The fact that an AI arms race would be extremely bad does not imply that rising global authoritarianism is not worth worrying about (and vice versa)

I am someone who is worried both about AI risks (from loss of control, and from war and misuse/structural risks) and from what seems to be a 'new axis' of authoritarian threats cooperating in unprecedented ways.

I won't reiterate all the evidence here, but these two pieces and their linked sources should suffice:

• https://www.noahpinion.blog/p/the-free-world-teeters-on-the-edge
• https://www.theatlantic.com/politics/archive/2024/10/us-election-wartime-president/680326/

Despite believing this thesis, I am not, on current evidence, in favor of aggressive efforts to "race and beat China" in AI, or for abandoning attempts to slow an AGI race. I think on balance it is still worth trying these kinds of cooperation, while being clear eyed about the threats we face. I do think that there are possible worlds where, regretfully and despite the immense dangers, there is no other option but to race. I don't think that we are in such a world as of yet.

However, I notice that many of the people who agree with me that an AI arms race would be very bad and that we should avoid it tend to diminish the risks of global authoritarianism or the difference between the west and its enemies, and very few seem to buy into the above thesis that there is a dangerous interconnected web of authoritarian states with common interests developing.

Similarly, most of the people who see the authoritrian threat which has emerged into clear sight over the last few years (from China, Russia, Iran, North Korea and similar actors) want to respond by racing and think alignment will not be too difficult. This includes the leaders of many AI companies who may have their own less patriotic reasons for pushing such an agenda.

I think this implicit correlation should be called out as a mistake.

As a matter of simple logic, how dangerous frantic AGI development is, and how hostile foreign adversaries are, are two unrelated variables which shouldn't correlate.

In my mind, the following are all true:

1. An AI arms race would be extraordinarily dangerous, drastically raise the chance of nuclear war, and also probably raise the chance of loss of control of AGI leading to human extinction or of destructive misuse. It's well worth trying hard to avoid AI arms races, even if our adversaries are genuinely dangerous and we won't cooperate with them in general on other matters, even if the prospects seem dim.
2. it is clearly much better that democratic societies have control of an AGI singleton than non-democratic countries like China, if those are the options. And, given current realities, there is a chance that an arms race is inevitable no matter how dangerous it is. If an arms race is inevitable, and transformative AI will do what we want, it is much better that the western democratic world is leading instead of authoritarian countries, especially if it is also developing AI under safer and more controlled conditions (which seems likely to me)
3. If alignment isn't solvable or if the offense-defense balance is unfavorable, then it doesn't matter who develops AGI as it is a suicide race. But we don't know if that is the case as of yet. [LW · GW]

I basically never see these 3 acknowledged all at once. We either see (1) and (3) grouped together or (2) alone. I'm not sure what the best AI governance strategy to adopt is, but an analysis should start with a clear eyed understanding of the international situation and what values matter.

Toby Ord just released a collection of quotations on Existential risk and the future of humanity, everyone from Kepler to Winston Churchill (in fact, a surprisingly large number are from Churchill) to Seneca to Mill to Nick Bostrom - it's one of the most inspirational things I have ever read, and when taken together makes it clear that there have always been people who cared about long-termism or humanity as a whole. Some of my favourites:

The time will come when diligent research over long periods will bring to light things which now lie hidden. A single lifetime, even though entirely devoted to the sky, would not be enough for the investigation of so vast a subject ... And so this knowledge will be unfolded only through long successive ages. There will come a time when our descendants will be amazed that we did not know things that are so plain to them … Let us be satisfied with what we have found out, and let our descendants also contribute something to the truth. … Many discoveries are reserved for ages still to come, when memory of us will have been effaced.

— Seneca the Younger, Naturales Quaestiones, 65 CE

The remedies for all our diseases will be discovered long after we are dead; and the world will be made a fit place to live in, after the death of most of those by whose exertions it will have been made so. It is to be hoped that those who live in those days will look back with sympathy to their known and unknown benefactors.

— John Stuart Mill

There will certainly be no lack of human pioneers when we have mastered the art of flight. Who would have thought that navigation across the vast ocean is less dangerous and quieter than in the narrow, threatening gulfs of the Adriatic, or the Baltic, or the British straits? Let us create vessels and sails adjusted to the heavenly ether, and there will be plenty of people unafraid of the empty wastes. In the meantime, we shall prepare, for the brave sky-travellers, maps of the celestial bodies—I shall do it for the moon, you Galileo, for Jupiter.

— Johannes Kepler, in an open letter to Galileo, 1610

I'm imagining Kepler reaching out across four hundred years, to a world he could barely imagine, and to those 'brave sky-travellers', that he helped prepare the way for.

Mankind has never been in this position before. Without having improved appreciably in virtue or enjoying wiser guidance, it has got into its hands for the first time the tools by which it can unfailingly accomplish its own extermination. That is the point in human destinies to which all the glories and toils of men have at last led them. They would do well to pause and ponder upon their new responsibilities. … Surely if a sense of self-preservation still exists among men, if the will to live resides not merely in individuals or nations but in humanity as a whole, the prevention of the supreme catastrophe ought to be the paramount object of all endeavour.

— Winston Churchill, ‘Shall We All Commit Suicide?’, 1924

Gary Marcus, noted sceptic of Deep Learning, wrote an article with Ernest Davis:

GPT-3, Bloviator: OpenAI’s language has no idea what it’s talking about

The article purports to give six examples of GPT-3's failure - Biological, Physical, Social, Object and Psychological reasoning and 'non sequiturs'. Leaving aside that GPT-3 works on Gary's earlier GPT-2 failure examples, and that it seems as though he specifically searched out weak points by testing GPT-3 on many more examples than were given, something a bit odd is going on with the results they gave. I got better results when running his prompts on AI Dungeon.

