ML could crack open AGI in 2026 and reveal that GPT-3 (and the mainstream-in-2026 stuff) was on a very different part of the tech tree’ view is coming from a detailed inside-view model of intelligence like this, then that immediately ends my confusion about the argument structure.

I don’t understand why you think you have the roadmap, and given a high-confidence roadmap I’m guessing I’d still put more probability than you on someone finding a very different, shorter path that works too. But the argument structure “roadmap therefore bonkers” makes sense to me.

If there are meant to be other arguments against ‘high-impact AGI via niche ideas/techniques’ that are strong enough to make it bonkers, then I remain confused about the argument structure and how it can carry that much weight.

I can imagine an inside-view model of human cognition, GPT-3 cognition, etc. that tells you ‘AGI coming from nowhere in 3 years is bonkers’; I can’t imagine an ML-is-a-reasonably-efficient-market argument that does the same, because even a perfectly efficient market isn’t omniscient and can still be surprised by undiscovered physics facts that tell you ‘nukes are relatively easy to build’ and ‘the fastest path to nukes is relatively hard to figure out’.

(Caveat: I’m using the ‘basement nukes’ and ‘Fermat’s last theorem’ analogy because it helps clarify the principles involved, not because I think AGI will be that extreme on the spectrum.)

Oh, I also wouldn’t be confused by a view like “I think it’s 25% likely we’ll see a more Eliezer-ish world. But it sounds like Eliezer is, like, 90% confident that will happen, and that level of confidence (and/or the weak reasoning he’s provided for that confidence) seems bonkers to me.”

The thing I’d be confused by is e.g. “ML is efficient-ish, therefore the out-of-the-blue-AGI scenario itself is bonkers and gets, like, 5% probability.”

(I’m unclear on whether this is acceptable for this channel, please let me know if not)

I can’t imagine an ML-is-a-reasonably-efficient-market argument that does the same, because even a perfectly efficient market isn’t omniscient and can still be surprised by undiscovered physics facts

I think this seems right as a first pass.

Suppose we then make the empirical observation that in tons and tons of other fields, it is extremely rare that people discover new facts that lead to immediate impact. (Set aside for now whether or not that’s true; assume that it is.) Two ways you could react to this:

1. Different fields are different fields. It’s not like there’s a common generative process that outputs a distribution of facts and how hard they are to find that is common across fields. Since there’s no common generative process, facts about field X shouldn’t be expected to transfer to make predictions about field Y.

2. There’s some latent reason, that we don’t currently know, that makes it so that it is rare for newly discovered facts to lead to immediate impact.

It seems like you’re saying that (2) is not a reasonable reaction (i.e. “not a valid argument structure”), and I don’t know why. There are lots of things we don’t know, is it really so bad to posit one more?

(Once we agree on the argument structure, we should then talk about e.g. reasons why such a latent reason can’t exist, or possible guesses as to what the latent reason is, etc, but fundamentally I feel generally okay with starting out with “there’s probably some reason for this empirical observation, and absent additional information, I should expect that reason to continue to hold”.)

I think 2 is a valid argument structure, but I didn’t mention it because I’d be surprised if it had enough evidential weight (in this case) to produce an ‘update to bonkers’. I’d love to hear more about this if anyone thinks I’m under-weighting this factor. (Or any others I left out!)

Idk if it gets all the way to “bonkers”, but (2) seems pretty strong to me, and is how I would interpret Paul-style arguments on timelines/takeoff if I were taking on what-I-believe-to-be your framework

Well, I’d love to hear more about that!

Another way of getting at my intuition: I feel like a view that assigns very small probability to ‘suddenly vastly superhuman AI, because something that high-impact hasn’t happened before’

(which still seems weird to me, because physics doesn’t know what ‘impact’ is and I don’t see what physical mechanism could forbid it that strongly and generally, short of simulation hypotheses)

… would also assign very small probability in 1800 to ‘given an alien prediction that totally new physics will let us build superbombs at least powerful enough to level cities, the superbomb in question will ignite the atmosphere or otherwise destroy the Earth’.

