I agree that FEP-shaped intuitions are very good for satisfice-ey agents. I’m unconvinced by the concrete mathematical modelling (notably not a fan of Bayesian generative models ) but I find the ideas conceptually useful if you abstract away the implementation.
My scepticism of general intelligence is closely related to your point that ASIs won’t infer every single law. Any given level of complexity in an organism can only acommodate a limited ontology. Of course, you can always “juice up” the agent and give it more resources so it learns a more textured world model. One pseudo-mathematical way to put this is that for every set of abstractions, there exists an abstraction that oblates all of them at once; for a fixed level of complexity however, there exist two sets of abstractions such that neither one clearly dominates.
Our crux might start at “some laws are convergently useful to infer”. One corollary of my last pseudo-mathematical claim is that any bounded agent has to “choose” between incomparable ontologies. The claim in my original post is that the revealed goals an agent is endowed with affects this choice. This amounts to advocating that a focus on the effect of selection pressures on learned abstractions will yield better predictions than a focus on finding “convergent” or “natural” abstractions.
quick addendum: my point feels spiritually related to the idea that “convergent evolution” is an incomplete concept without a specification of the attractor basin.
I agree that FEP-shaped intuitions are very good for satisfice-ey agents. I’m unconvinced by the concrete mathematical modelling (notably not a fan of Bayesian generative models ) but I find the ideas conceptually useful if you abstract away the implementation.
My scepticism of general intelligence is closely related to your point that ASIs won’t infer every single law. Any given level of complexity in an organism can only acommodate a limited ontology. Of course, you can always “juice up” the agent and give it more resources so it learns a more textured world model. One pseudo-mathematical way to put this is that for every set of abstractions, there exists an abstraction that oblates all of them at once; for a fixed level of complexity however, there exist two sets of abstractions such that neither one clearly dominates.
Our crux might start at “some laws are convergently useful to infer”. One corollary of my last pseudo-mathematical claim is that any bounded agent has to “choose” between incomparable ontologies. The claim in my original post is that the revealed goals an agent is endowed with affects this choice. This amounts to advocating that a focus on the effect of selection pressures on learned abstractions will yield better predictions than a focus on finding “convergent” or “natural” abstractions.
quick addendum: my point feels spiritually related to the idea that “convergent evolution” is an incomplete concept without a specification of the attractor basin.