I don’t think this has much direct application to alignment, because although you can build safe AI with it, it doesn’t differentially get us towards the endgame of AI that’s trying to do good things and not bad things. But it’s still an interesting question.
It seems like the way you’re thinking about this, there’s some directed relations you care about (the main one being “this is like that, but with some extra details”) between concepts, and something is “real”/”applied” if it’s near the edge of this network—if it doesn’t have many relations directed towards even-more-applied concepts. It seems like this is the sort of thing you could only ever learn by learning about the real world first—you can’t start from a blank slate and only learn “the abstract stuff”, because you only know which stuff is abstract by learning about its relationships to less abstract stuff.
It seems like this is the sort of thing you could only ever learn by learning about the real world first
Yep. The idea is to try and get a system that develops all practically useful “theoretical” abstractions, including those we haven’t discovered yet, without developing desires about the real world. So we train some component of it on the real-world data, then somehow filter out “real-world” stuff, leaving only a purified superhuman abstract reasoning engine.
One of the nice-to-have properties here would be is if we don’t need to be able to interpret its world-model to filter out the concepts – if, in place of human understanding and judgement calls, we can blindly use some ground-truth-correct definition of what is and isn’t a real-world concept.
I don’t think this has much direct application to alignment, because although you can build safe AI with it, it doesn’t differentially get us towards the endgame of AI that’s trying to do good things and not bad things. But it’s still an interesting question.
It seems like the way you’re thinking about this, there’s some directed relations you care about (the main one being “this is like that, but with some extra details”) between concepts, and something is “real”/”applied” if it’s near the edge of this network—if it doesn’t have many relations directed towards even-more-applied concepts. It seems like this is the sort of thing you could only ever learn by learning about the real world first—you can’t start from a blank slate and only learn “the abstract stuff”, because you only know which stuff is abstract by learning about its relationships to less abstract stuff.
Yep. The idea is to try and get a system that develops all practically useful “theoretical” abstractions, including those we haven’t discovered yet, without developing desires about the real world. So we train some component of it on the real-world data, then somehow filter out “real-world” stuff, leaving only a purified superhuman abstract reasoning engine.
One of the nice-to-have properties here would be is if we don’t need to be able to interpret its world-model to filter out the concepts – if, in place of human understanding and judgement calls, we can blindly use some ground-truth-correct definition of what is and isn’t a real-world concept.