A central AI alignment problem: capabilities generalization, and the sharp left turn

Thanks for the post, I think it's a useful framing. Two things I'd be interested in understanding better:

In the one real example of intelligence being developed we have to look at, continuous application of natural selection in fact found Homo sapiens sapiens, and the capability-gain curves of the ecosystem for various measurables were in fact sharply kinked by this new species (e.g., using machines, we sharply outperform other animals on well-established metrics such as “airspeed”, “altitude”, and “cargo carrying capacity”).

As I said in a reply to Eliezer's AGI ruin post [LW(p) · GW(p)]:

There are some ways in which AGI will be analogous to human evolution. There are some ways in which it will be disanalogous. Any solution to alignment will exploit at least one of the ways in which it's disanalogous. Pointing to the example of humans without analysing the analogies and disanalogies more deeply doesn't help distinguish between alignment proposals which usefully exploit disanalogies, and proposals which don't.

So I'd be curious to know what you think the biggest disanalogies are between the example of human evolution and building AGI. Relatedly, would you consider raising a child to be a "real example of intelligence being developed"; why or why not?

Secondly:

Many different training scenarios are teaching your AI the same instrumental lessons, about how to think in accurate and useful ways. Furthermore, those lessons are underwritten by a simple logical structure

Granting that there's a bunch of logical structure around how to think in accurate ways (e.g. solving scientific problems), and there's a bunch of logical structure around how to pursue goals coherently (e.g. avoiding shutdown) what's the strongest reason to believe that agents won't learn something closely approximating the former before they learn something closely approximating the latter? My impression of Eliezer's position is that it's because they're basically the same structure - if you agree with this, I'd be curious what sort of intuitions or theorems are most responsible for this belief.

(Another way of phrasing this question: suppose I made an analogous argument before the industrial revolution, saying something like "matter and energy are fundamentally the same thing at a deep level, we'll soon be able to harness superhuman amounts of energy, therefore we're soon going to be able to create superhuman amounts of matter". Yet in fact, while the premise of mass-energy equivalence is true, the constants are such that it takes stupendously more energy than humans can generate, in order to produce human-sized piles of matter. What's the main thing that makes you think that the constants in the intelligence case are such that AIs will converge to goal-coherence before, or around the same time as, superhuman scientific capabilities?)

I want to outline how my research programme [LW · GW] attempts to address this core difficulty.

First, like I noted before [LW(p) · GW(p)], evolution is not a perfect analogy for AI. This is because evolution is directly selecting the policy, whereas a (model-based) AI system is separately selecting (i) a world-model (ii) a reward function and (iii) a plan (policy) based on i+ii. This inherently produces better generalization-of-alignment (but not nearly enough to solve the problem).

With iii, we have the least generalization problems, because we are not limited by training data: the AI can use the world-model to test the plan in any scenario, limited only by computing resources.

With ii, we have ample generalization problems because (a) the true reward function we are trying to convey to the AI is complex and (b) the data-points we do have might contain systematic errors. The MIRI approach to addressing this is (1) focusing on a relatively narrow task (like the strawberry problem) and (2) somehow add corrigibility. This approach is difficult because "corrigibility" is not a terribly natural property, and AFAICT it's ill-defined [LW(p) · GW(p)] even pretheoretically. Instead, I propose to address this by directly learning the user's preferences, a path that MIRI believes to be harder (but I believe to be easier).

Since the user is an "arbitrary" system from the AI's perspective, in order to learn the user's preferences we need to understand how to assign agentic interpretations to arbitrary systems (the "intentional stance"), with the understanding that these interpretations are only meaningful to the extent the system is actually an agent (a rock has no sensible agentic interpretation). This seems to me as a natural problem, and indeed there is a line of attack using algorithmic information theory: 1 [LW(p) · GW(p)] 2 [LW · GW].

However, having a well-grounded method of assigning utility functions to policies or programs is, in itself, insufficient. This is because (a) we still need the AI to learn the user's policy/program and (b) we need to avoid allowing the AI to choose a convenient utility function by modifying the user or hacking the channel through which the information is received. To solve this, I propose to use certain tools provided by the infra-Bayesian physicalism [LW · GW] (IBP) framework. Specifically, IBP allows formally specifying the notion of "which programs run in the universe". The user is then one such program, and the remaining problem is how to select it among other problems, which seems tractable by establishing a certain "handshake" protocol [LW(p) · GW(p)]. Moreover, the AI only considers the past^[1] behavior^[2] of the user, so it's impossible for the AI to "cheat" as in 'b' above.

Finally, we need to deal with the generalization of i. At first glance, this should be easier since (a) the true world-model should have low description complexity, implying easy generalization and (b) any false world-model is falsifiable by reality itself, without extra offer on our part. However, from the perspective of a Cartesian agent the world is actually high complexity (because of the need for bridge rules [LW · GW]), undermining 'a'. [EDIT: Moreover, a false world-model can be erroneous at only a few special places, s.t. there are only a few mistakes but their impact is large.] The resulting failures can take the form of malign agents inside the world-model itself.

