Don’t focus on maximizing impact on participants; this is less important than reducing the mentorship bottleneck, which is best served by boosting the most advanced participants.
Could you clarify? Do you mean that if you have the chance to support someone new who would gain a lot since they haven’t participated in many AI safety programs or the chance to support someone more advanced, you’d suggest picking the later? With the reasoning being that the former might look like a better bet because of more room to make a difference, however boosting the latter increases the supply of mentors and therefore actually ends up benefiting beginners as least as much.
Yes, I would generally support picking the latter as they have a “faster time to mentorship/research leadership/impact” and the field seems currently bottlenecked on mentorship and research leads, not marginal engineers (though individual research leads might feel bottlenecked on marginal engineers).
We should prioritize people who already have research or engineering experience or a very high iteration speed as we are operating under time constraints; AGI is coming soon. Additionally, I think “research taste” will be more important than engineering ability given AI automation and this takes a long time to build; better to select people with existing research experience they can adapt from another field (also promotes interdisciplinary knowledge transfer).
Could you clarify? Do you mean that if you have the chance to support someone new who would gain a lot since they haven’t participated in many AI safety programs or the chance to support someone more advanced, you’d suggest picking the later? With the reasoning being that the former might look like a better bet because of more room to make a difference, however boosting the latter increases the supply of mentors and therefore actually ends up benefiting beginners as least as much.
Yes, I would generally support picking the latter as they have a “faster time to mentorship/research leadership/impact” and the field seems currently bottlenecked on mentorship and research leads, not marginal engineers (though individual research leads might feel bottlenecked on marginal engineers).
We should prioritize people who already have research or engineering experience or a very high iteration speed as we are operating under time constraints; AGI is coming soon. Additionally, I think “research taste” will be more important than engineering ability given AI automation and this takes a long time to build; better to select people with existing research experience they can adapt from another field (also promotes interdisciplinary knowledge transfer).
I talk more about it here.