Curated. I have wanted someone to write out an assessment of how the Risks from Learned Optimization arguments hold up in light of the evidence we have acquired over the last half decade. I particularly appreciated breaking down the potential reasons for risk and assessing to what degree we have encountered each problem, as well as reassessing the chances of running into those problems. I would love to see more posts that take arguments/models/concepts from before 2020, consider what predictions we should have made pre-2020 if these arguments/models/concepts were good, and then reassess them in light of our observations of progress in ML over the last five years.
Curated. I have wanted someone to write out an assessment of how the Risks from Learned Optimization arguments hold up in light of the evidence we have acquired over the last half decade. I particularly appreciated breaking down the potential reasons for risk and assessing to what degree we have encountered each problem, as well as reassessing the chances of running into those problems. I would love to see more posts that take arguments/models/concepts from before 2020, consider what predictions we should have made pre-2020 if these arguments/models/concepts were good, and then reassess them in light of our observations of progress in ML over the last five years.