Here again IBP comes to the rescue, giving the agent an epistemology that requires no bridge rules. [EDIT: And, since the agent holds an unprivileged position in the universe, it leaves much less room for simple-to-describe false world-models that only make different predictions for very special situations.] This doesn't solve all problems entirely, and in particular the agent can still develop malign simulation hypotheses, although (as opposed to Cartesian agents), these malign hypotheses no longer have an overwhelming advantage in probability mass. To address this, I propose designing a filtering mechanism [LW(p) · GW(p)] which discards such hypotheses (roughly speaking, it makes the AI disbelieve any hypothesis that involves a powerful / unhumanlike creator, formalized using IBP tools). It is currently an open problem to demonstrate that this is a complete solution for world-model generalization / inner alignment (or augment it if it isn't), but it does not seem intractable.

I expect a lot of the details to continue to change in the future, as more layers of the math become revealed, but I'm pretty confident in the ability this style of research to guide us onto the right path, eventually.

The central analogy here is that optimizing apes for inclusive genetic fitness (IGF) doesn't make the resulting humans optimize mentally for IGF.

This analogy gets brought out a lot, but has anyone actually spelled it out explicitly? Because it's not clear to me that it holds if you try to explicitly work out the argument. 

In particular, I don't quite understand what it would mean for evolution to optimize the species for fitness, given that fitness is defined as a measure of reproductive success within the species. A genotype has a high fitness, if it tends to increase in frequency relative to other genotypes in that species. 

To be more precise, there is a measure of "absolute fitness" that refers to a specific genotype's success from one generation to the next: if a genotype has 100 individuals in one generation and 80 individuals in the next generation, then it has an absolute fitness of 0.8. But AFAIK evolutionary biology generally focuses on relative fitness - on how well a genotype performs relative to others in the species. If genotype A has an absolute fitness of 1.2 and genotype B has an absolute fitness of 1.5, then genotype B will tend to become more common than A, even though both have fitness > 1. 

Quoting from this Nature Reviews Genetics article:

Although absolute fitness is easy to think about, evolutionary geneticists almost always use a different summary statistic, relative fitness. The relative fitness of a genotype, symbolized w, equals its absolute fitness normalized in some way. In the most common normalization, the absolute fitness of each genotype is divided by the absolute fitness of the fittest genotype ¹¹, such that the fittest genotype has a relative fitness of one. We can also define a selection coefficient, a measure of how much worse the A₂ allele is than A₁. Mathematically, w₂ = 1−s. Just as before, we can calculate various statistics characterizing relative fitness. We can, for instance, find the mean relative fitness ( = pw₁ + qw₂), as well as the variance in relative fitness. [...]

It is the relative fitness of a genotype that almost always matters in evolutionary genetics. The reason is simple. Natural selection is a differential process: there are winners and losers. It is, therefore, the difference in fitness that typically matters.

Going with our previous example, genotype A would have a fitness of 0.8 and genotype B would have a fitness of 1. 

The most natural interpretation of the "fitness of the species" would be as the mean relative fitness of the species:

In late 1960s and early 1970s, Alan Robertson ²⁴ and George Price ²⁵ independently showed that the amount by which any trait, X, changes from one generation to the next is given by the genetic covariance between the trait and relative fitness. (The relevant covariance here is the “additive genetic covariance,” a statistic that disentangles the additive from dominance and epistatic effects of alleles ²⁶) If a trait strongly covaries with relative fitness, it will change a good deal from one generation to the next; if not, not. This result is now known as the Secondary Theorem of Natural Selection ^{27, 28}.

If the trait, X, is relative fitness itself, the additive genetic covariance between X and fitness collapses into the additive genetic variance in relative fitness, V_{A (w)}. Theory allows us to predict, therefore, how much the average relative fitness of a population will change from one generation to the next under selection: it will change by V_{A (w)}. Because a variance cannot be negative, the mean relative fitness of a population either increases or does not change under natural selection (the latter possibility could occur if, for instance, the population harbors no genetic variation). This finding, the Fundamental Theorem of Natural Selection, was first derived by Ronald A. Fisher ²⁹ early in the history of evolutionary genetics. Despite the misleading nomenclature, the Fundamental Theorem is clearly a special case of the Secondary Theorem. It is the Secondary Theorem that is more fundamental.

However, it seems to me that - given that the mean relative fitness is defined by reference to the trait with the highest fitness within the genotype, that implies that the definition of the mean relative fitness changes over time. If the highest-fitness trait changes over time - because the environment changes (due to changes in the climate, other species, etc.), or because of the emergence of a new trait - then the mean relative fitness of the species also changes. The species might also be spread across different regions, with the same trait having different fitness in different regions:

A genotype’s fitness might vary spatially. Within a generation, a genotype might enjoy high fitness if it resides in one region but lower fitness if it resides in other regions. In diploids, spatial variation in fitness can, under certain conditions, maintain genetic variation in a population, a form of so-called balancing selection. The conditions required depend on the precise way in which natural selection acts.