But this seems flatly wrong to me — if you buy that the bomb works by a totally different mechanism (and exploits a different physics regime) than eg gunpowder, then the output of the bomb is a physics question, and I don’t see how we can concentrate our probability mass much without probing the relevant physics. The history of boat and building sizes is a negligible input to ‘given a totally new kind of bomb that suddenly lets us (at least) destroy cities, what is the total destructive power of the bomb?’.

(Obviously the bomb didn’t destroy the Earth, and I wouldn’t be surprised if there’s some Bayesian evidence or method-for-picking-a-prior that could have validly helped you suspect as much in 1800? But it would be a suspicion, not a confident claim.)

would also assign very small probability in 1800 to ‘given an alien prediction that totally new physics will let us build superbombs at least powerful enough to level cities, the superbomb in question will ignite the atmosphere or otherwise destroy the Earth’

(As phrased you also have to take into account the question of whether humans would deploy the resulting superbomb, but I’ll ignore that effect for now.)

I think this isn’t exactly right. The “totally new physics” part seems important to update on.

Let’s suppose that, in the reference class we built of boat and building sizes, empirically nukes were the 1 technology out of 20 that had property X. (Maybe X is something like “discontinuous jump in things humans care about” or “immediate large impact on the world” or so on.) Then, I think in 1800 you assign ~5% to ‘the first superbomb at least powerful enough to level cities will ignite the atmosphere or otherwise destroy the Earth’.

Once you know more details about how the bomb works, you should be able to update away from 5%. Specifically, “entirely new physics” is an important detail that causes you to update away from 5%. I wouldn’t go as far as you in throwing out reference classes entirely at that point — there can still be unknown latent factors that apply at the level of physics — but I agree reference classes look harder to use in this case.

With AI, I start from ~5% and then I don’t really see any particular detail for AI that I think I should strongly update on. My impression is that Eliezer thinks that “general intelligence” is a qualitatively different sort of thing than that-which-neural-nets-are-doing, and maybe that’s what’s analogous to “entirely new physics”. I’m pretty unconvinced of this, but something in this genre feels quite crux-y for me.

Actually, I think I’ve lost the point of this analogy. What’s the claim for AI that’s analogous to

‘given an alien prediction that totally new physics will let us build superbombs at least powerful enough to level cities, the superbomb in question will ignite the atmosphere or otherwise destroy the Earth’

?

Like, it seems like this is saying “We figure out how to build a new technology that does X. What’s the chance it has side effect Y?” Where X and Y are basically unrelated.

I was previously interpreting the argument as “if we know there’s a new superbomb based on totally new physics, and we know that the first such superbomb is at least capable of leveling cities, what’s the probability it would have enough destructive force to also destroy the world”, but upon rereading that doesn’t actually seem to be what you were gesturing at.

I’m basically responding to this thing Ajeya wrote:

I think Paul’s view would say:

• Things certainly happen for the first time
• When they do, they happen at small scale in shitty prototypes, like the Wright Flyer or GPT-1 or AlphaGo or the Atari bots or whatever
• When they’re making a big impact on the world, it’s after a lot of investment and research, like commercial aircrafts in the decades after Kitty Hawk or like the investments people are in the middle of making now with AI that can assist with coding

To which my reply is: I agree that the first AGI systems will be shitty compared to later AGI systems. But Ajeya’s Paul-argument seems to additionally require that AGI systems be relatively unimpressive at cognition compared to preceding AI systems that weren’t AGI.

If this is because of some general law that things are shitty / low-impact when they “happen for the first time”, then I don’t understand what physical mechanism could produce such a general law that holds with such force.

