The “extreme philosophical competence” hypothesis is that you need such competence to achieve “seriously aligned” in this sense. It sounds like you disagree, but I don’t know why since your reasoning just sidesteps the problem.
Yes, my reasoning is definitely part, but not all of the argument. Like the thing I said is a sufficient crux for me. (If I thought we had to directly use human labor to align AIs which were qualitatively wildly superhuman in general I would put much more weight on “extreme philosophical competence”.)
Looking over the comments of the first joshc post, it seems like that’s also basically asserted it wasn’t necessary by fiat.
I agree that Josh’s first post doesn’t argue about this, but the later two posts kinda do? That said, I think Josh doesn’t really state the argument very clearly in a single place anywhere (at least not in the way I would when trying to cover this objection).
“Training models to do specific things, cleverly, actually just makes it pretty hard for them to develop scheming or other motivated misalignments – they have to jump all the way from “don’t think about scheming ever” to “secretly think about scheming” to avoid getting caught, and that probably just won’t work?”
Hmm, no this isn’t really that much of my model. Or at least, I don’t feel myself thinking thoughts like this.
Interested in whatever Real You’s cruxes are, 1-2 steps removed.
I think this is probably not the best place to discuss this, but I’ll try laying out the quick version of the argument, mostly so I can practice explaining myself here.
Objectives and components
Our goal is to make a trustworthy AI that we defer to. This AI need to be:
Capable: At least as capable as we are collectively. This includes handling wicked strategy/forecasting/conceputual problems and anticipating risks from what it does.
Trustworthy: It needs to faithfully pursue our interests, including on confusing and open ended problems that are conceptually loaded and that we can’t really check.
We’ll do this using two components:
Avoid egregious misalignment: Rule out misalignment that could make behavioral tests very misleading: avoid scheming (both during training and later developed at runtime as we run more copies of the AI), types of sycophancy that are hard to detect, and value drift.
Use behavioral tests for capabilities and the parts of trustworthiness that remain.These tests will need to evaluate how the AI generalizes to tasks that are harder, more open ended, longer, and harder to check than the domains we trained it on. (Because we’ll need things to generalize further than this.) Iterate against these tests until we know how to train AIs that generalize well.
Avoiding egregious misalignment
I’d first claim that we at least have mechanisms (that don’t require extreme philosophical competence) for greatly reducing the risk of egregious misalignment in expectation. The strategies available are:
Test measures on analogous setups (model organisms) and then try to transfer
Apply principled methods where we have some a priori story it should help
Analogous setups includes:
Cases where a model naturally became egregiously misaligned (ideally with a roughly similar architecture and training method to the one we are using).
Cases where we trained in egregious misalignment (or at least heavily nudged the model towards this).
Very abstract analogies where the object we’re studying isn’t actually egregious misalignment, but is analogous to this.
If we had a large range of pretty good test beds of analogous naturally occuring misalignment that cover the failure modes we’re most worried about, then iterating on countermeasures in these test beds seems quite useful. (The countermeasures would be drawn from the principled methods.)
A serious problem is that we’re not necessarily able to consistently detect misalignment. We also need to be able to test whether misalignment is less likely after some countermeasure, so being able to reproducably catch the misalignment at sufficiently low cost is important.[1] This is probably the central difficulty in test beds. We also need to ensure that our countermeasures aren’t effectively training against our tests.[2]
(To get a lot of signal, our test beds would need to be representative of the failure modes we’re worried about or there would need to be some reason to expect the countermeasures we try to generalize to all failure modes)
Another objection is: “Actually, we didn’t have any methods available to try which could end up with a model that (always) isn’t egregiously misaligned. So, even if you can iterate a bunch, you’ll just either find that nothing works or you’ll just fool yourself.” I’m somewhat skeptical of this objection if we actually have a bunch of signal in the test beds and can afford to iterate. It feels to me like in most worlds where we have serious misalignment problems, we can get a good enough set of methods to try that we “just” need like <5 bits. As in, if we were to randomly sample sets of measures (and parameters for these measures, etc) from stuff that seem promising, it seems unlikely we’d need more than 32 tries before something actually worked. This means you maybe don’t need to apply crazy amounts of optimization pressure. I don’t have a cached quick justification.
Oops, I ran out of my time box for this comment. I didn’t get to principled measures. I can send you my doc though.
Behavioral tests
Oops, I ran out of my time box for this comment. Hopefully this section is somewhat comprehensible though I don’t get into detail.
