Suppose AI continues on its current trajectory: deep learning continues to get better as we throw more data and compute at it, researchers keep trying random architectures and using whatever seems to work well in practice. Do we end up with aligned AI “by default”?
I think there’s at least a plausible trajectory in which the answer is “yes”. Not very likely - I’d put it at ~10% chance - but plausible. In fact, there’s at least an argument to be made that alignment-by-default is more likely to work than many fancy alignment proposals, including IRL variants and HCH-family methods.
This post presents the rough models and arguments.
I’ll break it down into two main pieces:
Will a sufficiently powerful unsupervised learner “learn human values”? What does that even mean?
Will a supervised/reinforcement learner end up aligned to human values, given a bunch of data/feedback on what humans want?
Ultimately, we’ll consider a semi-supervised/transfer-learning style approach, where we first do some unsupervised learning and hopefully “learn human values” before starting the supervised/reinforcement part.
In this section, we’ll talk about why an unsupervised learner might not “learn human values”. Since an unsupervised learner is generally just optimized for predictive power, we’ll start by asking whether theoretical algorithms with best-possible predictive power (i.e. Bayesian updates on low-level physics models) “learn human values”, and what that even means. Then, we’ll circle back to more realistic algorithms.
Consider a low-level physical model of some humans - e.g. a model which simulates every molecule comprising the humans. Does this model “know human values”? In one sense, yes: the low-level model has everything there is to know about human values embedded within it, in exactly the same way that human values are embedded in physical humans. It has “learned human values”, in a sense sufficient to predict any real-world observations involving human values.
But it seems like there’s a sense in which such a model does not “know” human values. Specifically, although human values are embedded in the low-level model, the embedding itself is nontrivial. Even if we have the whole low-level model, we still need that embedding in order to “point to” human values specifically - e.g. to use them as an optimization target. Indeed, when we say “point to human values”, what we mean is basically “specify the embedding”. (Side note: treating human values as an optimization target is not the only use-case for “pointing to human values”, and we still need to point to human values even if we’re not explicitly optimizing for anything. But that’s a separate discussion, and imagining using values as an optimization target is useful to give a mental image of what we mean by “pointing”.)
In short: predictive power alone is not sufficient to define human values. The missing part is the embedding of values within the model. The hard part is pointing to the thing (i.e. specifying the values-embedding), not learning the thing (i.e. finding a model in which values are embedded).
Finally, here’s a different angle on the same argument which will probably drive some of the philosophers up in arms: any model of the real world with sufficiently high general predictive power will have a model of human values embedded within it. After all, it has to predict the parts of the world in which human values are embedded in the first place - i.e. the parts of which humans are composed, the parts on which human values are implemented. So in principle, it doesn’t even matter what kind of model we use or how it’s represented; as long the predictive power is good enough, values will be embedded in there, and the main problem will be finding the embedding.
Unsupervised: Natural Abstractions
In this section, we’ll talk about how and why a large class of unsupervised methods might “learn the embedding” of human values, in a useful sense.
First, notice that basically everything from the previous section still holds if we replace the phrase “human values” with “trees”. A low-level physical model of a forest has everything there is to know about trees embedded within it, in exactly the same way that trees are embedded in the physical forest. However, while there are trees embedded in the low-level model, the embedding itself is nontrivial. Predictive power alone is not sufficient to define trees; the missing part is the embedding of trees within the model.
More generally, whenever we have some high-level abstract object (i.e. higher-level than quantum fields), like trees or human values, a low-level model might have the object embedded within it but not “know” the embedding.
Now for the interesting part: empirically, we have whole classes of neural networks in which concepts like “tree” have simple, identifiable embeddings. These are unsupervised systems, trained for predictive power, yet they apparently “learn the tree-embedding” in the sense that the embedding is simple: it’s just the activation of a particular neuron, a particular channel, or a specific direction in the activation-space of a few neurons.
Neat example with “trees” from the paper linked above.
What’s going on here? We know that models optimized for predictive power will not have trivial tree-embeddings in general; low-level physics simulations demonstrate that much. Yet these neural networks do end up with trivial tree-embeddings, so presumably some special properties of the systems make this happen. But those properties can’t be that special, because we see the same thing for a reasonable variety of different architectures, datasets, etc.
