I agree we don’t currently know how to prevent AI systems from becoming adversarial, and that until we do it seems hard to make strong safety cases for them. But I think this inability is a skill issue, not an inherent property of the domain, and traditionally the core aim of alignment research was to gain this skill.
Plausibly we don’t have enough time to figure out how to gain as much confidence that transformative AI systems are safe as we typically have about e.g. single airplanes, but in my view that’s horrifying, and I think it’s useful to notice how different this situation is from the sort humanity is typically willing to accept.
Right, but then from my perspective it seems like the core problem is that the situations are currently disanalogous, and so it feels reasonable and important to draw the analogy.
Part of Buck’s point is that airplanes are disanalogous for another reason beyond being fundamentally adversarial: airplanes consist of many complex parts we can understand while AIs systems are simple but built out of simple parts we can understand simple systems built out of a small number of (complex) black-box components.
Separately, it’s noting that users being adversarial will be part of the situation. (Though maybe this sort of misuse poses relatively minimal risk.)
I’m still confused what sort of simplicity you’re imagining? From my perspective, the type of complexity which determines the size of the fail surface for alignment mostly stems from things like e.g. “degree of goal stability,” “relative detectability of ill intent,” and other such things that seem far more complicated than airplane parts.
Maybe I’m just confused what you mean by those words, but where is the disanalogy with safety engineering coming from? That normally safety engineering focuses on mitigating risks with complex causes, whereas AI risk is caused by some sort of scaffolding/bureaucracy which is simpler?
Buck’s claim is that safety engineering is mostly focused on problems where there are a huge number of parts that we can understand and test (e.g. airplanes) and the main question is about ensuring failure rates are sufficiently low under realistic operating conditions.
In the case of AIs, we might have systems that look more like a single (likely black-box) AI with access to various tools and the safety failures we’re worried about are concentrated in the AI system itself.
This seems much more analogous to insider threat reduction work than the places where safety engineering is typically applied.
I agree we might end up in a world like that, where it proves impossible to make a decent safety case. I just think of the ~whole goal of alignment research as figuring out how to avoid that world, i.e. of figuring out how to mitigate/estimate the risk as much/precisely as needed to make TAI worth building.
Currently, AI risk estimates are mostly just verbal statements like “I don’t know man, probably some double digit chance of extinction.” This is exceedingly unlike the sort of predictably tolerable risk humanity normally expects from its engineering projects, and which e.g. allows for decent safety cases. So I think it’s quite important to notice how far we currently are from being able to make them, since that suggests the scope and nature of the problem.
I agree we might end up in a world like that, where it proves impossible to make a decent safety case.
I don’t think that thing I said is consistent with “impossible to make a safety case good enough to make TAI worth building”? I think you can probably make a safety case which gets to around 1-5% risk while having AIs that are basically black boxs and which are very powerful, but not arbitrarily powerful. (Such a safety case might require decently expensive measures.) (1-5% risk can be consistent with this being worth it—e.g. if there is an impending hot war. That said, it is still a horrifying level of risk that demands vastly more investment.)
See e.g. control for what one part of this safety case could look like. I think that control can go quite far.
Other parts could look like:
A huge array of model organisms experiments which convince us that P(serious misalignment) is low (or low given XYZ adjustments to training).
coup probes or other simple runtime detection/monitoring techiques.
ELK/honesty techniques which seem to work in a wide variety of cases.
Ensuring AIs closely imitate humans and generalize similarly to humans in the cases we checked.
Ensuring that AIs are very unlikely to be schemers via making sure most of their reasoning happens in CoT and their forward passes are quite weak. (I often think about this from the perspective of control, but it can also be considered separately.)
It’s possible you don’t think any of this stuff gets off the ground. Fair enough if so.
I think that my OP was in hindsight taking for granted that we have to analyze AIs as adversarial. I agree that you could theoretically have safety cases where you never need to reason about AIs as adversarial; I shouldn’t have ignored that possibility, thanks for pointing it out.
I agree we don’t currently know how to prevent AI systems from becoming adversarial, and that until we do it seems hard to make strong safety cases for them. But I think this inability is a skill issue, not an inherent property of the domain, and traditionally the core aim of alignment research was to gain this skill.
