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Ok, so it seems clear that we are, for better or worse, likely going to try to get AGI to do our alignment homework.
Who has thought through all the other homework we might give AGI that is as good of an idea, assuming a model that isn't an instant-game-over for us? E.G., I remember @Buck rattling off a list of other ideas that he had in his The Curve talk, but I feel like I haven't seen the list of, e.g., "here are all the ways I would like to run an automated counterintelligence sweep of my organization" ideas.
(Yes, obviously, if the AI is sneakily misaligned, you're just dead because it will trick you into firing all your researchers, etc.; this is written in a "playing to your outs" mentality, not an "I endorse this as a good plan" mentality.)
I'm working on making sure we get high quality critical systems software out of early AGI. Hardened infrastructure buys us a lot in the slightly crazy story of "self-exfiltrated model attacks the power grid", but buys us even more in less crazy stories about all the software modules adjacent to AGI having vulnerabilities rapidly patched at crunchtime.
I favor proactive approaches to unlearning which prevent the target knowledge from being acquired in the first place. E.g. for gradient routing, if you can restrict "self-awareness and knowledge of how to corrupt metrics" to a particular submodule of the network during learning, then if that submodule isn't active, you can be reasonably confident that the metrics aren't currently being corrupted. (Even if that submodule sandbags and underrates its own knowledge, that should be fine if the devs know to be wary of it. Just ablate that submodule whenever you're measuring something that matters, regardless of whether your metrics say it knows stuff!)
Some related thoughts here
Unlearning techniques should probably be battle-tested in low-stakes "model organism" type contexts, where metrics corruption isn't expected.
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Curious what areas you are most excited about!
3Noosphere89
I basically agree with this, and on this question:
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My big areas of excitement are AI control (in a broad sense) and synthetic dataset making for AI alignment of successors.