OK, cool. How do you think generalization works? I thought the idea was that instead of finding a specific technique that only works on the data you were trained on, sufficiently big NN’s trained on sufficiently diverse data end up finding more general techniques that work on that data + other data that is somewhat different.
Generalization ability is a key metric for AGI, which I expect to go up before the end; like John said the kinds of AI we care about are the kinds that are pretty good at generalizing, meaning that they ARE close to the “fully general” end of the spectrum, or at least close enough that whatever they are doing can be retargeted to lots of other environments and tasks besides the exact ones they were trained on. Otherwise, they wouldn’t be AGI.
humans, despite being fully general, have vastly varying ability to do various tasks, e.g. they’re much better at climbing mountains than playing GO it seems. Humans also routinely construct entirely technology bases to enable them to do tasks that they cannot do themselves. This is, in some sense, a core human economic activity: the construction of artifacts that can do tasks better/faster/more efficiently than humans can do themselves. It seems like by default, you should expect a similar dynamic with “fully general” AIs. That is, AIs trained to do semiconductor manufacturing will create their own technology bases, specialized predictive artifacts, etc. and not just “think really hard” and “optimize within their own head.” This also suggests a recursive form of the alignment problem, where an AI that wants to optimize human values is in a similar situation to us, where it’s easy to construct powerful artifacts with SGD that optimize measurable rewards, but it doesn’t know how to do that for human values/things that can’t be measured.
Even if you’re selecting reasonably hard for “ability to generalize” by default, the range of tasks you’re selecting for aren’t all going to be “equally difficult”, and you’re going to get an AI that is much better at some tasks than other tasks, has heuristics that enable it to accurately predict key intermediates across many tasks, heuristics that enable it to rapidly determine quick portions of the action space are even feasible, etc. Asking that your AI can also generalize to “optimize human values” aswell as the best avaliable combination of skills that it has otherwise seems like a huge ask. Humans, despite being fully general, find it much harder to optimize for some things than others, e.g. constructing large cubes of iron versus status seeking, despite being able to in theory optimize for constructing large cubes of iron.
Nobody is asking that the AI can also generalize to “optimize human values as well as the best available combination of skills it has otherwise...” at least, I wasn’t asking that. (At no point did I assume that fully general means ‘equally good’ at all tasks. I am not even sure such comparisons can be made.) But now rereading your comments it seems you were all along, since you brought up competitiveness worries. So now maybe I understand you better: you are assuming a hypercompetitive takeoff in which if there are AIs running around optimized to play the training game or something, and then we use interpretability tools to intervene on some of them and make them optimize for long-run human values instead, they won’t be as good at it as they were at playing the training game, even though they will be able to do it (compare: humans can optimize for constructing large cubes of iron, but they aren’t as good at it as they are at optimizing for status) and so they’ll lose competitions to the remaining AIs that haven’t been modified?
(My response to this would be ah, this makes sense, but I don’t expect there to be this much competition so I’m not bothered by this problem. I think if we have the interpretability tools we’ll probably be able to retarget the search of all relevant AIs, and then they’ll optimize for human values inefficiently but well enough to save the day.)
I think competitiveness matters a lot even if there’s only moderate amounts of competitive pressure. The gaps in efficiency I’m imagining are less “10x worse” and more like “I only had support vector machines and you had SGD”
OK, cool. How do you think generalization works? I thought the idea was that instead of finding a specific technique that only works on the data you were trained on, sufficiently big NN’s trained on sufficiently diverse data end up finding more general techniques that work on that data + other data that is somewhat different.
Generalization ability is a key metric for AGI, which I expect to go up before the end; like John said the kinds of AI we care about are the kinds that are pretty good at generalizing, meaning that they ARE close to the “fully general” end of the spectrum, or at least close enough that whatever they are doing can be retargeted to lots of other environments and tasks besides the exact ones they were trained on. Otherwise, they wouldn’t be AGI.
Would you agree with that? I assume not...
humans, despite being fully general, have vastly varying ability to do various tasks, e.g. they’re much better at climbing mountains than playing GO it seems. Humans also routinely construct entirely technology bases to enable them to do tasks that they cannot do themselves. This is, in some sense, a core human economic activity: the construction of artifacts that can do tasks better/faster/more efficiently than humans can do themselves. It seems like by default, you should expect a similar dynamic with “fully general” AIs. That is, AIs trained to do semiconductor manufacturing will create their own technology bases, specialized predictive artifacts, etc. and not just “think really hard” and “optimize within their own head.” This also suggests a recursive form of the alignment problem, where an AI that wants to optimize human values is in a similar situation to us, where it’s easy to construct powerful artifacts with SGD that optimize measurable rewards, but it doesn’t know how to do that for human values/things that can’t be measured.
Even if you’re selecting reasonably hard for “ability to generalize” by default, the range of tasks you’re selecting for aren’t all going to be “equally difficult”, and you’re going to get an AI that is much better at some tasks than other tasks, has heuristics that enable it to accurately predict key intermediates across many tasks, heuristics that enable it to rapidly determine quick portions of the action space are even feasible, etc. Asking that your AI can also generalize to “optimize human values” aswell as the best avaliable combination of skills that it has otherwise seems like a huge ask. Humans, despite being fully general, find it much harder to optimize for some things than others, e.g. constructing large cubes of iron versus status seeking, despite being able to in theory optimize for constructing large cubes of iron.
Nobody is asking that the AI can also generalize to “optimize human values as well as the best available combination of skills it has otherwise...” at least, I wasn’t asking that. (At no point did I assume that fully general means ‘equally good’ at all tasks. I am not even sure such comparisons can be made.) But now rereading your comments it seems you were all along, since you brought up competitiveness worries. So now maybe I understand you better: you are assuming a hypercompetitive takeoff in which if there are AIs running around optimized to play the training game or something, and then we use interpretability tools to intervene on some of them and make them optimize for long-run human values instead, they won’t be as good at it as they were at playing the training game, even though they will be able to do it (compare: humans can optimize for constructing large cubes of iron, but they aren’t as good at it as they are at optimizing for status) and so they’ll lose competitions to the remaining AIs that haven’t been modified?
(My response to this would be ah, this makes sense, but I don’t expect there to be this much competition so I’m not bothered by this problem. I think if we have the interpretability tools we’ll probably be able to retarget the search of all relevant AIs, and then they’ll optimize for human values inefficiently but well enough to save the day.)
I think competitiveness matters a lot even if there’s only moderate amounts of competitive pressure. The gaps in efficiency I’m imagining are less “10x worse” and more like “I only had support vector machines and you had SGD”