Without causal assumptions or taking actions, it is simply not possible to deduce the correct causal model.
I think this is true, but you’re eliding how accessible causal assumptions can be. The granddaddy of all causal assumptions is that causes generally precede effects. Another useful one for an AI is “humans are usually right about the causal language they use.”
Another way to say that second one is semantic: “the language the AI uses to describe causes should usually match the language the humans use to describe similar causes.” Causation doesn’t have to be ontologically basic, we just have to be on the same page about it.
In that case it isn’t a scientist AI but rather a knowledge indexing AI. It can pull together causal concepts that humans have written about, but without RL-style interaction with the environment, it’s not a scientist.
Causes preceding effects is generally true but it’s not going to help you solve much.
I think this is true, but you’re eliding how accessible causal assumptions can be. The granddaddy of all causal assumptions is that causes generally precede effects. Another useful one for an AI is “humans are usually right about the causal language they use.”
Another way to say that second one is semantic: “the language the AI uses to describe causes should usually match the language the humans use to describe similar causes.” Causation doesn’t have to be ontologically basic, we just have to be on the same page about it.
In that case it isn’t a scientist AI but rather a knowledge indexing AI. It can pull together causal concepts that humans have written about, but without RL-style interaction with the environment, it’s not a scientist.
Causes preceding effects is generally true but it’s not going to help you solve much.