Here’s what I think is happening: “tree” is a natural abstraction. More on what that means here, but briefly: abstractions summarize information which is relevant far away. When we summarize a bunch of atoms as “a tree”, we’re throwing away lots of information about the exact positions of molecules/cells within the tree, or about the pattern of bark on the tree’s surface. But information like the exact positions of molecules within the tree is irrelevant to things far away - that signal is all wiped out by the noise of air molecules between the tree and the observer. The flap of a butterfly’s wings may alter the trajectory of a hurricane, but unless we know how all wings of all butterflies are flapping, that tiny signal is wiped out by noise for purposes of our own predictions. Most information is irrelevant to things far away, not in the sense that there’s no causal connection, but in the sense that the signal is wiped out by noise in other unobserved variables.
If a concept is a natural abstraction, that means that the concept summarizes all the information which is relevant to anything far away, and isn’t too sensitive to the exact notion of “far away” involved. That’s what I think is going on with “tree”.
Getting back to neural networks: it’s easy to see why a broad range of architectures would end up “using” natural abstractions internally. Because the abstraction summarizes information which is relevant far away, it allows the system to make far-away predictions without passing around massive amounts of information all the time. In a low-level physics model, we don’t need abstractions because we do pass around massive amounts of information all the time, but real systems won’t have anywhere near that capacity any time soon. So for the foreseeable future, we should expect to see real systems with strong predictive power using natural abstractions internally.
With all that in mind, it’s time to drop the tree-metaphor and come back to human values. Are human values a natural abstraction?
If you’ve read Value is Fragile or Godshatter, then there’s probably a knee-jerk reaction to say “no”. Human values are basically a bunch of randomly-generated heuristics which proved useful for genetic fitness; why would they be a “natural” abstraction? But remember, the same can be said of trees. Trees are a complicated pile of organic spaghetti code, but “tree” is still a natural abstraction, because the concept summarizes all the information from that organic spaghetti pile which is relevant to things far away. In particular, it summarizes anything about one tree which is relevant to far-away trees.
Similarly, the concept of “human” summarizes all the information about one human which is relevant to far-away humans. It’s a natural abstraction.
Now, I don’t think “human values” are a natural abstraction in exactly the same way as “tree” - specifically, trees are abstract objects, whereas human values are properties of certain abstract objects (namely humans). That said, I think it’s pretty obvious that “human” is a natural abstraction in the same way as “tree”, and I expect that humans “have values” in roughly the same way that trees “have branching patterns”. Specifically, the natural abstraction contains a bunch of information, that information approximately factors into subcomponents (including “branching pattern”), and “human values” is one of those information-subcomponents for humans.
Branching patterns for a few different kinds of trees.
I wouldn’t put super-high confidence on all of this, but given the remarkable track record of hackish systems learning natural abstractions in practice, I’d give maybe a 70% chance that a broad class of systems (including neural networks) trained for predictive power end up with a simple embedding of human values. A plurality of my uncertainty is on how to think about properties of natural abstractions. A significant chunk of uncertainty is also on the possibility that natural abstraction is the wrong way to think about the topic altogether, although in that case I’d still assign a reasonable chance that neural networks end up with simple embeddings of human values - after all, no matter how we frame it, they definitely have trivial embeddings of many other complicated high-level objects.
Aside: Microscope AI
Microscope AI is about studying the structure of trained neural networks, and trying to directly understand their learned internal algorithms, models and concepts. In light of the previous section, there’s an obvious path to alignment where there turns out to be a few neurons (or at least some simple embedding) which correspond to human values, we use the tools of microscope AI to find that embedding, and just like that the alignment problem is basically solved.
Of course it’s unlikely to be that simple in practice, even assuming a simple embedding of human values. I don’t expect the embedding to be quite as simple as one neuron activation, and it might not be easy to recognize even if it were. Part of the problem is that we don’t even know the type signature of the thing we’re looking for - in other words, there are unanswered fundamental conceptual questions here, which make me less-than-confident that we’d be able to recognize the embedding even if it were right under our noses.
That said, this still seems like a reasonably-plausible outcome, and it’s an approach which is particularly well-suited to benefit from marginal theoretical progress.