As I see it, physics ‘doesn’t care’ about human conceptions of impactfulness, and will instead produce AGI prototypes, aircraft prototypes, and nuke prototypes that have as much impact as is implied by the detailed case-specific workings of general intelligence, flight, and nuclear chain reactions respectively.

We could frame the analogy as:

• ‘If there’s a year where AI goes from being unable to do competitive par-human reasoning in the hard sciences, to being able to do such reasoning, we should estimate the impact of the first such systems by drawing on our beliefs about par-human scientific reasoning itself.’
• Likewise: ‘If there’s a year where explosives go from being unable to destroy cities to being able to destroy cities, we should estimate the impact of the first such explosives by drawing on our beliefs about how (current or future) physics might allow a city to be destroyed, and what other effects or side-effects such a process might have. We should spend little or no time thinking about the impactfulness of the first steam engine or the first telescope.’

Seems like your argument is something like “when there’s a zero-to-one transition, then you have to make predictions based on reasoning about the technology itself”. I think in that case I’d say this thing from above:

My impression is that Eliezer thinks that “general intelligence” is a qualitatively different sort of thing than that-which-neural-nets-are-doing, and maybe that’s what’s analogous to “entirely new physics”. I’m pretty unconvinced of this, but something in this genre feels quite crux-y for me.

(Like, you wouldn’t a priori expect anything special to happen once conventional bombs become big enough to demolish a football stadium for the first time. It’s because nukes are based on “totally new physics” that you might expect unprecedented new impacts from nukes. What’s the analogous thing for AGI? Why isn’t AGI just regular AI but scaled up in a way that’s pretty continuous?)

I’m curious if you’d change your mind if you were convinced that AGI is just regular AI scaled up, with no qualitatively new methods — I expect you wouldn’t but idk why

In my own head, the way I think of ‘AGI’ is basically: “Something happened that allows humans to do biochemistry, materials science, particle physics, etc., even though none of those things were present in our environment of evolutionary adaptedness. Eventually, AI will similarly be able to generalize to biochemistry, materials science, particle physics, etc. We can call that kind of AI ‘AGI’.”

There might be facts I’m unaware of that justify conclusions like ‘AGI is mostly just a bigger version of current ML systems like GPT-3’, and there might be facts that justify conclusions like ‘AGI will be preceded by a long chain of predecessors, each slightly less general and slightly less capable than its successor’.

But if so, I’m assuming those will be facts about CS, human cognition, etc., not at all a list of a hundred facts like ‘the first steam engine didn’t take over the world’, ‘the first telescope didn’t take over the world’…. Because the physics of brains doesn’t care about those things, and because in discussing brains we’re already in ‘things that have been known to take over the world’ territory.

(I think that paying much attention at all to the technology-wide base rate for ‘does this allow you to take over the world?’, once you already know you’re doing something like ‘inventing a new human’, doesn’t really make sense at all? It sounds to me like going to a bookstore and then repeatedly worrying ‘What if they don’t have the book I’m looking for? Most stores don’t sell books at all, so this one might not have the one I want.’ If you know it’s a book store, then you shouldn’t be thinking at that level of generality at all; the base rate just goes out the window.)

My way of thinking about AGI is pretty different from saying AGI follows ‘totally new mystery physics’ — I’m explicitly anchoring to a known phenomenon, humans.

The analogous thing for nukes might be ‘we’re going to build a bomb that uses processes kind of like the ones found in the Sun in order to produce enough energy to destroy (at least) a city’.

The analogous thing for nukes might be ‘we’re going to build a bomb that uses processes kind of like the ones found in the Sun in order to produce enough energy to destroy (at least) a city’.

(And I assume the contentious claim is “that bomb would then ignite the atmosphere, destroy the world, or otherwise have hugely more impact than just destroying a city”.)

In 1800, we say “well, we’ll probably just make existing fires / bombs bigger and bigger until they can destroy a city, so we shouldn’t expect anything particularly novel or crazy to happen”, and assign (say) 5% to the claim.

