Arguably, an aligned AI should be aligned to the user's prior as well as the user's utility function. Hence, any value-learning protocol should also be doing prior-learning. The problem is, any learning process requires (explicitly or implicitly) its own prior. But shouldn't this also be the user's prior? Is this an infinite regress? Maybe not: here is a way out that seems elegant in a way.
For now, we will work in the Bayesian framework. Let be the set of possible universes (the domain of our beliefs). Let be the kernel s.t. ... (read more)
Hi Vanessa! Thanks again for your previous answers. I've got one further concern.
Are all mesa-optimizers really only acausal attackers?
I think mesa-optimizers don't need to be purely contained in a hypothesis (rendering them acausal attackers), but can be made up of a part of the hypotheses-updating procedures (maybe this is obvious and you already considered it).
Of course, since the only way to change the AGI's actions is by changing its hypotheses, even these mesa-optimizers will have to alter hypothesis selection. But their whole running program doesn't need to be captured inside any hypothesis (which would be easier for classifying acausal attackers away).
That is, if we don't think about how the AGI updates its hypotheses, and just consider them magically updating (without any intermediate computations), then of course, the only mesa-optimizers will be inside hypotheses. If we actually think about these computations and consider a brute-force search over all hypotheses, then again they will only be found inside hypotheses, since the search algorithm itself is too simple and provides no further room for storing a subagent (even if the mesa-optimizer somehow takes advantage of the details of the search). But if more realistically our AGI employs more complex heuristics to ever-better approximate optimal hypotheses update, mesa-optimizers can be partially or completely encoded in those (put another way, those non-optimal methods can fail / be exploited). This failure could be seen as a capabilities failure (in the trivial sense that it failed to correctly approximate perfect search), but I think it's better understood as an alignment failure.
The way I see PreDCA (and this might be where I'm wrong) is as an "outer top-level protocol" which we can fit around any superintelligence of arbitrary architecture. That is, the superintelligence will only have to carry out the hypotheses update (plus some trivial calculations over hypotheses to find the best
3Vanessa Kosoy
First, no, the AGI is not going to "employ complex heuristics to ever-better approximate optimal hypotheses update". The AGI is going to be based on an algorithm which, as a mathematical fact (if not proved then at least conjectured), converges to the correct hypothesis with high probability. Just like we can prove that e.g. SVMs converge to the optimal hypothesis in the respective class, or that particular RL algorithms for small MDPs converge to the correct hypothesis (assuming realizability).
Second, there's the issue of non-cartesian attacks ("hacking the computer"). Assuming that the core computing unit is not powerful enough to mount a non-cartesian attack on its own, such attacks can arguably be regarded as detrimental side-effects of running computations on the envelope. My hope is that we can shape the prior about such side-effects in some informed way (e.g. the vast majority of programs won't hack the computer) s.t. we still have approximate learnability (i.e. the system is not too afraid to run computations) without misspecification (i.e. the system is not overconfident about the safety of running computations). The more effort we put into hardening the system, the easier it should be to find such a sweet spot.
Third, I hope that the agreement solution will completely rule out any undesirable hypothesis, because we will have an actual theorem that guarantees it. What are the exact assumptions going to be and what needs to be done to make sure these assumptions hold is work for the future, ofc.