One thing to keep in mind: this is still only about aligning one AI; success doesn’t necessarily mean a future in which more advanced AIs remain aligned. More on that later.
Supervised/Reinforcement: Proxy Problems
Suppose we collect some kind of data on what humans want, and train a system on that. The exact data and type of learning doesn’t really matter here; the relevant point is that any data-collection process is always, no matter what, at best a proxy for actual human values. That’s a problem, because Goodhart’s Law plus Hidden Complexity of Wishes. You’ve probably heard this a hundred times already, so I won’t belabor it.
Here’s the interesting possibility: assume the data is crap. It’s so noisy that, even though the data-collection process is just a proxy for real values, the data is consistent with real human values. Visually:
Real human values are represented by the blue point, and the true center of our proxy measure is the red point. In this case, the data generated (other points) is noisy enough that it’s consistent with real human values. Disclaimer: this is an analogy, I don’t actually imagine values and proxies being directly represented in the same space as the data.
At first glance, this isn’t much of an improvement. Sure, the data is consistent with human values, but it’s consistent with a bunch of other possibilities too - including the real data-collection process (which is exactly the proxy we wanted to avoid in the first place).
But now suppose we do some transfer learning. We start with a trained unsupervised learner, which already has a simple embedding of human values (we hope). We give our supervised learner access to that system during training. Because the unsupervised learner has a simple embedding of human values, the supervised learner can easily score well by directly using that embedded human values model. So, we cross our fingers and hope the supervised learner just directly uses that embedded human values model, and the data is noisy enough that it never “figures out” that it can get better performance by directly modelling the data-collection process instead.
In other words: the system uses an actual model of human values as a proxy for our proxy of human values.
This requires hitting a window - our data needs to be good enough that the system can tell it should use human values as a proxy, but bad enough that the system can’t figure out the specifics of the data-collection process enough to model it directly. This window may not even exist.
(Side note: we can easily adjust this whole story to a situation where we’re training for some task other than “satisfy human values”. In that case, the system would use the actual model of human values to model the Hidden Complexity of whatever task it’s training on.)
Of course in practice, the vast majority of the things people use as objectives for training AI probably wouldn’t work at all. I expect that they usually look like this:
In other words, most objectives are so bad that even a little bit of data is enough to distinguish the proxy from real human values. But if we assume that there’s some try-it-and-see going on, i.e. people try training on various objectives and keep the AIs which seem to do roughly what the humans want, then it’s maybe plausible that we end up iterating our way to training objectives which “work”. That’s assuming things don’t go irreversibly wrong before then - including not just hostile takeover, but even just development of deceptive behavior, since this scenario does not have any built-in mechanism to detect deception.
Overall, I’d give maybe a 10-20% chance of alignment by this path, assuming that the unsupervised system does end up with a simple embedding of human values. The main failure mode I’d expect, assuming we get the chance to iterate, is deception - not necessarily “intentional” deception, just the system being optimized to look like it’s working the way we want rather than actually working the way we want. It’s the proxy problem again, but this time at the level of humans-trying-things-and-seeing-if-they-work, rather than explicit training objectives.
Alignment in the Long Run
So far, we’ve only talked about one AI ending up aligned, or a handful ending up aligned at one particular time. However, that isn’t really the ultimate goal of AI alignment research. What we really want is for AI to remain aligned in the long run, as we (and AIs themselves) continue to build new and more powerful systems and/or scale up existing systems over time.
I know of two main ways to go from aligning one AI to long-term alignment:
Make the alignment method/theory very reliable and robust to scale, so we can continue to use it over time as AI advances.
Align one roughly-human-level-or-smarter AI, then use that AI to come up with better alignment methods/theories.
The alignment-by-default path relies on the latter. Even assuming alignment happens by default, it is unlikely to be highly reliable or robust to scale.
That’s scary. We’d be trusting the AI to align future AIs, without having any sure-fire way to know that the AI is itself aligned. (If we did have a sure-fire way to tell, then that would itself be most of a solution to the alignment problem.)
That said, there’s a bright side: when alignment-by-default works, it’s a best-case scenario. The AI has a basically-correct model of human values, and is pursuing those values. Contrast this to things like IRL variants, which at best learn a utility function which approximates human values (which are probably not themselves a utility function). Or the HCH family of methods, which at best mimic a human with a massive hierarchical bureaucracy at their command, and certainly won’t be any more aligned than that human+bureaucracy would be.