There is a wrinkle: you said it was processes like the ones found in the Sun. Idk what the state of knowledge was like in 1800, but maybe they knew that the Sun couldn’t be a conventional fire. If so, then they could update to a higher probability.

(You could also infer that since someone bothered to mention “processes like the ones found in the Sun”, those processes must be ones we don’t know yet, which also allows you to make that update. I’m going to ignore that effect, but I’ll note that this is one way in which the phrasing of the claim is incorrectly pushing you in the direction of “assign higher probability”, and I think a similar thing happens for AI when saying “processes like those in the human brain”.)

With AI I don’t see why the human brain is a different kind of thing than (say) convnets. So I feel more inclined to just take the starting prior of 5%.

Presumably you think that assigning 5% to the nukes claim in 1800 was incorrect, even if that perspective doesn’t know that the Sun is not just a very big conventional fire. I’m not sure why this is. According to me this is just the natural thing to do because things are usually continuous and so in the absence of detailed knowledge that’s what your prior should be. (If I had to justify this, I’d point to facts about bridges and buildings and materials science and so on.)

there might be facts that justify conclusions like ‘AGI will be preceded by a long chain of slightly-less-general, slightly-less-capable successors’.

The frame of “justify[ing] conclusions” seems to ask for more confidence than I expect to get. Rather I feel like I’m setting an initial prior that could then be changed radically by engaging with details of the technology. And then I’m further saying that I don’t see any particular details that should cause me to update away significantly (but they could arise in the future).

For example, suppose I have a random sentence generator, and I take the first well-formed claim it spits out. (I’m using a random sentence generator so that we don’t update on the process by which the claim was generated.) This claim turns out to be “Alice has a fake skeleton hidden inside her home”. Let’s say we know nothing about Alice except that she is a real person somewhere in the US who has a home. You can still assign < 10% probability to the claim, and take 10:1 bets with people who don’t know any additional details about Alice. Nonetheless, as you learn more about Alice, you could update towards higher probability, e.g. if you learn that she loves Halloween, that’s a modest update; if you learn she runs a haunted house at Halloween every year, that’s a large update; if you go to her house and see the fake skeleton you can update to ~100%. That’s the sort of situation I feel like we’re in with AI.

If you asked me what facts justify the conclusion that Alice probably doesn’t have a fake skeleton hidden inside her house, I could only point to reference classes, and all the other people I’ve met who don’t have such skeletons. This is not engaging with the details of Alice’s situation, and I could similarly say “if I wanted to know about Alice, surely I should spend most of my time learning about Alice, rather than looking at what Bob and Carol did”. Nonetheless, it is still correct to assign < 10% to the claim.

It really does seem to come down to — why is human-level intelligence such a special turning point that should receive special treatment? Just as you wouldn’t give special treatment to “the first time bridges were longer than 10m”, it doesn’t seem obvious that there’s anything all that special at the point where AIs reach human-level intelligence (at least for the topics we’re discussing; there are obvious reasons that’s an important point when talking about the economic impact of AI)

FWIW, my current 1-paragraph compression of the debate positions is something like:

catastrophists: when evolution was gradually improving hominid brains, suddenly something clicked – it stumbled upon the core of general reasoning – and hominids went from banana classifiers to spaceship builders. hence we should expect a similar (but much sharper, given the process speeds) discontinuity with AI.

gradualists: no, there was no discontinuity with hominids per se; human brains merely reached a threshold that enabled cultural accumulation (and in a meaningul sense it was culture that built those spaceships). similarly, we should not expect sudden discontinuities with AI per se, just an accelerating (and possibly unfavorable to humans) cultural changes as human contributions will be automated away.

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one possible crux to explore is “how thick is culture”: is it something that AGI will quickly decouple from (dropping directly to physics-based ontology instead) OR will culture remain AGI’s main environment/ontology for at least a decade.