Plausibly we don’t have enough time to figure out how to gain as much confidence that transformative AI systems are safe as we typically have about e.g. single airplanes, but in my view that’s horrifying, and I think it’s useful to notice how different this situation is from the sort humanity is typically willing to accept.
Yeah I agree the situation is horrifying and not consistent with eg how risk-aversely we treat airplanes.
Right, but then from my perspective it seems like the core problem is that the situations are currently disanalogous, and so it feels reasonable and important to draw the analogy.
Part of Buck’s point is that airplanes are disanalogous for another reason beyond being fundamentally adversarial: airplanes consist of many complex parts we can understand while AIs systems are
simple but built out of simple parts we can understandsimple systems built out of a small number of (complex) black-box components.Separately, it’s noting that users being adversarial will be part of the situation. (Though maybe this sort of misuse poses relatively minimal risk.)
What’s the sense in which you think they’re more simple? Airplanes strike me as having a much simpler fail surface.
I messed up the wording for that part of the sentence. Does it make more sense now?
I’m still confused what sort of simplicity you’re imagining? From my perspective, the type of complexity which determines the size of the fail surface for alignment mostly stems from things like e.g. “degree of goal stability,” “relative detectability of ill intent,” and other such things that seem far more complicated than airplane parts.
I think the system built out of AI components will likely be pretty simple—as in the scaffolding and bureaucracy surronding the AI will be simple.
The AI components themselves will likely be black-box.
Maybe I’m just confused what you mean by those words, but where is the disanalogy with safety engineering coming from? That normally safety engineering focuses on mitigating risks with complex causes, whereas AI risk is caused by some sort of scaffolding/bureaucracy which is simpler?
Buck’s claim is that safety engineering is mostly focused on problems where there are a huge number of parts that we can understand and test (e.g. airplanes) and the main question is about ensuring failure rates are sufficiently low under realistic operating conditions.
In the case of AIs, we might have systems that look more like a single (likely black-box) AI with access to various tools and the safety failures we’re worried about are concentrated in the AI system itself.
This seems much more analogous to insider threat reduction work than the places where safety engineering is typically applied.
I agree we might end up in a world like that, where it proves impossible to make a decent safety case. I just think of the ~whole goal of alignment research as figuring out how to avoid that world, i.e. of figuring out how to mitigate/estimate the risk as much/precisely as needed to make TAI worth building.
Currently, AI risk estimates are mostly just verbal statements like “I don’t know man, probably some double digit chance of extinction.” This is exceedingly unlike the sort of predictably tolerable risk humanity normally expects from its engineering projects, and which e.g. allows for decent safety cases. So I think it’s quite important to notice how far we currently are from being able to make them, since that suggests the scope and nature of the problem.
I don’t think that thing I said is consistent with “impossible to make a safety case good enough to make TAI worth building”? I think you can probably make a safety case which gets to around 1-5% risk while having AIs that are basically black boxs and which are very powerful, but not arbitrarily powerful. (Such a safety case might require decently expensive measures.) (1-5% risk can be consistent with this being worth it—e.g. if there is an impending hot war. That said, it is still a horrifying level of risk that demands vastly more investment.)
See e.g. control for what one part of this safety case could look like. I think that control can go quite far.
Other parts could look like:
A huge array of model organisms experiments which convince us that P(serious misalignment) is low (or low given XYZ adjustments to training).
coup probes or other simple runtime detection/monitoring techiques.
ELK/honesty techniques which seem to work in a wide variety of cases.
Ensuring AIs closely imitate humans and generalize similarly to humans in the cases we checked.
Ensuring that AIs are very unlikely to be schemers via making sure most of their reasoning happens in CoT and their forward passes are quite weak. (I often think about this from the perspective of control, but it can also be considered separately.)
It’s possible you don’t think any of this stuff gets off the ground. Fair enough if so.
I think that my OP was in hindsight taking for granted that we have to analyze AIs as adversarial. I agree that you could theoretically have safety cases where you never need to reason about AIs as adversarial; I shouldn’t have ignored that possibility, thanks for pointing it out.