I think that we basically have no way of ensuring that we get this nice “goals based on pointers to the correct concepts”/corrigible alignment thing using behavioral training. This seems like a super specific way to set up the AI, and there are so many degrees of freedom that behavioral training doesn’t distinguish.
For the Representation Engineering thing, I think the “workable” version of this basically looks like “Retarget the Search”, where you somehow do crazy good interp and work out where the “optimizer” is, and then point that at the right concepts which you also found using interp. And for some reason, the AI is set up such that you can “retarget it” with breaking everything. I expect if we don’t actually understand how “concepts” are represented in AIs and instead use something shallower (e.g. vectors or SAE neurons) then these will end up not being robust enough. I don’t expect RepE will actually change an AI’s goals if we have no idea how the goal-directness works in the first place.
I definitely don’t expect to be able to representation engineer our way into building an AI that is corrigible aligned, and remains that way even when it is learning a bunch of new things and is in very different distributions. (I do think that actually solving this problem would solve a large amount of the alignment problem)
What follows will all be pretty speculative, but I still think should probably provide some substantial evidence for more optimism.
I think that we basically have no way of ensuring that we get this nice “goals based on pointers to the correct concepts”/corrigible alignment thing using behavioral training. This seems like a super specific way to set up the AI, and there are so many degrees of freedom that behavioral training doesn’t distinguish.
The results in Robust agents learn causal world models suggest that robust models (to distribution shifts; arguably, this should be the case for ~all substantially x-risky models) should converge towards learning (approximately) the same causal world models. This talk suggests theoretical reasons to expect that the causal structure of the world (model) will be reflected in various (activation / rep engineering-y, linear) properties inside foundation models (e.g. LLMs), usable to steer them.
For the Representation Engineering thing, I think the “workable” version of this basically looks like “Retarget the Search”, where you somehow do crazy good interp and work out where the “optimizer” is, and then point that at the right concepts which you also found using interp. And for some reason, the AI is set up such that you can “retarget it” with breaking everything.
I definitely don’t expect to be able to representation engineer our way into building an AI that is corrigible aligned, and remains that way even when it is learning a bunch of new things and is in very different distributions. (I do think that actually solving this problem would solve a large amount of the alignment problem).
The activation / representation engineering methods might not necessarily need to scale that far in terms of robustness, especially if e.g. you can complement them with more control-y methods / other alignment methods / Swiss cheese models of safety more broadly; and also plausibly because they’d “only” need to scale to ~human-level automated alignment researchers / scaffolds of more specialized such automated researchers, etc. And again, based on the above theoretical results, future models might actually be more robustly steerable ‘by default’ / ‘for free’.
I think that we basically have no way of ensuring that we get this nice “goals based on pointers to the correct concepts”/corrigible alignment thing using behavioral training. This seems like a super specific way to set up the AI, and there are so many degrees of freedom that behavioral training doesn’t distinguish.
For the Representation Engineering thing, I think the “workable” version of this basically looks like “Retarget the Search”, where you somehow do crazy good interp and work out where the “optimizer” is, and then point that at the right concepts which you also found using interp. And for some reason, the AI is set up such that you can “retarget it” with breaking everything. I expect if we don’t actually understand how “concepts” are represented in AIs and instead use something shallower (e.g. vectors or SAE neurons) then these will end up not being robust enough. I don’t expect RepE will actually change an AI’s goals if we have no idea how the goal-directness works in the first place.
I definitely don’t expect to be able to representation engineer our way into building an AI that is corrigible aligned, and remains that way even when it is learning a bunch of new things and is in very different distributions. (I do think that actually solving this problem would solve a large amount of the alignment problem)
What follows will all be pretty speculative, but I still think should probably provide some substantial evidence for more optimism.
The results in Robust agents learn causal world models suggest that robust models (to distribution shifts; arguably, this should be the case for ~all substantially x-risky models) should converge towards learning (approximately) the same causal world models. This talk suggests theoretical reasons to expect that the causal structure of the world (model) will be reflected in various (activation / rep engineering-y, linear) properties inside foundation models (e.g. LLMs), usable to steer them.
I don’t think the “optimizer” ontology necessarily works super-well with LLMs / current SOTA (something like simulators seems to me much more appropriate); with that caveat, e.g. In-Context Learning Creates Task Vectors and Function Vectors in Large Language Models (also nicely summarized here), A Mechanistic Understanding of Alignment Algorithms: A Case Study on DPO and Toxicity seem to me like (early) steps in this direction already. Also, if you buy the previous theoretical claims (of convergence towards causal world models, with linear representations / properties), you might quite reasonably expect such linear methods to potentially work even better in more powerful / more robust models.
The activation / representation engineering methods might not necessarily need to scale that far in terms of robustness, especially if e.g. you can complement them with more control-y methods / other alignment methods / Swiss cheese models of safety more broadly; and also plausibly because they’d “only” need to scale to ~human-level automated alignment researchers / scaffolds of more specialized such automated researchers, etc. And again, based on the above theoretical results, future models might actually be more robustly steerable ‘by default’ / ‘for free’.
Haven’t read it as deeply as I’d like to, but Learning Interpretable Concepts: Unifying Causal Representation Learning and Foundation Models seems like potentially significant progress towards formalizing / operationalizing (some of) the above.