[TeamSlytherin]

That's just what an ML expert would say ;p Problem solvers, maximizers, and utility functions all go in the waste bin when working on AGI. And the problem peals away into other large "hard problems," like the nature of consciousness. NLP can just follow rules, but language understanding (before even reaching some general, high school level), requires knowledge outside of language itself. That leads to questions about embodiment and phenomenal consciousness, p-zombies, and the like. If it was an easy problem to encapsulate, it would have been "solved" by now.

[goldenkey]

The kind of AI being trained now aren't given the mechanisms of data space traversal/attention. Recently, attention mechanisms are being focused on by google. An AGI needs to learn that it can affect the system - dependent decision theory factors in here too.

Also, growth may be hugely important. Babies start out with fuzzy learning, almost as if the learning rate starts out very small which normalizes the lack of knowledge and elevated novelty/variance of the environment.

AGI is all about predicting future utility given a circular dependency between the agent and environment. QM says we can't solve this exactly.. it's a two object interaction.. no way to gain the joint state, the ground truth, assumptions always have to be made to approximate independence.