FWIW, my current 1-paragraph compression of the debate positions is something like:

catastrophists: when evolution was gradually improving hominid brains, suddenly something clicked – it stumbled upon the core of general reasoning – and hominids went from banana classifiers to spaceship builders. hence we should expect a similar (but much sharper, given the process speeds) discontinuity with AI.

gradualists: no, there was no discontinuity with hominids per se; human brains merely reached a threshold that enabled cultural accumulation (and in a meaningul sense it was culture that built those spaceships). similarly, we should not expect sudden discontinuities with AI per se, just an accelerating (and possibly unfavorable to humans) cultural changes as human contributions will be automated away.

—

one possible crux to explore is “how thick is culture”: is it something that AGI will quickly decouple from (dropping directly to physics-based ontology instead) OR will culture remain AGI’s main environment/ontology for at least a decade.

Clarification: in the sentence “just an accelerating (and possibly unfavorable to humans) cultural changes as human contributions will be automated away”, what work is “cultural changes” doing? Could we just say “changes” (including economic, cultural, etc) instead?

In my own head, the way I think of ‘AGI’ is basically: “Something happened that allows humans to do biochemistry, materials science, particle physics, etc., even though none of those things were present in our environment of evolutionary adaptedness. Eventually, AI will similarly be able to generalize to biochemistry, materials science, particle physics, etc. We can call that kind of AI ‘AGI’.”

There might be facts I’m unaware of that justify conclusions like ‘AGI is mostly just a bigger version of current ML systems like GPT-3’, and there might be facts that justify conclusions like ‘AGI will be preceded by a long chain of predecessors, each slightly less general and slightly less capable than its successor’.

But if so, I’m assuming those will be facts about CS, human cognition, etc., not at all a list of a hundred facts like ‘the first steam engine didn’t take over the world’, ‘the first telescope didn’t take over the world’…. Because the physics of brains doesn’t care about those things, and because in discussing brains we’re already in ‘things that have been known to take over the world’ territory.

(I think that paying much attention at all to the technology-wide base rate for ‘does this allow you to take over the world?’, once you already know you’re doing something like ‘inventing a new human’, doesn’t really make sense at all? It sounds to me like going to a bookstore and then repeatedly worrying ‘What if they don’t have the book I’m looking for? Most stores don’t sell books at all, so this one might not have the one I want.’ If you know it’s a book store, then you shouldn’t be thinking at that level of generality at all; the base rate just goes out the window.)

I’m broadly sympathetic to the idea that claims about AI cognition should be weighted more highly than claims about historical examples. But I think you’re underrating historical examples. There are at least three ways those examples can be informative – by telling us about:

1. Domain similarities

2. Human effort and insight

3. Human predictive biases

You’re mainly arguing against 1, by saying that there are facts about physics, and facts about intelligence, and they’re not very related to each other. This argument is fairly compelling to me (although it still seems plausible that there are deep similarities which we don’t understand yet – e.g. the laws of statistics, which apply to many different domains).

But historical examples can also tell us about #2 – for instance, by giving evidence that great leaps of insight are rare, and so if there exists a path to AGI which doesn’t require great leaps of insight, that path is more likely than one which does.

And they can also tell us about #3 – for instance, by giving evidence that we usually overestimate the differences between old and new technologies, and so therefore those same biases might be relevant to our expectations about AGI.

In the ‘alien warns about nukes’ example, my intuition is that ‘great leaps of insight are rare’ and ‘a random person is likely to overestimate the importance of the first steam engines and telescopes’ tell me practically nothing, compared to what even a small amount of high-uncertainty physics reasoning tells me.

The ‘great leap of insight’ part tells me ~nothing because even if there’s an easy low-insight path to nukes and a hard high-insight path, I don’t thereby know the explosive yield of a bomb on either path (either absolutely or relatively); it depends on how nukes work.

