Might be worth thinking about / comparing how and why things went wrong to produce the 2007⁄8 GFC. iirc credit raters had misaligned incentives that rhyme with this question/post.
David Lorell
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Why Not Subagents?
Lessons On How To Get Things Right On The First Try
Disclaimer: At the time of writing, this has not been endorsed by Evan.
I can give this a go.
Unpacking Evan’s Comment:
My read of Evan’s comment (the parent to yours) is that there are a bunch of learned high-level-goals (“strategies”) with varying levels of influence on the tactical choices made, and that a well-functioning end-to-end credit-assignment mechanism would propagate through action selection (“thoughts directly related to the current action” or “tactics”) all the way to strategy creation/selection/weighting. In such a system, strategies which decide tactics which emit actions which receive reward are selected for at the expense of strategies less good at that. Conceivably, strategies aiming directly for reward would produce tactical choices more highly rewarded than strategies not aiming quite so directly.
One way for this not to be how humans work would be if reward did not propagate to the strategies, and they were selected/developed by some other mechanism while reward only honed/selected tactical cognition. (You could imagine that “strategic cognition” is that which chooses bundles of context-dependent tactical policies, and “tactical cognition” is that which implements a given tactic’s choice of actions in response to some context.) This feels to me close to what Evan was suggesting you were saying is the case with humans.
One Vaguely Mechanistic Illustration of a Similar Concept:
A similar way for this to be broken in humans, departing just a bit from Evan’s comment, is if the credit assignment algorithm could identify tactical choices with strategies, but not equally reliably across all strategies. As a totally made up concrete and stylized illustration: Consider one evolutionarily-endowed credit-assignment-target: “Feel physically great,” and two strategies: wirehead with drugs (WIRE), or be pro-social (SOCIAL.) Whenever WIRE has control, it emits some tactic like “alone in my room, take the most fun available drug” which takes actions that result in physical pleasure over a day. Whenever SOCIAL has control, it emits some tactic like “alone in my room, abstain from dissociative drugs and instead text my favorite friend” taking actions which result in physical pleasure over a day.
Suppose also that asocial cognitions like “eat this” have poorly wired feed-back channels and the signal is often lost and so triggers credit-assignment only some small fraction of the time. Social cognition is much better wired-up and triggers credit-assignment every time. Whenever credit assignment is triggered, once a day, reward emitted is 1:1 with the amount of physical pleasure experienced that day.
Since WIRE only gets credit a fraction of the time that it’s due, the average reward (over 30 days, say) credited to WIRE is . If and only if , like if the drug is heroin or your friends are insufficiently fulfilling, WIRE will be reinforced more relative to SOCIAL. Otherwise, even if the drug is somewhat more physically pleasurable than the warm-fuzzies of talking with friends, SOCIAL will be reinforced more relative to WIRE.
Conclusion:
I think Evan is saying that he expects advanced reward-based AI systems to have no such impediments by default, even if humans do have something like this in their construction. Such a stylized agent without any signal-dropping would reinforce WIRE over SOCIAL every time that taking the drug was even a tiny bit more physically pleasurable than talking with friends.
Maybe there is an argument that such reward-aimed goals/strategies would not produce the most rewarding actions in many contexts, or for some other reason would not be selected for / found in advanced agents (as Evan suggests in encouraging someone to argue that such goals/strategies require concepts which are unlikely to develop,) but the above might be in the rough vicinity of what Evan was thinking.
REMINDER: At the time of writing, this has not been endorsed by Evan.
This feels like stepping on a rubber duck while tip-toeing around sleeping giants but:
Don’t these analogies break if/when the complexity of the thing to generate/verify gets high enough? That is, unless you think the difficulty of verification of arbitrarily complex plans/ideas is asymptotic to some human-or-lower level of verification capability (which I doubt you do) then at some point humans can’t even verify the complex plan.So, the deeper question just seems to be takeoff speeds again: If takeoff is too fast, we don’t have enough time to use “weak” AGI to help produce actually verifiable plans which solve alignment. If takeoff is slow enough, we might. (And if takeoff is too fast, we might not notice that we’ve passed the point of human verifiability until it’s too late.)
(I am consciously not bringing up ideas about HCH / other oversight-amplification ideas because I’m new to the scene and don’t feel familiar enough with them.)
But I’m not really accusing y’all of saying “try to produce a future that has no basis in human values.” I am accusing this post of saying “there’s some neutral procedure for figuring out human values, we should use that rather than a non-neutral procedure.”
My read was more “do the best we can to get through the acute risk period in a way that lets humanity have the time and power to do the best it can at defining/creating a future full of value.” And that’s in response and opposed to positions like “figure out / decide what is best for humanity (or a procedure that can generate the answer to that) and use that to shape the long term future.”
The point is that as moral attitudes/thoughts change, societies or individuals which exist long enough will likely come to regret permanently structuring the world according to the morality of a past age. The Roman will either live to regret it, or the society that follows the Roman will come to regret it even if the Roman dies happy, or the AI is brainwashing everyone all the time to prevent moral progress. The analogy breaks down a bit with the third option since I’d guess most people today would not accept it as a success and it’s today’s(ish) morals that might get locked in, not ancient Rome’s.
Some nits we know about but didn’t include in the problems section:
P[mushroom->anchovy] = 0. The current argument does not handle the case where subagents believe that there is a probability of 0 on one of the possible states. It wouldn’t be possible to complete the preferences exactly as written, then.
Indifference. If anchovy were placed directly above mushroom in the preference graph above (so that John is truly indifferent between them), then that might require some special handling. But also it might just work if the “Value vs Utility” issue is worked out. If the subagents are not myopic / handle instrumental values, then whether anchovy is less, identically, or more desirable than mushroom doesn’t really matter so much on its own as opposed to what opportunities are possible afterward from the anchovy state relative to the mushroom state.
Also, I think I buy the following part but I really wish it were more constructive.