Exactly. It seems like you need something beyond present imitation learning and deep reinforcement learning to efficiently learn strategies whose individual components don’t benefit you, but which have a major effect if assembled perfectly together.
(I mean, don’t underestimate gradient descent with huge numbers of trials—the genetic version did evolve a complicated eye in such a way that every step was a fitness improvement; but the final model has a literal blind spot that could have been avoided if it were engineered in another way.)
Genetic algorithms also eventually evolved causal reasoning agents, us. That’s why it feels weird to me that we’re once again relying on gradient descent to develop AI—it seems backwards.
Exactly. It seems like you need something beyond present imitation learning and deep reinforcement learning to efficiently learn strategies whose individual components don’t benefit you, but which have a major effect if assembled perfectly together.
(I mean, don’t underestimate gradient descent with huge numbers of trials—the genetic version did evolve a complicated eye in such a way that every step was a fitness improvement; but the final model has a literal blind spot that could have been avoided if it were engineered in another way.)
Genetic algorithms also eventually evolved causal reasoning agents, us. That’s why it feels weird to me that we’re once again relying on gradient descent to develop AI—it seems backwards.