To the extent that alignment of the successor system is limited by alignment of the parent system, that makes alignment-by-default potentially a more promising prospect than IRL or HCH. In particular, it seems plausible that imperfect alignment gets amplified into worse-and-worse alignment as systems design their successors. For instance, a system which tries to look like it’s doing what humans want rather than actually doing what humans want will design a successor which has even better human-deception capabilities. That sort of problem makes “perfect” alignment - i.e. an AI actually pointed at a basically-correct model of human values - qualitatively safer than a system which only manages to be not-instantly-disastrous.
(Side note: this isn’t the only reason why “basically perfect” alignment matters, but I do think it’s the most relevant such argument for one-time alignment/short-term term methods, especially on not-very-superhuman AI.)
In short: when alignment-by-default works, we can use the system to design a successor without worrying about amplification of alignment errors. However, we wouldn’t be able to tell for sure whether alignment-by-default had worked or not, and it’s still possible that the AI would make plain old mistakes in designing its successor.
Conclusion
Let’s recap the bold points:
A low-level model of some humans has everything there is to know about human values embedded within it, in exactly the same way that human values are embedded in physical humans. The embedding, however, is nontrivial. Thus...
Predictive power alone is not sufficient to define human values. The missing part is the embedding of values within the model. However…
This also applies if we replace the phrase “human values” with “trees”. Yet we have a whole class of neural networks in which a simple embedding lights up in response to trees. Why?
Trees are a natural abstraction, and we should expect to see real systems trained for predictive power use natural abstractions internally.
Human values are a little different from trees (they’re a property of an abstract object rather than an abstract object themselves), but I still expect that a broad class of systems trained for predictive power will end up with simple embeddings of human values (~70% chance).
Because the unsupervised learner has a simple embedding of human values, a supervised/reinforcement learner can easily score well on values-proxy-tasks by directly using that model of human values. In other words, the system uses an actual model of human values as a proxy for our proxy of human values (~10-20% chance).
When alignment-by-default works, it’s basically a best-case scenario, so we can safely use the system to design a successor without worrying about amplification of alignment errors (among other things).
Overall, I only give this whole path ~10% chance of working in the short term, and maybe half that in the long term. However, if amplification of alignment errors turns out to be a major limiting factor for long-term alignment, then alignment-by-default is plausibly more likely to work than approaches in the IRL or HCH families.
The limiting factor here is mainly identifying the (probably simple) embedding of human values within a learned model, so microscope AI and general theory development are both good ways to improve the outlook. Also, in the event that we are able to identify a simple embedding of human values in a learned model, it would be useful to have a way to translate that embedding into new systems, in order to align successors.
Suppose AI continues on its current trajectory: deep learning continues to get better as we throw more data and compute at it, researchers keep trying random architectures and using whatever seems to work well in practice. Do we end up with aligned AI “by default”?
I think there’s at least a plausible trajectory in which the answer is “yes”. Not very likely - I’d put it at ~10% chance - but plausible. In fact, there’s at least an argument to be made that alignment-by-default is more likely to work than many fancy alignment proposals, including IRL variants and HCH-family methods.
This post presents the rough models and arguments.
I’ll break it down into two main pieces:
Ultimately, we’ll consider a semi-supervised/transfer-learning style approach, where we first do some unsupervised learning and hopefully “learn human values” before starting the supervised/reinforcement part.
As background, I will assume you’ve read some of the core material about human values from the sequences, including Hidden Complexity of Wishes, Value is Fragile, and Thou Art Godshatter.
Unsupervised: Pointing to Values
In this section, we’ll talk about why an unsupervised learner might not “learn human values”. Since an unsupervised learner is generally just optimized for predictive power, we’ll start by asking whether theoretical algorithms with best-possible predictive power (i.e. Bayesian updates on low-level physics models) “learn human values”, and what that even means. Then, we’ll circle back to more realistic algorithms.
Consider a low-level physical model of some humans - e.g. a model which simulates every molecule comprising the humans. Does this model “know human values”? In one sense, yes: the low-level model has everything there is to know about human values embedded within it, in exactly the same way that human values are embedded in physical humans. It has “learned human values”, in a sense sufficient to predict any real-world observations involving human values.