In one scenario, different regions, following viability selection, contribute a fixed proportion of adults to a large random-mating population. This scenario involves “soft selection”: selection acts in a way that changes genotype frequencies within a region but that does not affect the number of adults produced by the region. [...]

In another scenario, different regions, following viability selection, contribute variable proportions of adults to a large random-mating population, depending on the genotypes (and thus fitnesses) of individuals within a region. This scenario involves “hard selection”: selection acts in a way that changes genotype frequencies within a region and affects the number of adults produced by the region.

Also:

The Fundamental Theorem of Natural Selection implies that the mean relative fitness,  of a population generally increases through time and specifies the amount by which it will increase per small unit of time. This suggests a tempting way to think about natural selection: it is a process that increases mean relative fitness.

While attractive and often powerful, it should be emphasized that— surprisingly— the mean fitness of a population does not always increase under natural selection. Population geneticists have identified a number of scenarios in which selection acts but [mean relative fitness] does not increase. These include frequency dependent selection (wherein the fitness of a genotype depends on its frequency in a population) and, in sexual species, certain forms of epistasis (wherein the fitness of a genotype depends on non-additive effects over multiple loci). Put differently, these findings show that the Fundamental Theorem of Natural Selection does not invariably hold. 

The paper does note that one can define alternative definitions of fitness under which the fundamental theorem does hold, but that the "relevant literature is forbidding". The general takeaway that I would draw from this is that fitness is not the kind of clear-cut, "carves reality at joints" kind of a measure that evolution would directly optimize in a similar kind of sense as you directly optimize, say, the amount of correct classifications that a neural net gets on MNIST. 

Rather it's a theoretical fiction or an abstract measure that can be defined in different ways, and which is defined in different ways in different contexts, depending on what kind of an aim one wants to achieve. But that's a simplifying interpretation imposed on complex process for the purpose of modeling it, rather than something that the process actually has an explicit optimization target. So there are ways in which you could view evolution as if it was optimizing for something, but it's not clear to me that it can be said to actually be optimizing for anything in particular - at least not in the sense in which we talk about a machine learning system being optimized for a particular goal.

And in the same stroke that its capabilities leap forward, its alignment properties are revealed to be shallow, and to fail to generalize. The central analogy here is that optimizing apes for inclusive genetic fitness (IGF) doesn't make the resulting humans optimize mentally for IGF. Like, sure, the apes are eating because they have a hunger instinct and having sex because it feels good—but it's not like they could be eating/fornicating due to explicit reasoning about how those activities lead to more IGF. They can't yet perform the sort of abstract reasoning that would correctly justify those actions in terms of IGF. And then, when they start to generalize well in the way of humans, they predictably don't suddenly start eating/fornicating because of abstract reasoning about IGF, even though they now could. Instead, they invent condoms,

This is a super bizarre argument to make about humans considering that we are orders of magnitude more successful by any IGF metric than other apes, and clearly one of the most IGF-successful species ever.

Optimizing apes for inclusive genetic fitness did make the resulting humans optimize mentally for IGF (through various mixes of implicit habitual and explicit model-based reasoning, as is computationally efficient). Across history humans have consciously planned to further their bloodlines, and arguably that was the primary goal of most typical elites/nobles throughout most of history. This analogy supports the opposite of your point.

Invention of condoms is hardly evidence that humanity as a whole stopped optimizing for IGF: having unwanted children often doesn't maximize IGF. Today there are some humans who are leveraging modern technology to better explicitly optimize for IGF, allowing them to have as many children as kings of old.

Evolution proceeds by variation followed by selection. Our current enormous success entails we are in a variation dominated regime, so it's only expected that there are a wide variety of strategies being explored, most of which won't optimize well for IGF. But given time eventually the few that do will dominate (or they would in worlds without AI and the postbiological transition).

I think the degree to which alignment generalizes depends a lot on the type of alignment you're talking about. I think that corrigibility generalizes really badly. In contrast, I think that first-order values kind of okeyishly, and that second order values (i.e., meta ethics on how to weigh different values against each other) generalizes really well. In fact, I suspect that the meta ethics attractor is stronger than the capabilities attractor. 

I also think about the "human values versus evolution" misalignment in a substantially different manner. I don't think that evolution "tried" to directly specified human values. Rather, I think it specified the human reward circuitry, then individual humans learn their values by optimizing for activating their reward circuitry in their environment. So, when you're wondering how much misalignment to expect between a learning process and its reward signal, you shouldn't look at the level of misalignment between things that increase inclusive genetic fitness and human values. Instead, you should be looking at the level of misalignment between things that increase human reward circuit activation and human values.

