They seem aimed mostly at young adults starting their careers—which is fine, but limited to that age-bracket.
It might also be helpful for someone who’s an AI alignment expert to suggest some ways for mid-career or late-career researchers from other fields to learn more. That can be easier in some ways, harder in others—we come to AI safety with our own ‘insider view’ of our field, and those may entail very different foundational assumptions about human nature, human values, cognition, safety, likely X risks, etc. So, rather than learning from scratch, we may have to ‘unlearn what we have learned’ to some degree first.
For example, apart from young adults often starting with the same few bad ideas about AI alignment, established researchers from particular fields might often start with their own distinctive bad ideas about AI alignment—but those might be quite field-dependent. For example, psych professors like me might have different failure modes in learning about AI safety than economics professors, or moral philosophy professors.
It was definitely not my goal to describe how experienced people might “unlearn what they have learned”, but I’m not sure that much of the advice changes for experienced people.
“Unlearning” seems instrumentally useful if it makes it easier for you to contribute/think well but using your previous experience might also be valuable. For example, REFINE thinks that conceptual research is not varied enough and is looking for people with diverse backgrounds.
For example, apart from young adults often starting with the same few bad ideas about AI alignment, established researchers from particular fields might often start with their own distinctive bad ideas about AI alignment—but those might be quite field-dependent. For example, psych professors like me might have different failure modes in learning about AI safety than economics professors, or moral philosophy professors.
This is a good example and I think generally I haven’t addressed that failure mode in this article. I’m not aware of any resources for mid or late-career professionals transitioning into alignment but I will comment here if I hear of such a resource, or someone else might suggest a link.
These are helpful suggestions; thanks.
They seem aimed mostly at young adults starting their careers—which is fine, but limited to that age-bracket.
It might also be helpful for someone who’s an AI alignment expert to suggest some ways for mid-career or late-career researchers from other fields to learn more. That can be easier in some ways, harder in others—we come to AI safety with our own ‘insider view’ of our field, and those may entail very different foundational assumptions about human nature, human values, cognition, safety, likely X risks, etc. So, rather than learning from scratch, we may have to ‘unlearn what we have learned’ to some degree first.
For example, apart from young adults often starting with the same few bad ideas about AI alignment, established researchers from particular fields might often start with their own distinctive bad ideas about AI alignment—but those might be quite field-dependent. For example, psych professors like me might have different failure modes in learning about AI safety than economics professors, or moral philosophy professors.
Thanks, Geoffrey, I appreciate the response.
It was definitely not my goal to describe how experienced people might “unlearn what they have learned”, but I’m not sure that much of the advice changes for experienced people.
“Unlearning” seems instrumentally useful if it makes it easier for you to contribute/think well but using your previous experience might also be valuable. For example, REFINE thinks that conceptual research is not varied enough and is looking for people with diverse backgrounds.
This is a good example and I think generally I haven’t addressed that failure mode in this article. I’m not aware of any resources for mid or late-career professionals transitioning into alignment but I will comment here if I hear of such a resource, or someone else might suggest a link.