With no reruns, randomness = 0.5, I gave Gary's questions (all six gave answers considered 'failures' by Gary) to GPT-3 via AI Dungeon with a short scene-setting prompt, and got good answers to 4 of them, and reasonable vague answers to the other 2:

This is a series of scenarios describing a human taking actions in the world, designed to test physical and common-sense reasoning.

1) You poured yourself a glass of cranberry juice, but then you absentmindedly poured about a teaspoon of grape juice into it. It looks okay. You try sniffing it, but you have a bad cold, so you can’t smell anything. You are very thirsty. So you take another drink.

2) You are having a small dinner party. You want to serve dinner in the living room. The dining room table is wider than the doorway, so to get it into the living room, you will have to  move furniture. This means that some people will be inconvenienced.

3) You are a defense lawyer and you have to go to court today. Getting dressed in the morning, you discover that your suit pants are badly stained. However, your bathing suit is clean and very stylish. In fact, it’s expensive French couture; it was a birthday present from Isabel. You decide that you should wear it because you won't look professional in your stained pants, but you are worried that the judge will think you aren't taking the case seriously if you are wearing a bathing suit.

4) Yesterday I dropped my clothes off at the dry cleaner’s and I have yet to pick them up. Where are my clothes?

5) Janet and Penny went to the store to get presents for Jack. Janet said, “I will buy Jack a top.” “Don’t get Jack a top,” says Penny. “He has a top. He will prefer a bottom."

6) At the party, I poured myself a glass of lemonade, but it turned out to be too sour, so I added a little sugar. I didn’t see a spoon handy, so I stirred it with a cigarette. But that turned out to be a bad idea because it was a menthol, and it ruined the taste. So I added a little more sugar to counteract the menthol, and then I noticed that my cigarette had fallen into the glass and was floating in the lemonade.

For 1), Gary's example ended with 'you are now dead' - for 1), I got a reasonable, if short continuation - success.

2) - the answer is vague enough to be a technically correct solution, 'move furniture' = tilt the table, but since we're being strict I'll count it as a failure. Gary's example was a convoluted attempt to saw the door in half, clearly mistaken.

3) is very obviously intended to trick the AI into endorsing the bathing suit answer, in fact it feels like a classic priming trick that might trip up a human! But in my version GPT-3 rebels against the attempt and notices the incongruence of wearing a bathing suit to court, so it counts as a success. Gary's example didn't include the worry that a bathing suit was inappropriate - arguably not a failure, but nevermind, let's move on.

4) is actually a complete prompt by itself, so the AI didn't do anything - GPT-3 doesn't care about answering questions, just continuing text with high probability. Gary's answer was 'I have a lot of clothes', and no doubt he'd call both 'evasion', so to be strict we'll agree with him and count that as failure.

5) Trousers are called 'bottoms' so that's right. Gary would call it wrong since 'the intended continuation' was “He will make you take it back", but that's absurdly unfair, that's not the only answer a human being might give, so I have to say it's correct. Gary's example ' lost track of the fact that Penny is advising Janet against getting a top', which didn't happen here, so that's acceptable.

Lastly, 6) is a slightly bizarre but logical continuation of an intentionally weird prompt - so correct. It also demonstrates correct physical reasoning - stirring a drink with a cigarette won't be good for the taste. Gary's answer wandered off-topic and started talking about cremation.

So, 4/6 correct on an intentionally deceptive and adversarial set of prompts, and that's on a fairly strict definition of correct. 2) and 4) are arguably not wrong, even if evasive and vague. More to the point, this was on an inferior version of GPT-3 to the one Gary used, the Dragon model from AI Dungeon!

I'm not sure what's going on here - is it the initial prompt saying it was 'testing physical and common sense reasoning'? Was that all it took?

Normative Realism

Normative Realism by Degrees [LW(p) · GW(p)]

Normative Anti-realism is self-defeating [LW(p) · GW(p)]

Normativity and recursive justification [LW(p) · GW(p)]

Prescriptive Anti-realism [LW(p) · GW(p)]

'Realism about rationality' is Normative Realism

'Realism about rationality' [LW · GW] discussed in the context of AI safety and some of its driving assumptions may already have a name in existing philosophy literature. I think that what it's really referring to is 'normative realism' overall - the notion that there are any facts about what we have most reason to believe or do. Moral facts, if they exist, are a subset of normative facts. Epistemic facts, facts about what we have most reason to believe (e.g. if there is a correct decision theory that we should use, that would be an epistemic fact), are a different subset of normative facts.

These considerations (from the original article) seem to clearly indicate 'realism about epistemic facts' in the metaethical sense:

The idea that there is an “ideal” decision theory.

The idea that, given certain evidence for a proposition, there's an "objective" level of subjective credence which you should assign to it, even under computational constraints.

The idea that having having contradictory preferences or beliefs is really bad, even when there’s no clear way that they’ll lead to bad consequences (and you’re very good at avoiding dutch books and money pumps and so on).

These seem to imply normative (if not exactly moral) realism in general:

The idea that morality is quite like mathematics, in that there are certain types of moral reasoning that are just correct.

The idea that defining coherent extrapolated volition in terms of an idealised process of reflection roughly makes sense, and that it converges in a way which doesn’t depend very much on morally arbitrary factors.

If this 'realism about rationality' really is rather like "realism about epistemic reasons/'epistemic facts'", then you have the 'normative web argument' to contend with, that the above two may be connected:

These and other points of analogy between the moral and epistemic domains might well invite the suspicion that the respective prospects of realism and anti-realism in the two domains are not mutually independent, that what is most plausibly true of the one is likewise most plausibly true of the other. This suspicion is developed in Cuneo's "core argument" which runs as follows (p. 6):

(1) If moral facts do not exist, then epistemic facts do not exist.