Likewise, I don’t think ‘a random person is likely to overestimate the first steam engine’ really helps with estimating the power of nuclear explosions. I could imagine a world where this bias exists and is so powerful and inescapable it ends up being a big weight on the scales, but I don’t think we live in that world?

I’m not even sure that a random person would overestimate the importance of prototypes in general. Probably, I guess? But my intuition is still that you’re better off in 1800 focusing on physics calculations rather than the tug-of-war ‘maybe X is cognitively biasing me in this way, no wait maybe Y is cognitively biasing me in this other way, no wait…’

Our situation might not be analogous to the 1800-nukes scenario (e.g., maybe we know by observation that current ML systems are basically scaled-down humans). But if it is analogous, then I think the history-of-technology argument is not very useful here.

re “cultural changes”: yeah, sorry, i meant “culture” in very general “substrate of human society” sense. “cultural changes” would then include things like changes in power structures and division of labour, but not things like “diamondoid bacteria killing all humans in 1 second” (that would be a change in humans, not in the culture)

I want to note that I agree with your (Rob’s) latest response, but I continue to think most of the action is in whether AGI involves something analogous to “totally new physics”, where I would guess “no” (and would do so particularly strongly for shorter timelines).

(And I would still point to historical examples for “many new technologies don’t involve something analogous to ‘totally new physics'”, and I’ll note that Richard’s #2 about human effort and insight still applies)

It feels to me like a common pattern is: I say that ML in particular, and most technologies in general, seem to improve quite gradually on metrics that people care about or track. You say that some kind of “gradualism” worldview predicts a bunch of other stuff (some claim about markets or about steve jobs or whatever that feels closely related on your view but not mine). But it feels to me like there are just a ton of technologies, and a ton of AI benchmarks, and those are just much more analogous to “future AI progress.” I know that to you this feels like reference class tennis, but I think I legitimately don’t understand what kind of approach to forecasting you are using that lets you just make (what I see as) the obvious boring prediction about all of the non-AGI technologies.

Perhaps you are saying that symmetrically you don’t understand what approach to forecasting I’m using, that would lead me to predict that technologies improve gradually yet people vary greatly in their abilities. To me it feels like the simplest thing in the world: I expect future technological progress in domain X to be like past progress in domain X, and future technological progress to be like past technological progress, and future market moves to be like past market moves, and future elections to be like past elections.

And it seems like you must be doing something that ends up making almost the same predictions as that almost all the time, which is why you don’t get incredibly surprised every single year by continuing boring and unsurprising progress in batteries or solar panels or robots or ML or computers or microscopes or whatever. Like it’s fine if you say “Yes, those areas have trend breaks sometimes” but there are so many boring years that you must somehow be doing something like having the baseline “this year is probably going to be boring.”

Such that intuitively it feels to me like the disagreement between us must be in the part where AGI feels to me like it is similar to AI-to-date and feels to you like it is very different and better compared to evolution of life or humans.

It has to be the kind of argument that you can make about progress-of-AI-on-metrics-people-care-about, but not progress-of-other-technologies-on-metrics-people-care-about, otherwise it seems like you are getting hammered every boring year for every boring technology.

I’m glad we have the disagreement on record where I expect ML progress to continue to get less jumpy as the field grows, and maybe the thing to do is just poke more at that since it is definitely a place where I gut level expect to win bayes points and so could legitimately change my mind on the “which kinds of epistemic practices work better?” question. But it feels like it’s not the main action, the main action has got to be about you thinking that there is a really impactful change somewhere between {modern AI, lower animals} and {AGI, humans} that doesn’t look like ongoing progress in AI.

I think “would GPT-3 + 5 person-years of engineering effort foom?” feels closer to core to me.

(That said, the way AI could be different need not feel like “progress is lumpier,” could totally be more like “Progress is always kind of lumpy, which Paul calls ‘pretty smooth’ and Eliezer calls ‘pretty lumpy’ and doesn’t lead to any disagreements; but Eliezer thinks AGI is different in that kind-of-lumpy progress leads to fast takeoff, while Paul thinks it just leads to kind-of-lumpy increases in the metrics people care about or track.”)

