Uh, apologies, I meant steepest gradients. The SGD is a locally greedy optimization process that updates a ML model’s parameters in the direction of the highest local increase in performance, i. e. along the steepest gradients. I’m saying that once there’s a general-purpose problem-solving algorithm represented somewhere in the ML model, the SGD (or evolution, or whatever greedy selection algorithm we’re using) would by default attempt to loop it into the model’s own problem-solving, because that would increase its performance the most. Even if it was assembled accidentally, or as part of the world-model and not the ML model’s own policy.
I’m not talking about a simulacrum breaking out.
Uh, apologies, I meant steepest gradients. The SGD is a locally greedy optimization process that updates a ML model’s parameters in the direction of the highest local increase in performance, i. e. along the steepest gradients. I’m saying that once there’s a general-purpose problem-solving algorithm represented somewhere in the ML model, the SGD (or evolution, or whatever greedy selection algorithm we’re using) would by default attempt to loop it into the model’s own problem-solving, because that would increase its performance the most. Even if it was assembled accidentally, or as part of the world-model and not the ML model’s own policy.