[felippee]

I'm skeptical, and side with Rodney Brooks on this one. First, reinforcement learning is incredibly inefficient. And sure, humans and animals have forms of reinforcement learning, but my hunch it that it works on an already incredibly semantically relevant representation and utilize the forward model. That model is generated by unsupervised learning (which is way more data efficient). Actually I side with Yann Lecun on this one, see some of his recent talks. But Yann is not a robotics guy, so I don't think he fully appreciates the role of a forward model.

Now using models for RL is the obvious choice, since trying to teach a robot a basic behavior with RL is just absurdly impractical. But the problem here, is that when somebody build that model (a 3d simulations) they put in a bunch of stuff they think is relevant to represent the reality. And that is the same trap as labeling a dataset. We only put in the stuff which is symbolically relevant to us, omitting a bunch of low level things we never even perceive.

This is a longer subject, and a HN is not enough to cover it, but there is also something about the complexity. Reality is not just more complicated than simulation, it is complex with all the consequences of that. Every attempt to put a human filtered input between AI and the world will inherently loose that complexity and ultimately the AI will not be able to immunize itself to it.

This is not an easy subject and if you read my entire blog you may get the gist of it, but I have not yet succeeded in verbalizing it concisely to my satisfaction.