Yes, I mentally skipped the part when you created “artificial lizard with RL architecture” (that was unexpected). Then, the argument collapses to the first part of the comment to which you are replying: gears-level is more precise, of course, but “birds-eye view” of Active Inference could give you the concepts for thinking about agency, persuadability (aka corrigibility), etc., without the need to re-invent them, and without spawning a plethora of concepts which don’t make sense in the abstract and are specific for each AI algorithm/architecture (such as, the concept of “reward” is not a fundamental concept of alignment, because it applies to RL agents, but doesn’t apply to LLMs, which are also agents).
Yes, I mentally skipped the part when you created “artificial lizard with RL architecture” (that was unexpected). Then, the argument collapses to the first part of the comment to which you are replying: gears-level is more precise, of course, but “birds-eye view” of Active Inference could give you the concepts for thinking about agency, persuadability (aka corrigibility), etc., without the need to re-invent them, and without spawning a plethora of concepts which don’t make sense in the abstract and are specific for each AI algorithm/architecture (such as, the concept of “reward” is not a fundamental concept of alignment, because it applies to RL agents, but doesn’t apply to LLMs, which are also agents).