AI ontology crises: an informal typology

I'm fairly skeptical about trying to understand AI behavior at this level, given the current state of affairs (that is, I think the implicit picture of AI behavior on which these analyses rely is quite unlikely, so that the utility of this sort of thinking is reduced by an order of magnitude). Anyway, some specific notes:

1. The utility scrambled situation is probably as dangerous as more subtle perturbations if you are dealing with human-level AI, as keeping human onlookers happy is instrumentally valuable (and this sort of reasoning is obvious to an AI as clever as we are on this axis, never mind one much smarter).

2. The presumed AI architecture involves human designers specifying a prior and utility function over the same ontology, which seems quite unlikely from here. In more realistic situations, the question of value generalization seems important beyond ontological crises, and in particular if it goes well before reaching an ontological crisis it seems overwhelmingly likely to continue to go well.

3. An AI of the sort you envision (with a prior and a utility function specified in the ontology of that prior) can never abandon its ontology. It will instead either become increasingly confused, or build a model for its observations in the original ontology (if the prior is sufficiently expressive). In both cases the utility function continues to apply without change, in contrast to the situation in de Blanc's paper (where an AI is explicitly shifting from one ontology to another). If the utility function was produced by human designers it may no longer correspond with reality in the intended way.

4. It seems extremely unlikely that an AI with very difficult to influence values will be catatonic. More likely hypotheses suggest themselves, such as: doing things that would be good in (potentially unlikely worlds) where value is more easily influenced, amassing resources to better understand whether value can be influenced, or having behavior controlled in apparently random (but quite likely extremely destructive) ways that give a tiny probabilistic edge. For only very rare values will killing yourself be a good play (since this suggests utility can be influenced by killing yourself, but not by doing anything more extreme).

The rest are unrelated to the substance of the post, except insofar as they relate to the general mode of thinking:

1. As far as I can tell, AI indifference doesn't work (see my comment here). I don't think it is salvageable, but even if it is it at least seems to require salvaging.

2. Note that depending on the structure of "evidence for goals" in the value indifference proposal, it is possible that an AI can in fact purposefully influence its utility function and will be motivated to do so. To see that the proof sketch given doesn't work, notice that I have some probability distribution over what I will be doing in a year, but that (despite the fact that this "obeys the axioms of probability") I can in fact influence the result and not just passively learn more about it. An agent in this framework is automatically going to be concerned with acausal control of its utility function, if its notion of evidence is sufficiently well-developed. I don't know if this is an issue.

If the ontological crisis is too severe, the AI may lose the ability to do anything at all, as the world becomes completely incomprehensible to it.

unless the AI is an entropy genie, it cannot influence utility values through its decisions, and will most likely become catatonic

Seems unwarrantedly optimistically anthropomorphic. A controlled shutdown in a case like this is a good outcome, but imagining a confused human spinning around and falling over does not make it so. The AI would exhibit undefined behavior, and hoping that this behavior is incoherent enough to be harmless or that it would drop an anvil on its own head seems unwarrantedly optimistic if that wasn't an explicit design consideration. Obviously undefined behavior is implementation-dependent but I'd expect that in some cases you would see e.g. subsystems running coherently and perhaps effectively taking over behavior as high-level directions ceased to provide strong utility differentials. In other words, the AI built an automatic memory-managing subsystem inside itself that did some degree of consequentialism but in a way that was properly subservient to the overall preference function; now the overall preference function is trashed and the memory manager is what's left to direct behavior. Some automatic system goes on trying to rewrite and improve code, and it gets advice from the memory manager but not from top-level preferences; thus the AI ends up as a memory-managing agent.

This probably doesn't make sense the way I wrote it, but the general idea is that the parts of the AI could easily go on carrying out coherent behaviors, and that could easily end up somewhere coherent and unpleasant, if top-level consequentialism went incoherent. Unless controlled shutdown in that case had somehow been imposed from outside as a desirable conditional consequence, using a complexly structured utility function such that it would evaluate, "If my preferences are incoherent then I want to do a quiet, harmless shutdown, and that doesn't mean optimize the universe for maximal quietness and harmlessness either." Ordinarily, an agent would evaluate, "If my utility function goes incoherent... then I must not want anything in particular, including a controlled shutdown of my code."

Why don't we see these crises happening in humans when they shift ontological models? Is there some way we can use the human intelligence case as a model to guide artificial intelligence safeguards?

I read through your post twice and still did not find the point you are presumably trying to make. Is it something along the lines of "Effects of deviation from FAI on observable utility function"? No, probably not it.

Adding a summary might be a good idea.

Is this entirely preventable by allowing the AI to do full inductive inference approximation with no hard-coded ontology and putting all values in terms of algorithms that must be run on the substrate that is reality?