For any 2 of {reflectively stable, general, embedded}, I can satisfy those properties.
{reflectively stable, general} → do something that just rolls out entire trajectories of the world given different actions that it takes, and then has some utility function/preference ordering over trajectories, and selects actions that lead to the highest expected utility trajectory.
{general, embedded} → use ML/local search with enough compute to rehash evolution and get smart agents out.
{reflectively stable, embedded} → a sponge or a current day ML system.
For any 2 of {reflectively stable, general, embedded}, I can satisfy those properties.
{reflectively stable, general} → do something that just rolls out entire trajectories of the world given different actions that it takes, and then has some utility function/preference ordering over trajectories, and selects actions that lead to the highest expected utility trajectory.
{general, embedded} → use ML/local search with enough compute to rehash evolution and get smart agents out.
{reflectively stable, embedded} → a sponge or a current day ML system.