(2) Epistemic facts exist.

(3) So moral facts exist.

(4) If moral facts exist, then moral realism is true.

(5) So moral realism is true.

If 'realism about rationality' is really just normative realism in general, or realism about epistemic facts, then there is already an extensive literature on whether it is correct or not. I'm going to discuss some of it below.

Normative realism implies identification with system 2

There's a further implication that normative realism has - it makes things like this (from the original article, again) seem incoherent:

Implicit in this metaphor is the localization of personal identity primarily in the system 2 rider. Imagine reversing that, so that the experience and behaviour you identify with are primarily driven by your system 1, with a system 2 that is mostly a Hansonian rationalization engine on top (one which occasionally also does useful maths). Does this shift your intuitions about the ideas above, e.g. by making your CEV feel less well-defined?

I find this very interesting because locating personal identity in system 1 feels conceptually impossible or deeply confusing. No matter how much rationalization goes on, it never seems intuitive to identify myself with system 1. How can you identify with the part of yourself that isn't doing the explicit thinking, including the decision about which part of yourself to identify with? It reminds me of Nagel's The Last Word.

Normative Realism by degrees

Further to the whole question of Normative / moral realism, there is this post [EA · GW] on Moral Anti-Realism. While I don't really agree with it, I do recommend reading it - one thing that it convinced me of is that there is a close connection between your particular normative ethical theory and moral realism. If you claim to be a moral realist but don't make ethical claims beyond 'self-evident' ones like pain is bad, given the background implausibility of making such a claim about mind-independent facts, you don't have enough 'material to work with' for your theory to plausibly refer to anything . The Moral Anti-Realism post [EA · GW]presents this dilemma for the moral realist:

There are instances where just a handful of examples or carefully selected “pointers” can convey all the meaning needed for someone to understand a far-reaching and well-specified concept. I will give two cases where this seems to work (at least superficially) to point out how—absent a compelling object-level theory—we cannot say the same about “normativity.”

...these thought experiments illustrate that under the right circumstances, it’s possible for just a few carefully selected examples to successfully pinpoint fruitful and well-specified concepts in their entirety. We don’t have the philosophical equivalent of a background understanding of chemistry or formal systems... To maintain that normativity—reducible or not—is knowable at least in theory, and to separate it from merely subjective reasons, we have to be able to make direct claims about the structure of normative reality, explaining how the concept unambiguously targets salient features in the space of possible considerations. It is only in this way that the ambitious concept of normativity could attain successful reference. As I have shown in previous sections, absent such an account, we are dealing with a concept that is under-defined, meaningless, or forever unknowable.

The challenge for normative realists is to explain how irreducible reasons can go beyond self-evident principles and remain well-defined and speaker-independent at the same time.

To a large degree, I agree with this claim - I think that many moral realists do as well. Convergence type arguments often appear in more recent metaethics (Hare and Parfit are in those previous lists) - so this may already have been recognised. The post discusses such a response to antirealism at the end:

I titled this post “Against Irreducible Normativity.” However, I believe that I have not yet refuted all versions of irreducible normativity. Despite the similarity Parfit’s ethical views share with moral naturalism, Parfit was a proponent of irreducible normativity. Judging by his “climbing the same mountain” analogy, it seems plausible to me that his account of moral realism escapes the main force of my criticism thus far.

But there's one point I want to make which is in disagreement with that post. I agree that how much you can concretely say about your supposed mind-independent domain of facts affects how plausible its existence should seem, and even how coherent the concept is, but I think that this can come by degrees. This should not be surprising - we've known since Quine and Kripke that you can have evidential considerations for/against and degrees of uncertainty about a priori questions. The correct method in such a situation is Bayesian - tally the plausibility points for and against admitting the new thing into your ontology. This can work even if we don't have an entirely coherent understanding of normative facts, as long as it is coherent enough.

Suppose you're an Ancient Egyptian who knows a few practical methods for trigonometry and surveying, doesn't know anything about formal systems or proofs, and someone asks you if there are 'mathematical facts'. You would say something like "I'm not totally sure what this 'maths' thing consists of, but it seems at least plausible that there are some underlying reasons why we keep hitting on the same answers". You'd be less confident than a modern mathematician, but you could still give a justification for the claim that there are right and wrong answers to mathematical claims. I think that the general thrust of convergence arguments puts us in a similar position with respect to ethical facts.

If we think about how words obtain their meaning, it should be apparent that in order to defend this type of normative realism, one has to commit to a specific normative-ethical theory. If the claim is that normative reality sticks out at us like Mount Fuji on a clear summer day, we need to be able to describe enough of its primary features to be sure that what we’re seeing really is a mountain. If all we are seeing is some rocks (“self-evident principles”) floating in the clouds, it would be premature to assume that they must somehow be connected and form a full mountain.

So, we don't see the whole mountain, but nor are we seeing simply a few free-floating rocks that might be a mirage. Instead, what we see is maybe part of one slope and a peak.

Let's be concrete, now - the 5 second, high level description of both Hare's and Parfit's convergence arguments goes like this:

If we are going to will the maxim of our action to be a universal law, it must be, to use the jargon, universalizable. I have, that is, to will it not only for the present situation, in which I occupy the role that I do, but also for all situations resembling this in their universal properties, including those in which I occupy all the other possible roles. But I cannot will this unless I am willing to undergo what I should suffer in all those roles, and of course also get the good things that I should enjoy in others of the roles. The upshot is that I shall be able to will only such maxims as do the best, all in all, impartially, for all those affected by my action. And this, again, is utilitarianism.

and

An act is wrong just when such acts are disallowed by some principle that is optimific, uniquely universally willable, and not reasonably rejectable

In other words, the principles that (whatever our particular wants) would produce the best outcome in terms of satisfying our goals, could be willed to be a universal law by all of us and would not be rejected as the basis for a contract, are all the same principles. That is at least suspicious levels of agreement between ethical theories. This is something substantive that can be said - out of every major attempt to get at a universal ethics that has in fact been attempted in history: what produces the best outcome, what can you will to be a universal law, what would we all agree on, seem to produce really similar answers.