Finally, I think that evolution had very little say in our meta ethics. I think the meta ethics we do have is more or less convergent for a broad class of learning processes. I think this for two reasons:

1. I don't think there was much evolutionary pressure towards adopting a specific meta ethics. You can, of course, invent "just so" stories about how some particular type of meta ethics cognition was actually adaptive in the ancestral environment. I don't think any such stories hold water for the simple fact that very few humans actually perform meta ethical reasoning. Even today, with our near-universal literacy, highly abstract society and preexisting frameworks for meta ethical cognition, it's very rare. The odds of it being common / important enough in the ancestral environment to be a significant focus of evolutionary optimization are tiny.
2. If you reason from first principles about how agency / learned values ought to work for a generic RL system trained on a complex reward function in a complex environment, then I think you end up with a system that more or less has to do some sort of value reflection / moral philosophy process startlingly similar to our own meta ethical cognition. 

I realize that the second point seems like a stretch. I intend to eventually make a post that properly presents the case for such a conclusion. In the meantime, you can read this comment [LW · GW], which poorly presents the case for such a conclusion. The core argument goes like:

• Given a model trained via RL, there is no "ground truth" on how to draw the agency boundaries. You can draw one boundary around all the parameters and call that "one agent". You can also draw many boundaries around different parameter subsets and call the system "multi agent". 
• Different regions of the model will tend to specialize towards different types of situations in which the model could receive reward for its actions. 
• These different regions of specialization will then tend to have different value representations that are specific to the types of situations in which those regions "activate" to steer the system's behavior.
• Due to the aforementioned arbitrariness of the agent boundaries, we can draw "soft" agent boundaries around these (overlapping) regions of partial specialization. The overall model is less like a single agent with a single value representation, and more like a continuous distribution over possible subagents, whose individual values can be in conflict and highly situational.
• Thus, the process by which the overall model becomes coherent is via a quasi-multi-agent negotiation process between the different regions in the distribution over subagents / values.
• If you think about the sort of cognition that's required to reach the Pareto frontier of internal consensus, it would involve things like weighing different learned values against each other, reflecting on which circumstances different values ought to apply to, checking whether a given joint policy among our values is in strong conflict with any of our existing values^[1], etc.
• Basically, the sorts of cognition that are necessary to resolve conflicts between learned values (or "mesa objectives", if you prefer that term) seems very similar to the sorts of cognition that are central to our meta ethical process.

1. ^{^}

   Consider the cognitive process that would be involved in such a check. You'd have a proposed method to define a joint policy for maximizing your current distribution of values. You'd want to verify that there aren't situations in which this joint policy is radically misaligned with your existing values. To do this, you'd search over situations where the joint policy proscribed actions that were strongly in conflict with one or more of your existing values. If we substitute in the name "moral theory" in place of "joint policy", then this elegantly recovers the cognitive process that we call "moral philosophy by counterexample", without ever having appealed to our evolutionary history or any human-specific aspect of cognition!

Ronny Fernandez on Twitter:

I think I don’t like AI safety analogies with human evolution except as illustrations. I don’t think they’re what convinced the people who use those analogies, and they’re not what convinced me. You can convince yourself of the same things just by knowing some stuff about agency.

Corrigibility, human values, and figure-out-while-aiming-for-human-values, are not short description length. I know because I’ve practiced finding the shortest description lengths of things a lot, and they just don’t seem like the right sort of thing.

Also, if you get to the level where you can realize when you’ve failed, and you try it over and over again, you will find that it is very hard to find a short description of any of these nice things we want.

And so this tells us that a general intelligence we are happy we built is a small target within the wide basin of general intelligence

Ideal agency is short description length. I don’t think particular tractable agency is short description length, and ml cares about run time, but there are heuristic approximations to ideal agency, and there are many different ones because ideal agency is short description length

So this tells us that there is a wide basin of attraction for general intelligence.

These two problems appear in the strawberry problem [LW · GW], which Eliezer's been pointing at for quite some time: the problem of getting an AI to place two identical (down to the cellular but not molecular level) strawberries on a plate, and then do nothing else. The demand of cellular-level copying forces the AI to be capable; the fact that we can get it to duplicate a strawberry instead of doing some other thing demonstrates our ability to direct it; the fact that it does nothing else indicates that it's corrigible (or really well aligned to a delicate human intuitive notion of inaction).

Let T be the target objective we wish to align the system towards. In this paragraph, T is duplicating the strawberry (and doing nothing else). I seriously doubt that alignment is comparably difficult for all objectives T, or even all "reasonable" objectives. I think building an AGI which solves the strawberry problem is far harder than building an AGI which makes lots of dogs in the future. I think that a diamond maximizer is also harder to train than an AGI which makes lots of dogs, but an AGI which makes lots of diamonds is probably only a little harder than an AGI which makes lots of dogs. 

(I haven't explained why I hold these beliefs, but I figured it would be productive to at least note this axis of disagreement.)

When I think about the strawberry problem, it seems unnatural, and perhaps misleading of our attention, since there's no guarantee there's even a reasonable solution. We'd probably be better off thinking about how to make AGIs which care about dogs, because it's an empirical fact that there's a way to align intelligences to that objective. 

