But as systems are modified or used to produce successor systems, they may be independently tuned to do things like represent their principal in bargaining situations. This tuning may introduce important divergenes in whatever default priors or notions of fairness were present in the initial mostly-identical systems. I don’t have much intuition for how large these divergences would be relative to those in a regime that started out more heterogeneous.
Importantly, I think this moves you from a human-misaligned AI bargaining situation into more of a human-human (with AI assistants) bargaining situation, which I expect to work out much better, as I don’t expect humans to carry out crazy threats to the same extent as a misaligned AI might.
For instance, multiple mesa-optimizers may be more likely under homogeneity, and if these have different mesa-objectives (perhaps due to being tuned by principals with different goals) then catastrophic bargaining failure may be more likely.
I find the prospect of multiple independent mesa-optimizers inside of the same system relatively unlikely. I think this could basically only happen if you were building a model that was built of independently-trained pieces rather than a single system trained end-to-end, which seems to be not the direction that machine learning is headed in—and for good reason, as end-to-end training means you don’t have to learn the same thing (such as optimization) multiple times.
I find the prospect of multiple independent mesa-optimizers inside of the same system relatively unlikely.
I think Jesse was just claiming that it’s more likely that everyone uses an architecture especially prone to mesa optimization. This means that (if multiple people train that architecture from scratch) the world is likely to end up with many different mesa optimizers in it (each localised to a single system). Because of the random nature of mesa optimization, they may all have very different goals.
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Importantly, I think this moves you from a human-misaligned AI bargaining situation into more of a human-human (with AI assistants) bargaining situation, which I expect to work out much better, as I don’t expect humans to carry out crazy threats to the same extent as a misaligned AI might.
I find the prospect of multiple independent mesa-optimizers inside of the same system relatively unlikely. I think this could basically only happen if you were building a model that was built of independently-trained pieces rather than a single system trained end-to-end, which seems to be not the direction that machine learning is headed in—and for good reason, as end-to-end training means you don’t have to learn the same thing (such as optimization) multiple times.
I think Jesse was just claiming that it’s more likely that everyone uses an architecture especially prone to mesa optimization. This means that (if multiple people train that architecture from scratch) the world is likely to end up with many different mesa optimizers in it (each localised to a single system). Because of the random nature of mesa optimization, they may all have very different goals.
I’m not sure if that’s true—see my comments here and here.