We need to test that our AI does reasonable and aligned stuff on really hard to check tasks. So, how can we get confidence in this given that the tasks are hard to check?
First, we can see how the AI does on relatively hard to check or philosophically tricky tasks which we have already looked into substantially for unrelated reasons. A big reason why things might be hard to check is that we don’t have enough time: the AI might do investigations that would take humans decades. But, we have done some investigations in the past! This won’t ultimately let us check tasks as hard as the ones we want the AIs to do, but we can get close. Note that we also have to apply a similar process for humans.
Second, we can study how generalization on this sort of thing works in general: when an AI is trained on easy, looks good on medium, how will it do on hard? We can study this in all kinds of ways so that we can gain confidence in generalization. In particular, we can get a sense of how reasonable humans are and trust them to generalize. If we could get a similar property for AIs, we might be good, and this doesn’t seem clearly harder than the problem of making AIs which are as capable as humans.
We’d then need to iterate against these tests in a way that doesn’t overfit.
Technically, we just need some signal here, but if catching the misalignment was very costly initially, P(catch) increases with spend, and our initial catch doesn’t make it much easier to catch peturbations of the model that might behave slightly differently, then we probably wouldn’t get enough signal. That said, if we can sometimes detect misalignment in some models, iteration still could be quite useful
More precisely, we need to ensure that our countermeasures aren’t just shifting from a type of misalignment we can detect to a type we can’t. Qualitatively analyzing the countermeasures and our tests should help here.
Thanks for laying this out thus far. I’mma reply but understand if you wanna leave the convo here . I would be interested in more effortpost/dialogue about your thoughts here.
Yes, my reasoning is definitely part, but not all of the argument. Like the thing I said is a sufficient crux for me. (If I thought we had to directly use human labor to align AIs which were qualitatively wildly superhuman in general I would put much more weight on “extreme philosophical competence”.)
This makes sense as a crux for the claim “we need philosophical competence to align unboundedly intelligent superintelligences.” But, it doesn’t make sense for the claim “we need philosophical competence to align general, openended intelligence.” I suppose my OP didn’t really distinguish these claims and there were a few interpretations of how the arguments fit together. I was more saying the second (although to be fair I’m not sure I was actually distinguishing them well in my head until now)
It doesn’t make sense for “we just’ need to be able to hand off to an AI which is seriously aligned” to be a crux for the second. A thing can’t be a crux for itself.
I notice my “other-guy-feels-like-they’re-missing-the-point” → “check if I’m not listening well, or if something is structurally wrong with the convo” alarm is firing, so maybe I do want to ask for one last clarification on “did you feel like you understood this the first time? Does it feel like I’m missing the point of what you said? Do you think you understand why it feels to me like you were missing the point (even if you think it’s because I’m being dense about something?)
Takes on your proposal
Meanwhile, here’s some takes based on my current understanding of your proposal.
These bits:
We need to ensure that our countermeasures aren’t just shifting from a type of misalignment we can detect to a type we can’t. Qualitatively analyzing the countermeasures and our tests should help here.
...is a bit I think is philosophical-competence bottlenecked. And this bit:
“Actually, we didn’t have any methods available to try which could end up with a model that (always) isn’t egregiously misaligned. So, even if you can iterate a bunch, you’ll just either find that nothing works or you’ll just fool yourself.”
...is a mix of “philosophically bottlenecked” and “rationality bottlenecked.” (i.e. you both have to be capable of reasoning about whether you’ve found things that really worked, and, because there are a lot of degrees of freedom, capable of noticing if you’re deploying that reasoning accurately)
I might buy that you and Buck are competent enough here to think clearly about it (not sure. I think you benefit from having a number of people around who seem likely to help), but I would bet against Anthropic decisionmakers being philosophically competent enough.
(I think at least some people on the alignment science or interpretability teams might be. I bet against the median such teammembers being able to navigate it. And ultimately, what matters is “does Anthropic leadership go forward with the next training run”, so it matters whether Anthropic leadership buys arguments from hypothetically-competent-enough alignment/interpretability people. And Anthropic leadership already seem to basically be ignoring arguments of this type, and I don’t actually expect to get the sort of empirical clarity that (it seems like) they’d need to update before it’s too late.)
Second, we can study how generalization on this sort of thing works in general
I think this counts as the sort of empiricism I’m somewhat optimisic about in my post. i.e. if you are able to find experiments that actually give you evidence about deeper laws, that let you then make predictions about new Actually Uncertain questions of generalization that you then run more experiments on… that’s the sort of thing I feel optimistic about. (Depending on the details, of course)
But, you still need technical philosophical competence to know if you’re asking the right questions about generalization, and to know when the results actually imply that the next scale-up is safe.