I think GPT-3 will foom given very little engineering effort, it will just be much slower than the human foom

and then that timeline will get faster and faster over time

it’s also fair to say that it wouldn’t foom because the computers would break before it figured out how to repair them (and it would run out of metal before it figured out how to mine it, etc.), depending on exactly how you define “foom,” but the point is that “you can repair the computers faster than they break” happens much before you can outrun human civilization

so the relevant threshold you cross is the one where you are outrunning civilization

(and my best guess about human evolution is pretty similar, it looks like humans are smart enough to foom over a few hundred thousand years, and that we were the ones to foom because that is also roughly how long it was taking evolution to meaningfully improve our cognition—if we foomed slower it would have instead been a smarter successor who overtook us, if we foomed faster it would have instead been a dumber predecessor, though this is much less of a sure-thing than the AI case because natural selection is not trying to make something that fooms)

and regarding {natural selection, humans, AGI} the main question is why modern AI and homo erectus (or even chimps) aren’t in the set

it feels like the core disagreement is that I mostly see a difference in degree between the various animals, and between modern AI and future AI, a difference that is likely to be covered by gradual improvements that are pretty analogous to contemporary improvements, and so as the AI community making contemporary improvements grows I get more and more confident that TAI will be a giant industry rather than an innovation

Do you have a source on Jobs having looked hard for a successor who wasn’t Tim Cook?

Also, I don’t have strong opinions about how well Apple is doing now, so I default to looking at the share price, which seems very healthy.

(Although I note in advance that this doesn’t feel like a particularly important point, roughly for the same reason that Paul mentioned: gradualism about Steve Jobs doesn’t seem like a central example of the type of gradualism that informs beliefs about AI development.)

I think the way to apply it is to use observable data (drawn widely) and math.

Steve Jobs does look like a (high) draw (selected for its height, in the sparsest tail of the CEO distribution) out of the economic and psychometric literature (using the same kind of approach I use in other areas like estimating effects of introducing slightly superhuman abilities on science, the genetics of height, or wealth distributions). You have roughly normal or log-normal distributions on some measures of ability (with fatter tails when there are some big factors present, e.g. super-tall people are enriched for normal common variants for height but are more frequent than a Gaussian estimated from the middle range because of some weird disease/hormonal large effects). And we have lots of empirical data about the thickness and gaps there. Then you have a couple effects that can make returns in wealth/output created larger.

You get amplification from winner-take-all markets, IT, and scale that let higher ability add value to more places. This is the same effect that lets top modern musicians make so much money. Better CEOs get allocated to bigger companies because multiplicative management decisions are worth more in big companies. Software engineering becomes more valuable as the market for software grows.

Wealth effects are amplified by multiplicative growth (noise in a given period multiplies wealth for the rest of the series, and systematic biases from abilities can grow exponentially or superexponentially over a lifetime), and there are some versions of that in gaining expensive-to-acquire human capital (like fame for Hollywood actors, or experience using incredibly expensive machinery or companies).

And we can read off the distributions of income, wealth, market share, lead time in innovations, scientometrics, etc.

That sort of data lead you to expect cutting edge tech to be months to a few years ahead of followers, winner-take-all tech markets to a few leading firms and often a clearly dominant one (but not driving an expectation of being able to safely rest on laurels for years while others innovate without a moat like network effects). That’s one of my longstanding arguments with Robin Hanson, that his model has more even capabilities and market share for AGI/WBE than typically observed (he says that AGI software will have to be more diverse requiring more specialized companies, to contribute so much GDP).