But it seems like there’s a sense in which such a model does not “know” human values. Specifically, although human values are embedded in the low-level model, the embedding itself is nontrivial. Even if we have the whole low-level model, we still need that embedding in order to “point to” human values specifically - e.g. to use them as an optimization target. Indeed, when we say “point to human values”, what we mean is basically “specify the embedding”. (Side note: treating human values as an optimization target is not the only use-case for “pointing to human values”, and we still need to point to human values even if we’re not explicitly optimizing for anything. But that’s a separate discussion, and imagining using values as an optimization target is useful to give a mental image of what we mean by “pointing”.)
In short: predictive power alone is not sufficient to define human values. The missing part is the embedding of values within the model. The hard part is pointing to the thing (i.e. specifying the values-embedding), not learning the thing (i.e. finding a model in which values are embedded).
Finally, here’s a different angle on the same argument which will probably drive some of the philosophers up in arms: any model of the real world with sufficiently high general predictive power will have a model of human values embedded within it. After all, it has to predict the parts of the world in which human values are embedded in the first place - i.e. the parts of which humans are composed, the parts on which human values are implemented. So in principle, it doesn’t even matter what kind of model we use or how it’s represented; as long the predictive power is good enough, values will be embedded in there, and the main problem will be finding the embedding.
Unsupervised: Natural Abstractions
In this section, we’ll talk about how and why a large class of unsupervised methods might “learn the embedding” of human values, in a useful sense.
First, notice that basically everything from the previous section still holds if we replace the phrase “human values” with “trees”. A low-level physical model of a forest has everything there is to know about trees embedded within it, in exactly the same way that trees are embedded in the physical forest. However, while there are trees embedded in the low-level model, the embedding itself is nontrivial. Predictive power alone is not sufficient to define trees; the missing part is the embedding of trees within the model.
More generally, whenever we have some high-level abstract object (i.e. higher-level than quantum fields), like trees or human values, a low-level model might have the object embedded within it but not “know” the embedding.
Now for the interesting part: empirically, we have whole classes of neural networks in which concepts like “tree” have simple, identifiable embeddings. These are unsupervised systems, trained for predictive power, yet they apparently “learn the tree-embedding” in the sense that the embedding is simple: it’s just the activation of a particular neuron, a particular channel, or a specific direction in the activation-space of a few neurons.
Neat example with “trees” from the paper linked above.
What’s going on here? We know that models optimized for predictive power will not have trivial tree-embeddings in general; low-level physics simulations demonstrate that much. Yet these neural networks do end up with trivial tree-embeddings, so presumably some special properties of the systems make this happen. But those properties can’t be that special, because we see the same thing for a reasonable variety of different architectures, datasets, etc.
Here’s what I think is happening: “tree” is a natural abstraction. More on what that means here, but briefly: abstractions summarize information which is relevant far away. When we summarize a bunch of atoms as “a tree”, we’re throwing away lots of information about the exact positions of molecules/cells within the tree, or about the pattern of bark on the tree’s surface. But information like the exact positions of molecules within the tree is irrelevant to things far away - that signal is all wiped out by the noise of air molecules between the tree and the observer. The flap of a butterfly’s wings may alter the trajectory of a hurricane, but unless we know how all wings of all butterflies are flapping, that tiny signal is wiped out by noise for purposes of our own predictions. Most information is irrelevant to things far away, not in the sense that there’s no causal connection, but in the sense that the signal is wiped out by noise in other unobserved variables.
If a concept is a natural abstraction, that means that the concept summarizes all the information which is relevant to anything far away, and isn’t too sensitive to the exact notion of “far away” involved. That’s what I think is going on with “tree”.
Getting back to neural networks: it’s easy to see why a broad range of architectures would end up “using” natural abstractions internally. Because the abstraction summarizes information which is relevant far away, it allows the system to make far-away predictions without passing around massive amounts of information all the time. In a low-level physics model, we don’t need abstractions because we do pass around massive amounts of information all the time, but real systems won’t have anywhere near that capacity any time soon. So for the foreseeable future, we should expect to see real systems with strong predictive power using natural abstractions internally.
With all that in mind, it’s time to drop the tree-metaphor and come back to human values. Are human values a natural abstraction?