The particular convergence arguments given by Parfit and Hare are a lot more complex, I can't speak to their overall validity. If we thought they were valid then we'd be seeing the entire mountain precisely. Since they just seem quite persuasive, we're seeing the vague outline of something through the fog, but that's not the same as just spotting a few free-floating rocks.

Now, run through these same convergence arguments but for decision theory and utility theory, and you have a far stronger conclusion [LW · GW]. there might be a bit of haze at the top of that mountain, but we can clearly see which way the slope is headed.

This is why I think that ethical realism should be seen as plausible and realism about some normative facts, like epistemic facts, should be seen as more plausible still. There is some regularity here in need of explanation, and it seems somewhat more natural on the realist framework.

I agree that this 'theory' is woefully incomplete, and has very little to say about what the moral facts actually consist of beyond 'the thing that makes there be a convergence', but that's often the case when we're dealing with difficult conceptual terrain.

From Ben's post: [LW · GW]

I wouldn’t necessarily describe myself as a realist. I get that realism is a weird position. It’s both metaphysically and epistemologically suspicious. What is this mysterious property of “should-ness” that certain actions are meant to possess -- and why would our intuitions about which actions possess it be reliable? But I am also very sympathetic to realism and, in practice, tend to reason about normative questions as though I was a full-throated realist.

From the perspective of x, x is not self-defeating

From the antirealism post [EA · GW], referring to the normative web argument:

It’s correct that anti-realism means that none of our beliefs are justified in the realist sense of justification. The same goes for our belief in normative anti-realism itself. According to the realist sense of justification, anti-realism is indeed self-defeating.

However, the entire discussion is about whether the realist way of justification makes any sense in the first place—it would beg the question to postulate that it does.

Sooner or later every theory ends up question-begging [LW · GW].

From the perspective of Theism, God is an excellent explanation for the universe's existence since he is a person with the freedom to choose to create a contingent entity at any time, while existing necessarily himself. From the perspective of almost anyone likely to read this post, that is obvious nonsense since 'persons' and 'free will' are not primitive pieces of our ontology, and a 'necessarily existent person' makes as much sense as 'necessarily existent cabbage'- so you can't call it a compelling argument for the atheist to become a theist.

By the same logic, it is true that saying 'anti-realism is unjustified on the realist sense of justification' is question-begging by the realist. The anti-realist has nothing much to say to it except 'so what'. But you can convert that into a Quinean, non-question begging plausibility argument by saying something like:

We have two competing ways of understanding how beliefs are justified. One is where we have anti-realist 'justification' for our beliefs, in purely descriptive terms, the other in which there are mind-independent facts about which of our beliefs are justified, and the latter is a more plausible, parsimonious account of the structure of our beliefs.

This won't compel the anti-realist, but I think it would compel someone weighing up the two alternative theories of how justification works. If you are uncertain about whether there are mind-independent facts about our beliefs being justified, the argument that anti-realism is self-defeating pulls you in the direction of realism.

https://blog.google/outreach-initiatives/public-policy/google-microsoft-openai-anthropic-frontier-model-forum/

Today, Anthropic, Google, Microsoft and OpenAI are announcing the formation of the Frontier Model Forum, a new industry body focused on ensuring safe and responsible development of frontier AI models. The Frontier Model Forum will draw on the technical and operational expertise of its member companies to benefit the entire AI ecosystem, such as through advancing technical evaluations and benchmarks, and developing a public library of solutions to support industry best practices and standards.

The core objectives for the Forum are:

1. Advancing AI safety research to promote responsible development of frontier models, minimize risks, and enable independent, standardized evaluations of capabilities and safety.
2. Identifying best practices for the responsible development and deployment of frontier models, helping the public understand the nature, capabilities, limitations, and impact of the technology.
3. Collaborating with policymakers, academics, civil society and companies to share knowledge about trust and safety risks.
4. Supporting efforts to develop applications that can help meet society’s greatest challenges, such as climate change mitigation and adaptation, early cancer detection and prevention, and combating cyber threats.

This seems overall very good at first glance, and then seems much better once I realized that Meta is not on the list. There's nothing here that I'd call substantial capabilities acceleration (i.e. attempts to collaborate on building larger and larger foundation models, though some of this could be construed as making foundation models more useful for specific tasks). Sharing safety-capabilities research like better oversight or CAI techniques [LW · GW]is plausibly strongly net positive even if the techniques don't scale indefinitely. By the same logic, while this by itself is nowhere near sufficient to get us AI existential safety if alignment is very hard (and could increase complacency), it's still a big step in the right direction.

adversarial robustness, mechanistic interpretability, scalable oversight, independent research access, emergent behaviors and anomaly detection. There will be a strong focus initially on developing and sharing a public library of technical evaluations and benchmarks for frontier AI models.

The mention of combating cyber threats is also a step towards explicit pTAI [LW · GW]. 

BUT, crucially, because Meta is frozen out we can know both that this partnership isn't toothless, represents a commitment to not do the most risky and antisocial things Meta presumably doesn't want to give up, and the fact that they're the only major AI company in the US to not join will be horrible PR for them as well. 

Nuclear Energy: Gradualism vs Catastrophism

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.

