Do you believe current models have large amounts of undetected misalignment?
I believe that there’s no way to reliably tell. I think “people’s observations in their real world usage” is also the sort of evidence that can easily fail to detect misalignment even if it exists.
I predict that ASI trained using anything resembling current techniques would be catastrophically misaligned. I don’t have a strong prediction about whether current-gen models are “actually” aligned. If I had to guess, I’d say they’re not. This is very speculative but my guess would be that they have misaligned internal drives/goals that would produce bad consequences if they were smart enough to reason through the implications of their goals, but they’re not smart enough to do that (or it might be more accurate to say that their long-term planning isn’t good enough).
What I will say is I don’t think it’s reasonable to believe with (say) >90% confidence that current-gen models are “actually” aligned, because our understanding of what’s going on with LLMs just isn’t that reliable.
I should mention that I don’t keep up with the vast majority of alignment research. There might be some convincing research about why the lack-of-detected-misalignment means there is genuinely no misalignment, and I just missed it; but my guess is if that research existed, then I would’ve heard about it (e.g. it would’ve gotten tons of upvotes on LessWrong, b/c that would be a really important finding). So when Leike claims that models are becoming more aligned, most of my subjective probability for why he said that is that he doesn’t understand the difference between detected misalignment and actual misalignment.
AI psychosis. When you ask LLMs point blank if it’s bad to induce AI psychosis, they say yes, and (AFAIK) there is no evidence that they’re being deceptive, and yet they do it anyway. This is a case where (IIRC) recent models have gotten better (less psychosis-prone), but my guess is that training psychosis out of LLMs doesn’t generalize to other forms of misalignment, and maybe in the next-gen models some new form of bad behavior will emerge.
IMO the best evidence of bad behavior by LLMs is coming out of Palisade, not any AI company. This suggests that AI companies (who have way more resources and access) are not trying sufficiently hard to detect misalignment. The implication is that, if AI companies fail to detect misalignment, this is only weak evidence that the misalignment isn’t there.
I believe that there’s no way to reliably tell. I think “people’s observations in their real world usage” is also the sort of evidence that can easily fail to detect misalignment even if it exists.
I predict that ASI trained using anything resembling current techniques would be catastrophically misaligned. I don’t have a strong prediction about whether current-gen models are “actually” aligned. If I had to guess, I’d say they’re not. This is very speculative but my guess would be that they have misaligned internal drives/goals that would produce bad consequences if they were smart enough to reason through the implications of their goals, but they’re not smart enough to do that (or it might be more accurate to say that their long-term planning isn’t good enough).
What I will say is I don’t think it’s reasonable to believe with (say) >90% confidence that current-gen models are “actually” aligned, because our understanding of what’s going on with LLMs just isn’t that reliable.
I should mention that I don’t keep up with the vast majority of alignment research. There might be some convincing research about why the lack-of-detected-misalignment means there is genuinely no misalignment, and I just missed it; but my guess is if that research existed, then I would’ve heard about it (e.g. it would’ve gotten tons of upvotes on LessWrong, b/c that would be a really important finding). So when Leike claims that models are becoming more aligned, most of my subjective probability for why he said that is that he doesn’t understand the difference between detected misalignment and actual misalignment.
A couple other relevant bits of evidence:
AI psychosis. When you ask LLMs point blank if it’s bad to induce AI psychosis, they say yes, and (AFAIK) there is no evidence that they’re being deceptive, and yet they do it anyway. This is a case where (IIRC) recent models have gotten better (less psychosis-prone), but my guess is that training psychosis out of LLMs doesn’t generalize to other forms of misalignment, and maybe in the next-gen models some new form of bad behavior will emerge.
IMO the best evidence of bad behavior by LLMs is coming out of Palisade, not any AI company. This suggests that AI companies (who have way more resources and access) are not trying sufficiently hard to detect misalignment. The implication is that, if AI companies fail to detect misalignment, this is only weak evidence that the misalignment isn’t there.