For me, this is the paper where I learned to connect ideas about delegation to machine learning. The paper sets up simple ideas of mesa-optimizers, and shows a number of constraints and variables that will determine how the mesa-optimizers will be developed – in some environments you want to do a lot of thinking in advance then delegate execution of a very simple algorithm to do your work (e.g. this simple algorithm Critch developed that my group house uses to decide on the rent for each room), and in some environments you want to do a little thinking and then delegate a very complex algorithm to figure out what to do (e.g. evolution is very stupid and then makes very complex brains to figure out what to do in lots of situations that humans encountered in the EEA).
Seeing this more clearly in ML shocked me with the level of inadequacy that ML has for being able to do this with much direction whatsoever. It just doesn’t seem like something that we have much control of. Of course I may be wrong, and there are some simple proposals (though that have not worked so far). Nonetheless, it’s a substantial step forward in discussing delegation in modern ML systems. It discusses lots of related ideas very clearly.
Definitely should be included in the review. I expect to vote on this with something like +5 to +8.
I don’t do research in this area, I expect others like Daniel Filan and Adam Shimi will have more detailed opinions of the sequence’s strengths and weaknesses. (Nonetheless I stand by my assessment and will vote accordingly.)
For me, this is the paper where I learned to connect ideas about delegation to machine learning. The paper sets up simple ideas of mesa-optimizers, and shows a number of constraints and variables that will determine how the mesa-optimizers will be developed – in some environments you want to do a lot of thinking in advance then delegate execution of a very simple algorithm to do your work (e.g. this simple algorithm Critch developed that my group house uses to decide on the rent for each room), and in some environments you want to do a little thinking and then delegate a very complex algorithm to figure out what to do (e.g. evolution is very stupid and then makes very complex brains to figure out what to do in lots of situations that humans encountered in the EEA).
Seeing this more clearly in ML shocked me with the level of inadequacy that ML has for being able to do this with much direction whatsoever. It just doesn’t seem like something that we have much control of. Of course I may be wrong, and there are some simple proposals (though that have not worked so far). Nonetheless, it’s a substantial step forward in discussing delegation in modern ML systems. It discusses lots of related ideas very clearly.
Definitely should be included in the review. I expect to vote on this with something like +5 to +8.
I don’t do research in this area, I expect others like Daniel Filan and Adam Shimi will have more detailed opinions of the sequence’s strengths and weaknesses. (Nonetheless I stand by my assessment and will vote accordingly.)