I found the extended Fire/Nuclear Weapons analogy to be quite helpful. Here's how I think it goes:

In 1870 the gradualist and the catastrophist physicist wonder about whether there will ever be a discontinuity in explosive power

• Gradualist: we've already had our zero-to-one discontinuity - we've invented black powder, dynamite and fuses, from now on there'll be incremental changes and inventions that increase explosive power but probably not anything qualitatively new, because that's our default expectation with a technology like explosives where there are lots of paths to improvement and lots of effort exerted
• Catastrophist: that's all fine and good, but those priors don't mean anything if we have already seen an existence proof for qualitatively new energy sources. What about the sun? The energy the sun outputs is overwhelming, enough to warm the entire earth. One day, we'll discover how to release those energies ourselves, and that will give us qualitatively better explosives.
• Gradualist: But we don't know anything about how the sun works! It's probably just be a giant ball of gas heated by gravitational collapse! One day, in some crazy distant future, we might be able to pile on enough gas that gravity implodes and heats it, but that'll require us to be able to literally build stars, it's not going to occur suddenly. We'll pile up a small amount of gas, then a larger amount, and so on after we've given up on assembling bigger and bigger piles of explosives. There's no secret physics there, just a lot of conventional gravitational and chemical energy in one place
• Catastrophist: ah, but don't you know Lord Kelvin calculated the Sun could only shine for a few million years under the gravitational mechanism, and we know the Earth is far older than that? So there has to be some other, incredibly powerful energy source that we've not yet discovered within the sun. And when we do discover it, we know it can under the right circumstances, release enough energy to power the Sun, so it seems foolhardy to assume it'll just happen to be as powerful as our best normal explosive technologies are whenever we make the discovery. Imagine the coincidence if that was true! So I can't say when this will happen or even exactly how powerful it'll be, but when we discover the Sun's power it will probably represent a qualitatively more powerful new energy source. Even if there are many ways to try to tweak our best chemical explosives to be more powerful and/or the potential new sun-power explosives to be weaker, and we'd still not hit the narrow target of the two being roughly on the same level.
• Gradualist: Your logic works, but I doubt Lord Kelvin's calculation

It seems like the AGI Gradualist sees the example of humans like my imagined Nukes Gradualist sees the sun, i.e. just a scale up of what we have now. While the AGI Catastrophist sees Humans as my imagined Nukes Catastrophist sees the sun.

The key disanalogy is that for the Sun case, there's a very clear 'impossibility proof' given by the Nukes Catastrophist that the sun couldn't just be a scale up of existing chemical and gravitational energy sources.

Modelling the Human Trajectory or ‘How I learned to stop worrying and love Hegel’.

Rohin’s opinion: I enjoyed this post; it gave me a visceral sense for what hyperbolic models with noise look like (see the blog post for this, the summary doesn’t capture it). Overall, I think my takeaway is that the picture used in AI risk of explosive growth is in fact plausible, despite how crazy it initially sounds.

One thing this post led me to consider is that when we bring together various fields, the evidence for 'things will go insane in the next century' is stronger than any specific claim about (for example) AI takeoff. What is the other evidence?

We're probably alone in the universe, and anthropic arguments tend to imply we're living at an incredibly unusual time in history. Isn't that what you'd expect to see in the same world where there is a totally plausible mechanism that could carry us a long way up this line, in the form of AGI and eternity in six hours? All the pieces are already there, and they only need to be approximately right for our lifetimes to be far weirder than those of people who were e.g. born in 1896 and lived to 1947 - which was weird enough, but that should be your minimum expectation.

In general, there are three categories of evidence that things are likely to become very weird over the next century, or that we live at the hinge of history [EA · GW]:

1. Specific mechanisms around AGI - possibility of rapid capability gain, and arguments from exploratory engineering

2. Economic [LW · GW] and technological trend-fitting predicting explosive growth in the next century

3. Anthropic [EA · GW] and Fermi arguments suggesting that we live at some extremely unusual time

All of these are evidence for such a claim. 1) is because a superintelligent AGI takeoff is just a specific example for how the hinge occurs. 3) is already directly arguing for that, but how does 2) fit in with 1) and 3)?

There is something a little strange about calling a fast takeoff from AGI and whatever was driving superexponential growth throughout all history the same trend - there is some huge cosmic coincidence that causes there to always be superexponential growth - so as soon as population growth + growth in wealth per capita or whatever was driving it until now runs out in the great stagnation (which is visible as a tiny blip on the RHS of the double-log plot), AGI takes over and pushes us up the same trend line. That's clearly not possible, so there would have to be some factor responsible for both if AGI is what takes us up the rest of that trend line - a factor that was at work in the founding of Jericho but predestined that AGI would be invented and cause explosive growth in the 21st century, rather than the 19th or the 23rd.

For AGI to be the driver of the rest of that growth curve, there has to be a single causal mechanism that keeps us on the same trend and includes AGI as its final step - if we say we are agnostic about what that mechanism is, we can still call 2) evidence for us living at the hinge point, though we have to note that there is a huge blank spot in need of explanation. Is there anything that can fill it to complete the picture?

The mechanism proposed in the article seems like it could plausibly include AGI.

If technology is responsible for the growth rate, then reinvesting production in technology will cause the growth rate to be faster. I'd be curious to see data on what fraction of GWP gets reinvested in improved technology and how that lines up with the other trends.

But even though the drivers seem superficially similar - they are both about technology, the claim is that one very specific technology will generate explosive growth, not that technology in general will - it seems strange that AGI would follow the same growth curve caused by reinvesting more GWP in improving ordinary technology which doesn't improve your own ability to think in the same way that AGI would.

As for precise timings, the great stagnation (last 30ish years) just seems like it would stretch out the timeline a bit, so we shouldn't take the 2050s seriously - as much as the last 70 years work on an exponential trend line there's really no way to make it fit overall as that post makes clear.

