This is just capabilities stuff. I expect that people will use this to train larger networks, as much larger as they can. If your method shrinks the model, it likely induces demand proportionately. In this case it’s not new capabilities stuff by you so it’s less concerning, bit still. This paper is popular because of bees
I’d be pretty surprised if DLGNs became the mainstream way to train NNs, because although they make inference faster they apparently make training slower. Efficient training is arguably more dangerous than efficient inference anyway, because it lets you get novel capabilities sooner. To me, DLGN seems like a different method of training models but not necessarily a better one (for capabilities).
Anyway, I think it can be legitimate to try to steer the AI field towards techniques that are better for alignment/interpretability even if they grant non-zero benefits to capabilities. If you research a technique that could reduce x-risk but can’t point to any particular way it could be beneficial in the near term, it can be hard to convince labs to actually implement it. Of course, you want to be careful about this.
I buy that training slower is a sufficiently large drawback to break scaling. I still think bees are why the paper got popular. But if intelligence depends on clean representation, interpretability due to clean representation is natively and unavoidably bees. We might need some interpretable-bees insights in order to succeed, it does seem like we could get better regret bound proofs (or heuristic arguments) that go through a particular trained model with better (reliable, clean) interp. But the whole deal is the ai gets to exceed us in ways that make human interpreting stuff inherently (as opposed to transiently or fixably) too slow. To be useful durably, interp must become a component in scalably constraining an ongoing training/optimization process. Which means it’s gonna be partly bees in order to be useful. Which means it’s easy to accidentally advance bees more than durable alignment. Not a new problem, and not one with an obvious solution, but occasionally I see something I feel like i wanna comment on.
I was a big disagree vote because of induced demand. You’ve convinced me this paper induces less demand in this version than I worried (I had just missed that it trained slower), but my concern that something like this scales and induces demand remains.
This is just capabilities stuff. I expect that people will use this to train larger networks, as much larger as they can. If your method shrinks the model, it likely induces demand proportionately. In this case it’s not new capabilities stuff by you so it’s less concerning, bit still. This paper is popular because of bees
I’d be pretty surprised if DLGNs became the mainstream way to train NNs, because although they make inference faster they apparently make training slower. Efficient training is arguably more dangerous than efficient inference anyway, because it lets you get novel capabilities sooner. To me, DLGN seems like a different method of training models but not necessarily a better one (for capabilities).
Anyway, I think it can be legitimate to try to steer the AI field towards techniques that are better for alignment/interpretability even if they grant non-zero benefits to capabilities. If you research a technique that could reduce x-risk but can’t point to any particular way it could be beneficial in the near term, it can be hard to convince labs to actually implement it. Of course, you want to be careful about this.
What do you mean?
I buy that training slower is a sufficiently large drawback to break scaling. I still think bees are why the paper got popular. But if intelligence depends on clean representation, interpretability due to clean representation is natively and unavoidably bees. We might need some interpretable-bees insights in order to succeed, it does seem like we could get better regret bound proofs (or heuristic arguments) that go through a particular trained model with better (reliable, clean) interp. But the whole deal is the ai gets to exceed us in ways that make human interpreting stuff inherently (as opposed to transiently or fixably) too slow. To be useful durably, interp must become a component in scalably constraining an ongoing training/optimization process. Which means it’s gonna be partly bees in order to be useful. Which means it’s easy to accidentally advance bees more than durable alignment. Not a new problem, and not one with an obvious solution, but occasionally I see something I feel like i wanna comment on.
I was a big disagree vote because of induced demand. You’ve convinced me this paper induces less demand in this version than I worried (I had just missed that it trained slower), but my concern that something like this scales and induces demand remains.
Capabilities → capabees → bees