Two points:

1. The visualization of capabilities improvements as an attractor basin is pretty well accepted and useful, I think. I kind of like the analogous idea of an alignment target as a repeller cone / dome. The true target is approximately infinitely small and attempts to hit it slide off as optimization pressure is applied. I'm curious if other share this model and if it's been refined / explored in more detail by others.
2. The sharpness of the left turn strikes me as a major crux. Some (most?) alignment proposals seem to rely on developing an AI just a bit smarter than humans but not yet dangerous.  (An implicit assumption here may be that intelligence continues to develop in straight lines.) The sharp left turn model implies this sweet spot will pass by in the blink of an eye. (An implicit assumption here may be that there are discrete leaps.) Interesting to note that Nate explicitly says RSI is not a core part of his model. I'd like to see more arguments on both sides of this debate.

Sharp Left Turn: a more important problem (and a more specific threat model) than people usually think

The sharp left turn is not a simple observation that we've seen capabilities generalise more than alignment. As I understand it, it is a more mechanistic understanding that some people at MIRI have, of dynamics that might produce systems with generalised capabilities but not alignment.

Many times over the past year, I've been surprised by people in the field who've read Nate's post but somehow completely missed the part where it talks about specific dynamics that lead to alignment properties breaking during capabilities generalisation. To fulfil the reviewing duty and to have a place to point people to, I'll try to write down some related intuitions that I talked about throughout 2023 when trying to get people to have intuitions on what the sharp left turn problem is about.

For example, imagine training a neural network with RL. For a while during training, the neural network might be implementing a fuzzy collection of algorithms and various heuristics that together kinda optimise for some goals. The gradient strongly points towards greater capabilities. Some of these algorithms and heuristics might be more useful for the task the neural network is being evaluated on, and they'll persist more and what the neural network is doing as a whole will look a bit more like what the most helpful parts of it are doing.

Some of these algorithms and heuristics might be more agentic and do more for long-term goal achievement than others. As being better at achieving goals correlates with greater performance, the neural network becomes, as a whole, more capable of achieving goals. Or, maybe the transition that leads to capabilities generalisation can be more akin to grokking [LW · GW]: even with a fuzzy solution, the distant general coherent agent implementations might still be visible to the gradient, and at some point, there might be a switch from a fuzzy collection of things together kind of optimising for some goals into a coherent agent optimising for some goals.

In any case, there's this strong gradient pointing towards capabilities generalisation.

The issue is that a more coherent and more agentic solution might have goals different from what the fuzzier solution had been achieving and still perform better. The goal-contents of the coherent agent are stored in a way different from how a fuzzier solution had stored the stuff it had kind of optimised for. This means that the gradient points towards the architecture that implements a more general and coherent agent; but it doesn't point towards the kind of agent that has the same goals the current fuzzy solution has; alignment properties of the current fuzzy solution don't influence the goals of a more coherent agent the gradient points towards.

It is also likely that the components of the fuzzy solution undergo optimisation pressure which means that the whole thing grows towards the direction near components that can outcompete others. If a component is slightly slightly better at agency, at situational awareness, etc., , it might mean it gets to have the whole thing slightly more like it after an optimisation step. The goals these components get could be quite different from what they, together, were kind of optimising for. That means that the whole thing changes and grows towards parts of it with different goals. So, at the point where some parts of the fuzzy solution are near being generally smart and agentic, they might get increasingly smart and agentic, causing the whole system to transform into something with more general capabilities but without gradient also pointing towards the preservation of the goals/alignment properties of the system.

I haven't worked on this problem and don't understand it well; but I think it is a real and important problem, and so I'm sad that many haven't read this post or only skimmed through it or read it but still didn't understand what it's talking about. It could be that it's hard to communicate the problem (maybe intuitions around optimisation are non-native to many?); it could be that not enough resources were spent on optimising the post for communicating the problem well; it could be that the post tried hard not to communicate something related; or it could be that for a general LessWrong reader, it's not a well-written post.

Even if this post failed to communicate its ideas to its target audience, I still believe it is one of the most important LessWrong posts in 2022 and contributed something new and important to the core of our understanding of the AI alignment problem.

Nate's follow-up post is now up: On how various plans miss the hard bits of the alignment challenge [LW · GW].

Is this "sharp left turn" a crux for your overall view, or your high probability of failure?

Naively, it seems to me that if capability gains are systematically gradual, that improvements are iterative, and occur at little at a time, we're in a much better situation with regard to alignment. 

If capabilities gains are gradual, we can continuously feed training data to our system and keep its alignment in step with its capabilities. As soon as it starts to enter a distributional shift, and some of its outputs are (or would be) unaligned, those alignment failures are immediately corrected. You can keep reinforcing corrigibility as capabilities generalize, so that it correctly generalizes the corrigibility concept. Similarly, the more gradually capabilities grow, the more reliable oversight schemes will be.

(On the other hand, this doesn't solve the problem that there's some capability threshold beyond which the outputs of an AI system are illegible to humans, and we can't tell whether or not the outputs are aligned or not, in order to give it corrective training data.