This makes sense as a crux for the claim “we need philosophical competence to align unboundedly intelligent superintelligences.” But, it doesn’t make sense for the claim “we need philosophical competence to align general, openended intelligence.”
I was thinking of a slightly broader claim: “we need extreme philosophical competence”. If I thought we had to use human labor to align wildly superhuman AIs, I would put much more weight on “extreme philosophical competence is needed”. I agree that “we need philosophical competence to align any general, openended intelligence” isn’t affected by the level of capability at handoff.
I might buy that you and Buck are competent enough here to think clearly about it (not sure. I think you benefit from having a number of people around who seem likely to help), but I would bet against Anthropic decisionmakers being philosophically competent enough.
I think there might be a bit of a (presumably unintentional) motte and bailey here where the motte is “careful conceptual thinking might be required rather than pure naive empiricism (because we won’t have good enough test beds by default) and it seems like Anthropic (leadership) might fail heavily at this thinking” and the bailey is “extreme philosophical competence (e.g. 10-30 years of tricky work) is pretty likely to be needed”.
I buy the motte here, but not the bailey. I think the motte is a substantial discount on Anthropic from my perspective, but I’m kinda sympathetic to where they are coming from. (Getting conceptual stuff and futurism right is real hard! How would they know who to trust among people disagreeing wildly!)
And ultimately, what matters is “does Anthropic leadership go forward with the next training run”, so it matters whether Anthropic leadership buys arguments from hypothetically-competent-enough alignment/interpretability people.
I don’t think “does anthropic stop (at the right time)” is the majority of the relevance of careful conceptual thinking from my perspective. Probably more of it is “do they do a good job allocating their labor and safety research bets”. This is because I don’t think they’ll have very much lead time if any (median −3 months) and takeoff will probably be slower than the amount of lead time if any, so pausing won’t be as relevant. Correspondingly, pausing at the right time isn’t the biggest deal relative to other factors, though it does seem very important at an absolute level.
I think there might be a bit of a (presumably unintentional) motte and bailey here where the motte is “careful conceptual thinking might be required rather than pure naive empiricism (because we won’t be given good enough test beds by default) and it seems like Anthropic (leadership) might fail heavily at this” and the bailey is “extreme philosophical competence (e.g. 10-30 years of tricky work) is pretty likely to be needed”.
Yeah I agree that was happening somewhat. The connecting dots here are “in worlds where it turns out we need a long Philosophical Pause, I think you and Buck would probably be above some threshold where you notice and navigate it reasonably.”
I think my actual belief is “the Motte is high likelihood true, the Bailey is… medium-ish likelihood true, but, like, it’s a distribution, there’s not a clear dividing line between them”
I also think the pause can be “well, we’re running untrusted AGIs and ~trusted pseudogeneral LLM-agents that help with the philosophical progress, but, we can’t run them that long or fast, they help speed things up and make what’d normally be a 10-30 year pause into a 3-10 year pause, but also the world would be going crazy left to it’s own devices, and the sort of global institutional changes necessary are still similarly-outside-of-overton window as a 20 year global moratorium and the “race with China” rhetoric is still bad.
Yes, my reasoning is definitely part, but not all of the argument. Like the thing I said is a sufficient crux for me. (If I thought we had to directly use human labor to align AIs which were qualitatively wildly superhuman in general I would put much more weight on “extreme philosophical competence”.)
I agree that Josh’s first post doesn’t argue about this, but the later two posts kinda do? That said, I think Josh doesn’t really state the argument very clearly in a single place anywhere (at least not in the way I would when trying to cover this objection).
Hmm, no this isn’t really that much of my model. Or at least, I don’t feel myself thinking thoughts like this.
I think this is probably not the best place to discuss this, but I’ll try laying out the quick version of the argument, mostly so I can practice explaining myself here.
Objectives and components
Our goal is to make a trustworthy AI that we defer to. This AI need to be:
Capable: At least as capable as we are collectively. This includes handling wicked strategy/forecasting/conceputual problems and anticipating risks from what it does.
Trustworthy: It needs to faithfully pursue our interests, including on confusing and open ended problems that are conceptually loaded and that we can’t really check.