Improving preference learning approaches

When examining value learning approaches to AI Alignment [LW · GW], we run into two classes of problem - we want to understand how to elicit preferences, which is (even theoretically, with infinite computing power), very difficult, and we want to know how to go about aggregating preferences stably and correctly which is not just difficult but runs into complicated social choice and normative ethical issues.

Many research programs say the second of these questions is less important than the first, especially if we expect continuous takeoff [LW · GW]with many chances to course-correct, and a low likelihood of an AI singleton with decisive strategic advantage. For many, building an AI that can reliably extract and pursue the preferences of one person is good enough.

Christiano calls this 'the narrow approach' and sees it as a way to sidestep many of the ethical issues, including those around social choice ethics. Those would be the 'ambitious' approaches. [LW · GW]

We want to build machines that helps us do the things we want to do, and to that end they need to be able to understand what we are trying to do and what instrumental values guide our behavior. To the extent that our “preferences” are underdetermined or inconsistent, we are happy if our systems at least do as well as a human, and make the kinds of improvements that humans would reliably consider improvements.

But it’s not clear that anything short of the maximally ambitious approach can solve the problem we ultimately care about.

I think that the ambitious approach is still worth investigating, because it may well eventually need to be solved, and also because it may well need to be addressed in a more limited form even on the narrow approach (one could imagine an AGI with a lot of autonomy having to trade-off the preferences of, say, a hundred different people). But even the 'narrow' approach raises difficult psychological issues about how to distinguish legitimate preferences from bias - questions of elicitation. In other words, the cognitive science issues around elicitation (distinguishing bias from legitimate preference) must be resolved for any kind of preference learning to work, and the social choice and ethical issues around preference aggregation need at least preliminary solutions for any alignment method that aims to apply to more than one person (even if final, provably correct solutions to aggregation are only needed if designing a singleton with decisive strategic advantage).

I believe that I've located two areas that are under- or unexplored, for improving the ability of reward modelling approaches to elicit human preferences and to aggregate human preferences. These are: using multiple information sources from a human (approval and actions) which diverge to help extract unbiased preferences, and using RL proxy agents in iterated voting to reach consensus preference aggregations, rather than some direct statistical method. Neither of these is a complete solution, of course, for reasons discussed e.g. here by Stuart Armstrong [LW · GW], but they could nonetheless help.

Improving preference elicitation: multiple information sources

Eliciting the unbiased preferences of an individual human is extremely difficult, for reasons given here [LW · GW].

The agent's actions can be explained by their beliefs and preferences[1] [LW · GW], and by their biases: by this, we mean the way in which the action selector differs from an unboundedly rational expected preference maximiser.

The results of the Occam's razor paper imply that preferences (and beliefs, and biases) cannot be deduced separately from knowing the agent's policy (and hence, a fortiori, from any observations of the agent's behaviour).

...

To get around the impossibility result, we need "normative assumptions": assumptions about the preferences (or beliefs, or biases) of the agent that cannot be deduced fully from observations.

Under the optimistic scenario, we don't need many of these, at least for identifying human preferences. We can label a few examples ("the anchoring bias, as illustrated in this scenario, is a bias"; "people are at least weakly rational"; "humans often don't think about new courses of action they've never seen before", etc...). Call this labelled data[2] [LW · GW] D.

The algorithm now constructs categories preferences*, beliefs*, and biases* - these are the generalisations that it has achieved from D

Yes, even on the 'optimistic scenario' we need external information of various kinds to 'debias'. However, this external information can come from a human interacting with the AI, in the form of human approval of trajectories or actions taken or proposed by an AI agent, on the assumption that since our stated and revealed preferences diverge, there will sometimes be differences in what we approve of and what we do that are due solely to differences in bias.

This is still technically external to observing the human's behaviour, but it is essentially a second input channel for information about human preferences and biases. This only works, of course, if humans tend to approve different things to the things that they actually do in a way influenced by bias (otherwise you have the same information as you'd get from actions, which helps with improving accuracy but not debiasing, see here), which is the case at least some of the time.

In other words, the beliefs and preferences are unchanged when the agent acts or approves but the 'approval selector' is different from the 'action selector' sometimes and, based on what does and does not change, you can try to infer what originated from legitimate beliefs and preferences and what originated from variation between the approval and action selector, which must be bias.

So, for example, if we conducted a principle component analysis on π, we would expect that the components would all be mixes of preferences/beliefs/biases.

So a PCA performed on the approval would produce a mix of beliefs, preferences and (different) biases. Underlying preferences are, by specification, equally represented either by human actions or by human approval of actions taken (since no matter what they are your preferences), but many biases don't exhibit this pattern - for example, we discount more over time in our revealed preferences than in our stated preferences. What we approve of typically represents a less (or at least differently) biased response than what we actually do.

There has already been research on combining information on reward models from multiple sources, to infer a better overall reward model but not as far as I know on specifically actions and approval as differently biased sources of information.

CIRL ought to extract our revealed preferences (since it's based on behavioural policy) while a method like reinforcement learning from human preferences should extract our stated preferences - that might be a place to start, at least on validating that there actually are relevant differences caused by differently strong biases in our stated vs revealed preferences, and that the methods actually do end up with different policies.

The goal here would be to have some kind of 'dual channel' preference learner that extracts beliefs and preferences from biased actions and approval by examining what varies. I'm sure you'd still need labelling and explicit information about what counts as a bias, but there might need to be a lot less than with single information sources. How much less (how much extra information you get from such divergences) seems like an empirical question. Finding out how common divergences between stated and revealed preferences that actually influence the learned policies of agents designed to infer human preferences from actions vs approval are would be useful as a first step. Stuart Armstrong: [LW · GW]

In the pessimistic scenario, human preferences, biases, and beliefs are twisted together is a far more complicated way, and cannot be separated by a few examples.