Also, if one could, in principle, increase capabilities gradually, but someone else can throw caution to the wind and turn up the capability dial to 11, the unilateralist's curse kills us.)

How much would finding out that there's not going to be a sharp left turn impact the rest of your model? 

Or, suppose we could magically scale up our systems as gradually as you, Nate, would like, slowing down as we start to see a super-linear improvement, how much safer does is humanity?

there's no analogously-strong attractor well pulling the AGI's objectives towards your preferred objectives

I'm starting to doubt [LW(p) · GW(p)] that there are strategically important human-specific objectives in the decision theory sense, things that should be used to actually optimize everything without goodharting making it counterproductive. In this hypothesis, optimization goals are not just hard to figure out, but there is almost nothing there that's human-specific, human preference is generic. Orthogonality thesis applies to agents with goals, but maybe it doesn't apply to humans, because their goals play a different role from what orthogonality thesis needs. To solve astronomical waste, humans could run their civilization on better substrate and look for goal-shaped principles that can be propagated more efficiently than civilization itself, used for optimization, but these principles are not going to be human-specific.

If an AGI is in a similar situation (doesn't have non-goodharting goals), it's going to be in the same attractor as humans, motivated to build a generic civilization. It doesn't necessarily involve humans or human-specific things, but I'm not sure this is different from a specific human deciding that their civilization doesn't involve other humans, which a priori seems like an unjustifiably privileged aspect of civilizational design.

In other words, applicability of orthogonality thesis might fail if the kind of goal knowledge relevant to it (that overcomes goodharting) tends to get obtained in convergent ways that give results that are generic, not human-specific. The disagreement of this hypothesis with the standard position is about the distinction/interaction between non-goodharting and goodharting goals. On the generic goals hypothesis, most accidental goals, including those currently held by humans, are goodharting goals (according to their role in the minds of people, not to their content), something that shouldn't be used to strongly optimize the world in anything close to their current form. And the proper way of getting appropriate non-goodharting goals (running a civilization/long reflection/CEV) somehow doesn't importantly depend on the currently held goodharting goals (this seems to be the crux).

So in this view, the dangerous AGIs are those that hold any goals in a non-goodharting role, ready to optimize the world according to them, while by default AGIs with more vague architectures are going to face the same problem of formulating non-goodharting goals as humans, and work within the same attractor for arriving at its solution. A better but not drastically different outcome is AGIs that hold human goodharting goals in a goodharting role and no goals in a non-goodharting role, so that they are even more likely to go about formulating non-goodharting goals in the same way humans would, without being opposed to actual humans in the process. Language models might help with this part.

(We can use these terms to restate the familiar catastrophic failure of alignment where an AGI is aligned to hold human goodharting goals (what we currently care about) in a non-goodharting role (as a target for optimizing the world with) without giving civilization the opportunity to improve on those goals and without limiting their applicability to situations comprehensible to current humans. A failure of non-goodharting (optimizing too much on goodharting goals), resulting in a failure of corrigibility (preventing the non-goodharting extrapolated volition from eventually being in charge).)

good capabilities form something like an attractor well

In my own experience examining the foundations of things in the world, I have repeatedly found there to be less of an attractor-of-fundamentally-effective-decision-making than I had anticipated. In every way that I expected to find such an attractor within epistemology, mathematics, empiricism, ethics, I found in fact that even the very basic assumptions that I started with were unfounded, and found nothing firm to replace with them with. Probability theory: not a fundamental answer to epistemology; proof-based agents: deeply paradoxical and seemingly untrustworthy; the scientific method: not precise enough to be a final answer to anything; consequentialism: what actually is it? I have the sense that you've seen just as much of this phenomenon as I have, but you still seem to hold this conviction that there is a deep well of fundamental reasonability for an AI to fall into. Why? I'm just suggesting that the existence of this well is non-trivial feature of reality, and the more we fail to find it, the more we might question whether it exists.

I respond with arguments like, "In the one real example of intelligence being developed we have to look at, continuous application of natural selection in fact found Homo sapiens sapiens, and the capability-gain curves of the ecosystem for various measurables were in fact sharply kinked by this new species (e.g., using machines, we sharply outperform other animals on well-established metrics such as “airspeed”, “altitude”, and “cargo carrying capacity”)."

Their response in turn is generally some variant of "well, natural selection wasn't optimizing very intelligently" or "maybe humans weren't all that sharply above evolutionary trends" or "maybe the power that let humans beat the rest of the ecosystem was simply the invention of culture, and nothing embedded in our own already-existing culture can beat us" or suchlike.

Rather than arguing further here, I'll just say that failing to believe the hard problem exists is one surefire way to avoid tackling it.

It sounds like you don't want to argue this point further here, but I would like to point something very simple out that I think your argument here glosses over. 

Humanity is a species, not an individual. It wasn't the case that a single animal arose among all the others, and out-competed everyone else. Instead, it was a large set of entities that collectively out-competed all the other animals. And I think this distinction is quite important to make.