We’ll do this using two components:
Avoid egregious misalignment: Rule out misalignment that could make behavioral tests very misleading: avoid scheming (both during training and later developed at runtime as we run more copies of the AI), types of sycophancy that are hard to detect, and value drift.
Use behavioral tests for capabilities and the parts of trustworthiness that remain.These tests will need to evaluate how the AI generalizes to tasks that are harder, more open ended, longer, and harder to check than the domains we trained it on. (Because we’ll need things to generalize further than this.) Iterate against these tests until we know how to train AIs that generalize well.
Avoiding egregious misalignment
I’d first claim that we at least have mechanisms (that don’t require extreme philosophical competence) for greatly reducing the risk of egregious misalignment in expectation. The strategies available are:
Test measures on analogous setups (model organisms) and then try to transfer
Apply principled methods where we have some a priori story it should help
Analogous setups includes:
Cases where a model naturally became egregiously misaligned (ideally with a roughly similar architecture and training method to the one we are using).
Cases where we trained in egregious misalignment (or at least heavily nudged the model towards this).
Very abstract analogies where the object we’re studying isn’t actually egregious misalignment, but is analogous to this.
If we had a large range of pretty good test beds of analogous naturally occuring misalignment that cover the failure modes we’re most worried about, then iterating on countermeasures in these test beds seems quite useful. (The countermeasures would be drawn from the principled methods.)
A serious problem is that we’re not necessarily able to consistently detect misalignment. We also need to be able to test whether misalignment is less likely after some countermeasure, so being able to reproducably catch the misalignment at sufficiently low cost is important.[1] This is probably the central difficulty in test beds. We also need to ensure that our countermeasures aren’t effectively training against our tests.[2]
(To get a lot of signal, our test beds would need to be representative of the failure modes we’re worried about or there would need to be some reason to expect the countermeasures we try to generalize to all failure modes)
Another objection is: “Actually, we didn’t have any methods available to try which could end up with a model that (always) isn’t egregiously misaligned. So, even if you can iterate a bunch, you’ll just either find that nothing works or you’ll just fool yourself.” I’m somewhat skeptical of this objection if we actually have a bunch of signal in the test beds and can afford to iterate. It feels to me like in most worlds where we have serious misalignment problems, we can get a good enough set of methods to try that we “just” need like <5 bits. As in, if we were to randomly sample sets of measures (and parameters for these measures, etc) from stuff that seem promising, it seems unlikely we’d need more than 32 tries before something actually worked. This means you maybe don’t need to apply crazy amounts of optimization pressure. I don’t have a cached quick justification.
Oops, I ran out of my time box for this comment. I didn’t get to principled measures. I can send you my doc though.
Behavioral tests
Oops, I ran out of my time box for this comment. Hopefully this section is somewhat comprehensible though I don’t get into detail.
We need to test that our AI does reasonable and aligned stuff on really hard to check tasks. So, how can we get confidence in this given that the tasks are hard to check?
First, we can see how the AI does on relatively hard to check or philosophically tricky tasks which we have already looked into substantially for unrelated reasons. A big reason why things might be hard to check is that we don’t have enough time: the AI might do investigations that would take humans decades. But, we have done some investigations in the past! This won’t ultimately let us check tasks as hard as the ones we want the AIs to do, but we can get close. Note that we also have to apply a similar process for humans.
Second, we can study how generalization on this sort of thing works in general: when an AI is trained on easy, looks good on medium, how will it do on hard? We can study this in all kinds of ways so that we can gain confidence in generalization. In particular, we can get a sense of how reasonable humans are and trust them to generalize. If we could get a similar property for AIs, we might be good, and this doesn’t seem clearly harder than the problem of making AIs which are as capable as humans.
We’d then need to iterate against these tests in a way that doesn’t overfit.
Technically, we just need some signal here, but if catching the misalignment was very costly initially, P(catch) increases with spend, and our initial catch doesn’t make it much easier to catch peturbations of the model that might behave slightly differently, then we probably wouldn’t get enough signal. That said, if we can sometimes detect misalignment in some models, iteration still could be quite useful
More precisely, we need to ensure that our countermeasures aren’t just shifting from a type of misalignment we can detect to a type we can’t. Qualitatively analyzing the countermeasures and our tests should help here.
Thanks for laying this out thus far. I’mma reply but understand if you wanna leave the convo here . I would be interested in more effortpost/dialogue about your thoughts here.