If you’ve read Value is Fragile or Godshatter, then there’s probably a knee-jerk reaction to say “no”. Human values are basically a bunch of randomly-generated heuristics which proved useful for genetic fitness; why would they be a “natural” abstraction? But remember, the same can be said of trees. Trees are a complicated pile of organic spaghetti code, but “tree” is still a natural abstraction, because the concept summarizes all the information from that organic spaghetti pile which is relevant to things far away. In particular, it summarizes anything about one tree which is relevant to far-away trees.
Similarly, the concept of “human” summarizes all the information about one human which is relevant to far-away humans. It’s a natural abstraction.
Now, I don’t think “human values” are a natural abstraction in exactly the same way as “tree” - specifically, trees are abstract objects, whereas human values are properties of certain abstract objects (namely humans). That said, I think it’s pretty obvious that “human” is a natural abstraction in the same way as “tree”, and I expect that humans “have values” in roughly the same way that trees “have branching patterns”. Specifically, the natural abstraction contains a bunch of information, that information approximately factors into subcomponents (including “branching pattern”), and “human values” is one of those information-subcomponents for humans.
Branching patterns for a few different kinds of trees.
I wouldn’t put super-high confidence on all of this, but given the remarkable track record of hackish systems learning natural abstractions in practice, I’d give maybe a 70% chance that a broad class of systems (including neural networks) trained for predictive power end up with a simple embedding of human values. A plurality of my uncertainty is on how to think about properties of natural abstractions. A significant chunk of uncertainty is also on the possibility that natural abstraction is the wrong way to think about the topic altogether, although in that case I’d still assign a reasonable chance that neural networks end up with simple embeddings of human values - after all, no matter how we frame it, they definitely have trivial embeddings of many other complicated high-level objects.
Aside: Microscope AI
Microscope AI is about studying the structure of trained neural networks, and trying to directly understand their learned internal algorithms, models and concepts. In light of the previous section, there’s an obvious path to alignment where there turns out to be a few neurons (or at least some simple embedding) which correspond to human values, we use the tools of microscope AI to find that embedding, and just like that the alignment problem is basically solved.
Of course it’s unlikely to be that simple in practice, even assuming a simple embedding of human values. I don’t expect the embedding to be quite as simple as one neuron activation, and it might not be easy to recognize even if it were. Part of the problem is that we don’t even know the type signature of the thing we’re looking for - in other words, there are unanswered fundamental conceptual questions here, which make me less-than-confident that we’d be able to recognize the embedding even if it were right under our noses.
That said, this still seems like a reasonably-plausible outcome, and it’s an approach which is particularly well-suited to benefit from marginal theoretical progress.
One thing to keep in mind: this is still only about aligning one AI; success doesn’t necessarily mean a future in which more advanced AIs remain aligned. More on that later.
Supervised/Reinforcement: Proxy Problems
Suppose we collect some kind of data on what humans want, and train a system on that. The exact data and type of learning doesn’t really matter here; the relevant point is that any data-collection process is always, no matter what, at best a proxy for actual human values. That’s a problem, because Goodhart’s Law plus Hidden Complexity of Wishes. You’ve probably heard this a hundred times already, so I won’t belabor it.
Here’s the interesting possibility: assume the data is crap. It’s so noisy that, even though the data-collection process is just a proxy for real values, the data is consistent with real human values. Visually:
Real human values are represented by the blue point, and the true center of our proxy measure is the red point. In this case, the data generated (other points) is noisy enough that it’s consistent with real human values. Disclaimer: this is an analogy, I don’t actually imagine values and proxies being directly represented in the same space as the data.
At first glance, this isn’t much of an improvement. Sure, the data is consistent with human values, but it’s consistent with a bunch of other possibilities too - including the real data-collection process (which is exactly the proxy we wanted to avoid in the first place).
But now suppose we do some transfer learning. We start with a trained unsupervised learner, which already has a simple embedding of human values (we hope). We give our supervised learner access to that system during training. Because the unsupervised learner has a simple embedding of human values, the supervised learner can easily score well by directly using that embedded human values model. So, we cross our fingers and hope the supervised learner just directly uses that embedded human values model, and the data is noisy enough that it never “figures out” that it can get better performance by directly modelling the data-collection process instead.