It is tough to sample for extreme values on multiple traits at once, superexponentially tough as you go out or have more criteria. CEOs of big companies are smarter than average, taller than average, have better social skills on average, but you can’t find people who are near the top on several of those.

https://www.hbs.edu/ris/Publication%20Files/16-044_9c05278e-9d11-4315-a744-de008edf4d80.pdf

Correlations between the things help, but it’s tough. E.g. if you have thousands of people in a class on a measure of cognitive skill, and you select on only partially correlated matters of personality, interest, motivation, prior experience, etc, the math says it gets thin and you’ll find different combos (and today we see more representation of different profiles of abilities, including rare and valuable ones, in this community)

I think the bigger update for me from trying to expand high-quality save the world efforts has been on the funny personality traits/habits of mind that need to be selected and their scarcity.

A cpl comments, without commitment to respond to responses:

1. Something in the zone of “context / experience / obsession” seems important for explaining the Steve Jobs type thing. It seems to me that people who enter an area early tend to maintain an edge even over more talented people who enter later – examples are not just founder/CEO types but also early employees of some companies who are more experienced with higher-level stuff (and often know the history of how they got there) better than later-entering people.

2. I’m not sure if I am just rephrasing something Carl or Paul has said, but something that bugs me a lot about the Rob/Eliezer arguments is that I feel like if I accept >5% probability for the kind of jump they’re talking about, I don’t have a great understanding of how I avoid giving >5% to a kajillion other claims from various startups that they’re about to revolutionize their industry, in ways that seem inside-view plausible and seem to equally “depend on facts about some physical domain rather than facts about reference classes.”

The thing that actually most comes to mind here is Thiel – he has been a phenomenal investor financially, but he has also invested by now in a lot of “atoms” startups with big stories about what they might do, and I don’t think any have come close to reaching those visions (though they have sometimes made $ by doing something orders of magnitude less exciting).

If a big crux here is “whether Thielian secrets exist” this track record could be significant.

I think I might update if I had a cleaner sense of how I could take on this kind of “Well, if it is just a fact about physics that I have no idea about, it can’t be that unlikely” view without then betting on a lot of other inside-view-plausible breakthroughs that haven’t happened. Right now all I can say to imitate this lens is “General intelligence is ‘different'”

I don’t feel the same way about “AI might take over the world” – I feel like I have good reasons this applies to AI and not a bunch of other stuff

Ok, a few notes from me (feel free to ignore):

1. It seems to me like the convo here is half attempting-to-crux and half attempting-to-distill-out-a-bet. I’m interested in focusing explicitly on cruxing for the time being, for whatever that’s worth. (It seems to me like y’all’re already trending in that direction.)

2. It seems to me that one big revealed difference between the Eliezerverse and the Paulverse is something like:

• In the Paulverse, we already have basically all the fundamental insights we need for AGI, and now it’s just a matter of painstaking scaling.
• In the Eliezerverse, there are large insights yet missing (and once they’re found we have plenty of reason to expect things to go quickly).

For instance, in Eliezerverse they say “The Wright flyer didn’t need to have historical precedents, it was allowed to just start flying. Similarly, the AI systems of tomorrow are allowed to just start GIing without historical precedent.”, and in the Paulverse they say “The analog of the Wright flyer has already happened, it was Alexnet, we are now in the phase analogous to the slow grinding transition from human flight to commercially viable human flight.”

(This seems to me like basically what Ajeya articulated upthread.)

3. It seems to me that another revealed intuition-difference is in the difficulty that people have operating each other’s models. This is evidenced by, eg, Eliezer/Rob saying things like “I don’t know how to operate the gradualness model without making a bunch of bad predictions about Steve Jobs”, and Paul/Holden responding with things like “I don’t know how to operate the secrets-exist model without making a bunch of bad predictions about material startups”.

I’m not sure whether this is a shallower or deeper disagreement than (2). I’d be interested in further attempts to dig into the questions of how to operate the models, in hopes that the disagreement looks interestingly different once both parties can at least operate the other model.