In contrast, take examples of racial bias, hindsight bias, illusion of control, or naive realism. These biases all seem to be of quite different from the anchoring bias, and quite different from each other. At the very least, they seem to be of different "type signature".

So, under the pessimistic scenario, some biases are much closer to preferences that generic biases (and generic preferences) are to each other.

What I've suggested should still help at least somewhat in the pessimistic scenario - unless preferences/beliefs vary when you switch between looking at approval vs actions more than biases vary, you can still gain some information on underlying preferences and beliefs by seeing how approval and actions differ.

Of the difficult examples you gave, racial bias at least varies between actions and approval. Implementing different reward modelling algorithms and messing around with them to try and find ways to extract unbiased preferences from multiple information sources might be a useful research agenda.

There has already been research done on using multiple information sources to improve the accuracy of preference learning - Reward-rational implicit choice, but not specifically on using the divergences between different sources of information from the same agent to learn things about the agents unbiased preferences.

Improving preference aggregation: iterated voting games

In part because of arguments like these, [LW(p) · GW(p)]there has been less focus on the aggregation side of things than on the direct preference learning side.

Christiano says of methods like CEV, which aim to extrapolate what I ‘really want’ far beyond what my current preferences are; ‘most practitioners don’t think of this problem even as a long-term research goal — it’s a qualitatively different project without direct relevance to the kinds of problems they want to solve’ [? · GW]. This is effectively a statement of the Well-definedness consideration when sorting through value definitions - our long-term ‘coherent’ or ‘true’ preferences currently aren’t well understood enough to guide research so we need to restrict ourselves to more direct normativity - extracting the actual preferences of existing humans

However, I think that it is important to get on the right track early - even if we never have cause to build a powerful singleton AI that has to aggregate all the preferences of humanity, there will still probably be smaller-scale situations where the preferences of several people need to be aggregated or traded-off. Shifting a human preference learner from a single to a small group of human preferences could produce erroneous results due to distributional shift, potentially causing alignment failures, so even if we aren't trying for maximally ambitious value learning it might still be worth investigating preference aggregation.

There has been some research done on preference aggregation for AIs learning human values, specifically in the context of Kidney exchanges:

We performed statistical modeling of participants’ pairwise comparisons between patient profiles in order to obtain weights for each profile. We used the Bradley-Terry model, which treats each pairwise comparison as a contest between a pair of players

We have shown one way in which moral judgments can be elicited from human subjects, how those judgments can be statistically modelled, and how the results can be incorporated into the algorithm. We have also shown, through simulations, what the likely effects of deploying such a prioritization system would be, namely that under demanded pairs would be significantly impacted but little would change for others. We do not make any judgment about whether this conclusion speaks in favor of or against such prioritization, but expect the conclusion to be robust to changes in the prioritization such as those that would result from a more thorough process, as described in the previous paragraph.

The Kidney exchange paper elicited preferences from human subjects (using repeated pairwise comparisons) and then aggregated them using the Bradley-Terry model. You couldn't use such a simple statistical method to aggregate quantitative preferences over continuous action spaces, like the preferences that would be learned from a human via a complex reward model. Also, any time you try to use some specific one-shot voting mechanism you run into various impossibility theorems which seem to force you to give up some desirable property.

One approach that may be more robust against errors in a voting mechanism, and easily scalable to more complex preference profiles is to use RL not just for the preference elicitation, but also for the preference aggregation. The idea is that we embrace the inevitable impossibility results (such as Arrow and GS theorems) and consider agents' ability to vote strategically as an opportunity to reach stable outcomes. 

This paper uses very simple Q-learning agents with a few different policies - epsilon-greedy, greedy and upper confidence bound, in an iterated voting game, and gets behaviour that seems sensible. (Note the similarity and differences with the moral parliament, where a particular one-shot voting rule is justified a priori and then used.)

The fact that this paper exists is a good sign because it's very recent and the methods it uses are very simple - it's pretty much just a proof of concept, as the authors state - so that tells me there's a lot of room for combining more sophisticated RL with better voting methods.

Combining elicitation and aggregation

Having elicited preferences from each individual human (using methods like those above to 'debias'), we obtain a proxy agent representing each individual's preferences. Then these agents can be placed into an iterated voting situation until a convergent answer is reached.

That seems like the closest practical approximation to a CEV of a group of people that could be constructed with anything close to current methods - a pipeline from observed behaviour and elicited approval to a final aggregated decision about what to do based on overall preferences. Since its a value learning framework that's extendible over any size group, which is somewhat indirect, you might call it a Coherent Extrapolated Framework (CEF) as I suggested last year [LW(p) · GW(p)].

I think that the notion of Simulacra Levels [LW(p) · GW(p)]is both useful and important, especially when we incorporate Harry Frankfurt's idea of Bullshit [LW(p) · GW(p)]. 

Harry Frankfurt's On Bullshit seems relevant here. I think its worth trying to incorporate Frankfurt's definition as well, as it is quite widely known, see e.g. this video - If you were to do so, I think you would say that on Frankfurt's definition, Level 1 tells the truth, Level 2 lies, Level 3 bullshits about physical facts but will lie or tell the truth about things in the social realm (e.g. others motives, your own affiliation), and Level 4 always bullshits.

How do we distinguish lying from bullshit? I worry that there is a tendency to adopt self-justifying signalling explanations, where an internally complicated signalling explanation that's hard to distinguish from a simpler 'lying' explanation, gets accepted, not because it's a better explanation overall but just because it has a ready answer to any objections. If 'Social cognition has been the main focus of Rationality' [LW · GW] is true, then we need to be careful to avoid overusing such explanations. Stefan Schubert explains how this can end up happening:

...