This makes sense as a crux for the claim “we need philosophical competence to align unboundedly intelligent superintelligences.” But, it doesn’t make sense for the claim “we need philosophical competence to align general, openended intelligence.” I suppose my OP didn’t really distinguish these claims and there were a few interpretations of how the arguments fit together. I was more saying the second (although to be fair I’m not sure I was actually distinguishing them well in my head until now)
It doesn’t make sense for “we just’ need to be able to hand off to an AI which is seriously aligned” to be a crux for the second. A thing can’t be a crux for itself.
I notice my “other-guy-feels-like-they’re-missing-the-point” → “check if I’m not listening well, or if something is structurally wrong with the convo” alarm is firing, so maybe I do want to ask for one last clarification on “did you feel like you understood this the first time? Does it feel like I’m missing the point of what you said? Do you think you understand why it feels to me like you were missing the point (even if you think it’s because I’m being dense about something?)
Takes on your proposal
Meanwhile, here’s some takes based on my current understanding of your proposal.
These bits:
...is a bit I think is philosophical-competence bottlenecked. And this bit:
...is a mix of “philosophically bottlenecked” and “rationality bottlenecked.” (i.e. you both have to be capable of reasoning about whether you’ve found things that really worked, and, because there are a lot of degrees of freedom, capable of noticing if you’re deploying that reasoning accurately)
I might buy that you and Buck are competent enough here to think clearly about it (not sure. I think you benefit from having a number of people around who seem likely to help), but I would bet against Anthropic decisionmakers being philosophically competent enough.
(I think at least some people on the alignment science or interpretability teams might be. I bet against the median such teammembers being able to navigate it. And ultimately, what matters is “does Anthropic leadership go forward with the next training run”, so it matters whether Anthropic leadership buys arguments from hypothetically-competent-enough alignment/interpretability people. And Anthropic leadership already seem to basically be ignoring arguments of this type, and I don’t actually expect to get the sort of empirical clarity that (it seems like) they’d need to update before it’s too late.)
I think this counts as the sort of empiricism I’m somewhat optimisic about in my post. i.e. if you are able to find experiments that actually give you evidence about deeper laws, that let you then make predictions about new Actually Uncertain questions of generalization that you then run more experiments on… that’s the sort of thing I feel optimistic about. (Depending on the details, of course)
But, you still need technical philosophical competence to know if you’re asking the right questions about generalization, and to know when the results actually imply that the next scale-up is safe.
I was thinking of a slightly broader claim: “we need extreme philosophical competence”. If I thought we had to use human labor to align wildly superhuman AIs, I would put much more weight on “extreme philosophical competence is needed”. I agree that “we need philosophical competence to align any general, openended intelligence” isn’t affected by the level of capability at handoff.
I think there might be a bit of a (presumably unintentional) motte and bailey here where the motte is “careful conceptual thinking might be required rather than pure naive empiricism (because we won’t have good enough test beds by default) and it seems like Anthropic (leadership) might fail heavily at this thinking” and the bailey is “extreme philosophical competence (e.g. 10-30 years of tricky work) is pretty likely to be needed”.
I buy the motte here, but not the bailey. I think the motte is a substantial discount on Anthropic from my perspective, but I’m kinda sympathetic to where they are coming from. (Getting conceptual stuff and futurism right is real hard! How would they know who to trust among people disagreeing wildly!)
I don’t think “does anthropic stop (at the right time)” is the majority of the relevance of careful conceptual thinking from my perspective. Probably more of it is “do they do a good job allocating their labor and safety research bets”. This is because I don’t think they’ll have very much lead time if any (median −3 months) and takeoff will probably be slower than the amount of lead time if any, so pausing won’t be as relevant. Correspondingly, pausing at the right time isn’t the biggest deal relative to other factors, though it does seem very important at an absolute level.
Yeah I agree that was happening somewhat. The connecting dots here are “in worlds where it turns out we need a long Philosophical Pause, I think you and Buck would probably be above some threshold where you notice and navigate it reasonably.”
I think my actual belief is “the Motte is high likelihood true, the Bailey is… medium-ish likelihood true, but, like, it’s a distribution, there’s not a clear dividing line between them”
I also think the pause can be “well, we’re running untrusted AGIs and ~trusted pseudogeneral LLM-agents that help with the philosophical progress, but, we can’t run them that long or fast, they help speed things up and make what’d normally be a 10-30 year pause into a 3-10 year pause, but also the world would be going crazy left to it’s own devices, and the sort of global institutional changes necessary are still similarly-outside-of-overton window as a 20 year global moratorium and the “race with China” rhetoric is still bad.