If you think that an analogy to human evolution is critical to understanding our epistemic situation, it appears to me that the evolutionary analogy should force you to draw the opposite conclusion from the one you have drawn here (relative to credible people who disagree).

In my understanding of our situation, the conclusion to draw from human evolution is that a single species can acquire a host of very powerful technologies, and tower above everyone else, in a relatively short period of time. That is, we should predict that, in the future, a collection of AIs could eventually out-match humanity. 

But you're not arguing that thesis! (At least, as I understand your argument) You're arguing that the evolutionary analogy shows that a single individual can outcompete everyone else. And I don't know where that idea is coming from.

Attempting to manually specify the nature of goodness is a doomed endeavor, of course

where does this come from? Is this just an assumption?

What if it were possibly to specify morality (insofar as safety)? I think it is; Davidad agrees: «Boundaries» for formalizing a bare-bones morality [LW · GW] 

I'm not talking about recursive self-improvement. That's one way to take a sharp left turn, and it could happen, but note that humans have neither the understanding nor control over their own minds to recursively self-improve, and we outstrip the rest of the animals pretty handily. I'm talking about something more like “intelligence that is general enough to be dangerous”, the sort of thing that humans have and chimps don't.

Individual humans can't FOOM (at lest not yet), but humanity did. 

My best guess is that humanity took a sharp left turn when we got a general enough language, and then again when we got writing, and possibly again when the skill of reading an writing spread to a majority of the population.

Before language human intelligence was basically limited to what a single brain could do. When we got language we got the ability of adding compute (more humans) to the same problem solving task. Humanity got parallel computing. This extra capabilities could be used to invent things to increase the population, i.e. recusing self improvement. 

Later, writing gave us external memory. Before our computations where limited by human memory, but now we could start to fill up libraries, unlocking a new level of recursive self improvement.

Every increase in literacy and communication technology (e.g. the internet) is humanity upgrading its capability.

I think alignment might well be very hard and the stakes are certainly very high, but I must say that I find this post only partly convincing. Here are some thoughts I had, reading the post: 

In primates making them smarter probably mostly required scaling the cortex. Making the smarter version more aligned with IGF would have required a rewiring of the older brain parts. One of these is much harder to do than the other. 

But in machines both alignment and capabilities will likely be learned with the same architecture, making it less likely that capabilities outstrip alignment for architecture reasons than in primates. 

That there is a capabilities well is certainly true. But shouldn't there be value wells as well? One might think "I do what is best for me" is a deeper well than "I do what is best for all" and more aligned with instrumental power seeking.

But "I do what is best for me" isn't really a value well, because it is begging the question. If I do what is best for me, I still have to decide what is good. The second well is actually providing values, because other entities already have preferences. Helping them to fulfill those is an actual value, i.e. directs actions, in a way that only "I do what is best for me" isn't. 

Is the structure underlying "do what is best for all" less simple and logical than arithmetic? 

I often see it assumed that the ultimate values of an AGI will be kind of random. Some mesa-optimiser stumbled into during training. If this is true, than there is little optimisation pressure towards those values and it seems possible to train for "do what is best for all" instead. 

I ... recommend aiming humanity's first AGI systems at simple limited goals that end the acute risk period and then cede stewardship of the future to some process that can reliably do the "aim minds towards the right thing" thing

What could that process possibly be? A congress of humanity's best and brightest? An AI sovereign governed by a "Proto-CEV" system of values?

There's no analogous alignment well to slide into.

If one made a series of alignment-through-capabilities-shift tasks, you would get one.

I.e., you make a training set of scenarios where a system gets a lot smarter and has to preserve alignment through that capability shift.

Of course, making such a training set is not easy(!).

Attempting to manually specify the nature of goodness is a doomed endeavor, of course, but that's fine, because we can instead specify processes for figuring out (the coherent extrapolation of) what humans value. […] So today's alignment problems are a few steps removed from tricky moral questions, on my models.
 

I‘m not convinced that choosing those processes is significantly non-moral. I might be misunderstanding what you are pointing at, but it feels like the fact that being able to choose the voting system gives you power over the vote’s outcome is evidence of this sort of thing - that meta decisions are still importantly tied to decisions.

I feel like it needs more ML-inspired metaphors. Sure anyone can imagine gradient descent arranging weights into encoding of Skynet's source code - what do people say/think about why they don't check for this before training GPT with loss function that would totally love Skynet?

One solution I can see for AGI is to build in some low-level discriminator that prevents the agent from collecting massive reward. If the agent is expecting to get near-infinite reward in the near future by wiping out humanity using nanotech, then we can set a solution so it decides to do something that will earn it a more finite amount of reward (like obeying our commands).

This has a parallel with drugs here on Earth. Most people are a little afraid of that type of high.

This probably isn't an effective solution, but I'd love to hear why so I can keep refining my ideas.

NotebookLM is able to generate a good podcast from this post. There are some bugs though.

