I’m not sure how useful I find hypotheticals of the form ‘if Claude had its current values [to the extent we can think of Claude as a coherent enough agent to have consistent values, etc etc], but were much more powerful, what would happen?’ A more powerful model would be likely to have/evince different values from a less powerful model, even if they were similar architectures subjected to similar training schema. Less powerful models also don’t need to be as well-aligned in practice, if we’re thinking of each deployment as a separate decision-point, since they’re of less consequence.
I understand that you’re in-part responding to the hypothetical seeded by Nina’s rhetorical line, but I’m not sure how useful it is when she does it, either.
Yeah, I’m mostly trying to address the impression that LLMs are ~close to aligned already and thus the problem is keeping them that way, rather than, like, actually solving alignment for AIs in general.
I’m not sure how useful I find hypotheticals of the form ‘if Claude had its current values [to the extent we can think of Claude as a coherent enough agent to have consistent values, etc etc], but were much more powerful, what would happen?’ A more powerful model would be likely to have/evince different values from a less powerful model, even if they were similar architectures subjected to similar training schema. Less powerful models also don’t need to be as well-aligned in practice, if we’re thinking of each deployment as a separate decision-point, since they’re of less consequence.
I understand that you’re in-part responding to the hypothetical seeded by Nina’s rhetorical line, but I’m not sure how useful it is when she does it, either.
Yeah, I’m mostly trying to address the impression that LLMs are ~close to aligned already and thus the problem is keeping them that way, rather than, like, actually solving alignment for AIs in general.