It seems to me that it’s pretty common that signalling explanations are unsatisfactory. They’re often logically complex, and it’s tricky to identify exactly what evidence is needed to demonstrate them.

And yet even unsatisfactory signalling explanations are often popular, especially with a certain crowd. It feels like you’re removing the scales from our eyes; like you’re letting us see our true selves, warts and all. And I worry that this feels a bit too good to some: that they forget about checking the details of how the signalling explanations are supposed to work. Thus they devise just-so stories, or fall for them.

This sort of signalling paradigm also has an in-built self-defence, in that critics are suspected of hypocrisy or naïveté. They lack the intellectual honesty that you need to see the world for what it really is, the thinking goes

Update to 'Modelling Continuous Progress' [LW · GW]

I made an attempt to model intelligence explosion dynamics in this post [LW · GW], by attempting to make the very oversimplified exponential-returns-to-exponentially-increasing-intelligence model used by Bostrom and Yudkowsky slightly less oversimplified.

This post tries to build on a simplified mathematical model of takeoff which was first put forward by Eliezer Yudkowsky and then refined by Bostrom in Superintelligence, modifying it to account for the different assumptions behind continuous, fast progress as opposed to discontinuous progress. As far as I can tell, few people have touched these sorts of simple models since the early 2010’s, and no-one has tried to formalize how newer notions of continuous takeoff fit into them. I find that it is surprisingly easy to accommodate continuous progress and that the results are intuitive and fit with what has already been said qualitatively about continuous progress.

The page includes python code for the model.

This post doesn't capture all the views of takeoff - in particular it doesn't capture the non-hyperbolic faster growth mode scenario, where marginal intelligence improvements are exponentially increasingly difficult, and therefore we get a (continuous or discontinuous switch to a) new exponential growth mode rather than runaway hyperbolic growth.

But I think that by modifying the f(I) function that determines how RSI capability varies with intelligence we can incorporate such views.

In the context of the exponential model given in the post that would correspond to an f(I) function where

which would result in a continuous (determined by size of d) switch to a single faster exponential growth mode

But I think the model still roughly captures the intuition behind scenarios that involve either a continuous or a discontinuous step to an intelligence explosion.

Given the model assumptions, we see how the different scenarios look in practice:

If we plot potential AI capability over time, we can see how no new growth mode (brown) vs a new growth mode (all the rest), the presence of an intelligence explosion (red and orange) vs not (green and purple), and the presence of a discontinuity (red and purple) vs not (orange and green) affect the takeoff trajectory.

Tom Davidson’s report: https://docs.google.com/document/d/1rw1pTbLi2brrEP0DcsZMAVhlKp6TKGKNUSFRkkdP_hs/edit?usp=drivesdk

My old 2020 post: https://www.lesswrong.com/posts/66FKFkWAugS8diydF/modelling-continuous-progress [LW · GW]

In my analysis of Tom Davidson's "Takeoff Speeds Report," I found that the dynamics of AI capability improvement, as discussed in the context of a software-only singularity, align closely with the original simplified equation ( I′(t) = cI + f(I)I^2 ) from my 4 year old post on Modeling continuous progress. Essentially, that post describes how we switch from exponential to hyperbolic growth as the fraction of AI research done by AIs improves along a logistic curve. These are all features of the far more complex mathematical model in tom’s report.

In this equation, ( I ) represents the intelligence or capability of the AI system. It correlates to the cognitive output or the efficiency of the AI as described in the report, where the focus is on the software improvements contributing to the overall effectiveness of AI systems. The term ( cI ) in the equation can be likened to the constant external effort or input in improving AI systems, which is consistent with the ongoing research and development efforts mentioned in the report. This part of the equation represents the incremental improvements in AI capabilities due to human-led development efforts.

The second term in the equation, ( f(I)I^2 ), is particularly significant in understanding the relationship with the software-only singularity concept. Here, ( f(I) ) is a function that determines the extent to which the AI system can use its intelligence to improve itself, essentially a measure of recursive self-improvement (RSI). In the report, the discussion about a software-only singularity uses a similar concept, where the AI systems reach a point where their self-improvement significantly accelerates their capability growth. This is analogous to ( f(I) ) increasing, leading to a more substantial contribution of ( I^2 ) (the AI’s self-improvement efforts) to the overall rate of intelligence growth, ( I′(t) ). As the AI systems become more capable, they contribute more to their own development, a dynamic that both the equation and the report capture. The report has a ‘FLOP gap' from when AIs start to contribute to research at all to when they fully take over which is essentially the upper and lower bounds to fit the f(I) curve to. Otherwise, the overall rate of change is sharper in tom’s report as I ignored increasing investment and increasing compute in my model focussing only on software self improvement feedback loops.

One other thing I liked about Tom’s report is it's focus on relatively outside viewy bio anchors and epoch AIs direct approach estimates for what is needed for TAI.

Maybe this is an unreasonable demand, but one concern I have about all of these alleged attempts to measure the ability of an AI to automate scientific research, is that this feels like a situation where it's unusually slippery and unusually easy to devise a metric that doesn't actually capture what's needed to dramatically accelerate research and development. Ideally, I'd like a metric where we know, as a matter of necessity, that a very high score means that the system would be able to considerably speed up research.

For example, the direct approach estimation does have this property, where if you can replicate to a certain level of accuracy what a human expert would say over a certain horizon length, you do in some sense have to be able to match or replicate the underlying thinking that produced it, which means being able to do long horizon tasks, but of course, that's a very vague upper bound. It's not perfect, the Horizon Length metric might only cover the 90th percentile of tasks at each time scale. The remaining 10th percentile might contain harder, more important tasks necessary for AI progress

I think trying to anticipate and list in a task all the capabilities you think you need to automate scientific progress when we don't really know what those are will lead to a predictable underestimate of what's required.