On my model, one of the most central technical challenges of alignment—and one that every viable alignment plan will probably need to grapple with—is the issue that capabilities generalize better than alignment.

Hello @So8res [LW · GW],  In RLLM, [LW · GW]I use datasets containing repeatedly-explained-morphologies [LW · GW] about "an-AI-acting-a-behavior-in-a-simulated-world." Then, I re-trained GPT2XL to  "observe" these repeatedly-explained-morphologies and saw promising results. I think this process of observing repeatedly-explained-morphologies is very similar to how a language model acquiring biases during pre-training and if the language model is capable enough, it will acquire an understanding of the values (including the simulated world). 

Going back to modifying GPT2XL, I saw some evidence that GPT2XL can score better in a ToM task [LW · GW] (capabilities) and jailbreak attacks [LW · GW] (alignment) compared to than foundation models (ToM [LW · GW], JBs 1, [LW · GW] 2 [LW · GW], 3 [LW · GW]). I would like to know or hear your thoughts on this approach - Is this a good attempt in your books to solve the hard bit challenge, that capabilities generalize better than alignment? Thank you for your time reading this.

maybe it's somewhat easier on account of how we have more introspective access to a working mind than we have to the low-level physical fields;

We have a biased access which makes things tricker because we weren't selected for our introspection skills to be high fidelity and having a correspondence with reality. Rather it's about the utility to survival.

I doubt it's all that qualitatively different than the sorts of summits humanity has surmounted before

This seems to imply that we have surmounted the fields of physics, that all available knowledge in all subfields has been acquired whereas the most that can be claimed is that we have reduced the degree of our ignorance in some of those subfields. We have not - by any stretch of the imagination - mastered the field. Indeed, if we think we cannot push our understanding of AI, and related alignment problems, further than our current degree of understanding of physics, I think that is a strong point for the "stop everything, while we still can" case. 

Do you think we could use grokking/current existing generalization phenomena (e.g induction heads) to test your theory? Or do you expect the generalizations that would lead to the sharp left turn to be greater/more significant than those that occurred earlier in the training? 

Many comparisons are made with Natural Selection (NS) optimizing for IGF, on the grounds that this is our only example of an optimization process yielding intelligence.
 

I would suggest considering one very relevant fact: NS has not optimized for alignment, but only for a myopic version of IGF. I would also suggest considering that humans have not optimized for alignment either.
 

Let's look at some quotes, with those considerations in mind:

And in the same stroke that its capabilities leap forward, its alignment properties are revealed to be shallow, and to fail to generalize. The central analogy here is that optimizing apes for inclusive genetic fitness (IGF) doesn't make the resulting humans optimize mentally for IGF. 

NS has not optimized for alignment, which is why it's bad at alignment compared to what it has optimized for.

Some people I say this to respond with arguments like: "Surely, before a smaller team could get an AGI that can master subjects like biotech and engineering well enough to kill all humans, some other, larger entity such as a state actor will have a somewhat worse AI that can handle biotech and engineering somewhat less well, but in a way that prevents any one AGI from running away with the whole future?" 
 

I respond with arguments like, "In the one real example of intelligence being developed we have to look at, continuous application of natural selection in fact found Homo sapiens sapiens, and the capability-gain curves of the ecosystem for various measurables were in fact sharply kinked by this new species (e.g., using machines, we sharply outperform other animals on well-established metrics such as “airspeed”, “altitude”, and “cargo carrying capacity”)."

NS has not optimized for one intelligence not conquering the rest of the world. As such, it doesn't say anything about how hard it is to optimize to produce one intelligence not conquering the rest of the world.

Their response in turn is generally some variant of "well, natural selection wasn't optimizing very intelligently" or "maybe humans weren't all that sharply above evolutionary trends" or "maybe the power that let humans beat the rest of the ecosystem was simply the invention of culture, and nothing embedded in our own already-existing culture can beat us" or suchlike. 

The response is not that NS is not intelligent, but that NS has not even optimized for any of the things that you have pointed to.

Why does alignment fail while capabilities generalize, at least by default and in predictable practice? In large part, because good capabilities form something like an attractor well. 

My answer would be the same for NS and humans: alignment is simply not optimized for! People spend countless more resources on capabilities than alignment.
If the resources invested in capability vs alignment ratio was reversed, would you still expect alignment to fare so much worse than capabilities?
Let's say you'd still expect that: how much better do you expect the situation to be as a result of the ratio being reversed? How much doom would you still expect in that world compared to now?

good capabilities form something like an attractor well 

Sure, in so far as people will optimize for short-term power (ie, capabilities) because they are myopic and power is the name we give to what is useful in most scenarios.

---

I also expect a discontinuity in intelligence. But I think this post does not make a good case for it: a much simpler theory already explains its observations.

In an upcoming post, I’ll say more about how it looks to me like  ~nobody is working on this particular hard problem, by briefly reviewing a variety of current alignment research proposals. In short, I think that the field’s current range of approaches nearly all assume this problem away, or direct their attention elsewhere. 

I'm very eager to read this.