In other words: the system uses an actual model of human values as a proxy for our proxy of human values.
This requires hitting a window - our data needs to be good enough that the system can tell it should use human values as a proxy, but bad enough that the system can’t figure out the specifics of the data-collection process enough to model it directly. This window may not even exist.
(Side note: we can easily adjust this whole story to a situation where we’re training for some task other than “satisfy human values”. In that case, the system would use the actual model of human values to model the Hidden Complexity of whatever task it’s training on.)
Of course in practice, the vast majority of the things people use as objectives for training AI probably wouldn’t work at all. I expect that they usually look like this:
In other words, most objectives are so bad that even a little bit of data is enough to distinguish the proxy from real human values. But if we assume that there’s some try-it-and-see going on, i.e. people try training on various objectives and keep the AIs which seem to do roughly what the humans want, then it’s maybe plausible that we end up iterating our way to training objectives which “work”. That’s assuming things don’t go irreversibly wrong before then - including not just hostile takeover, but even just development of deceptive behavior, since this scenario does not have any built-in mechanism to detect deception.
Overall, I’d give maybe a 10-20% chance of alignment by this path, assuming that the unsupervised system does end up with a simple embedding of human values. The main failure mode I’d expect, assuming we get the chance to iterate, is deception - not necessarily “intentional” deception, just the system being optimized to look like it’s working the way we want rather than actually working the way we want. It’s the proxy problem again, but this time at the level of humans-trying-things-and-seeing-if-they-work, rather than explicit training objectives.
Alignment in the Long Run
So far, we’ve only talked about one AI ending up aligned, or a handful ending up aligned at one particular time. However, that isn’t really the ultimate goal of AI alignment research. What we really want is for AI to remain aligned in the long run, as we (and AIs themselves) continue to build new and more powerful systems and/or scale up existing systems over time.
I know of two main ways to go from aligning one AI to long-term alignment:
The alignment-by-default path relies on the latter. Even assuming alignment happens by default, it is unlikely to be highly reliable or robust to scale.
That’s scary. We’d be trusting the AI to align future AIs, without having any sure-fire way to know that the AI is itself aligned. (If we did have a sure-fire way to tell, then that would itself be most of a solution to the alignment problem.)
That said, there’s a bright side: when alignment-by-default works, it’s a best-case scenario. The AI has a basically-correct model of human values, and is pursuing those values. Contrast this to things like IRL variants, which at best learn a utility function which approximates human values (which are probably not themselves a utility function). Or the HCH family of methods, which at best mimic a human with a massive hierarchical bureaucracy at their command, and certainly won’t be any more aligned than that human+bureaucracy would be.
To the extent that alignment of the successor system is limited by alignment of the parent system, that makes alignment-by-default potentially a more promising prospect than IRL or HCH. In particular, it seems plausible that imperfect alignment gets amplified into worse-and-worse alignment as systems design their successors. For instance, a system which tries to look like it’s doing what humans want rather than actually doing what humans want will design a successor which has even better human-deception capabilities. That sort of problem makes “perfect” alignment - i.e. an AI actually pointed at a basically-correct model of human values - qualitatively safer than a system which only manages to be not-instantly-disastrous.
(Side note: this isn’t the only reason why “basically perfect” alignment matters, but I do think it’s the most relevant such argument for one-time alignment/short-term term methods, especially on not-very-superhuman AI.)
In short: when alignment-by-default works, we can use the system to design a successor without worrying about amplification of alignment errors. However, we wouldn’t be able to tell for sure whether alignment-by-default had worked or not, and it’s still possible that the AI would make plain old mistakes in designing its successor.
Conclusion
Let’s recap the bold points:
Overall, I only give this whole path ~10% chance of working in the short term, and maybe half that in the long term. However, if amplification of alignment errors turns out to be a major limiting factor for long-term alignment, then alignment-by-default is plausibly more likely to work than approaches in the IRL or HCH families.
The limiting factor here is mainly identifying the (probably simple) embedding of human values within a learned model, so microscope AI and general theory development are both good ways to improve the outlook. Also, in the event that we are able to identify a simple embedding of human values in a learned model, it would be useful to have a way to translate that embedding into new systems, in order to align successors.