In exchange, he offers the idea that we should have something that is an 'energy minimization' architecture; as I understand it, this would have a concept of the 'energy' of an entire response, and training would try and minimize that.
Which is to say, I don't fully understand this. That said, I'm curious to hear what ML researchers think about Lecun's take, and if there's any engineering done around it. I can't find much after the release of ijepa from his group.
Unlike in chess, there’s a functionally infinite number of actions you can take in real life. So just argmax over possible actions is going to be hard.
Two, you have to have some value function of how good an action is in order to argmax. But many actions are impossible to know the value of in practice because of hidden information and the chaotic nature of the world (butterfly effect).
I think you are thinking of the fact that it had to be approached in a different way than Minimax in chess because a brute force decision tree grows way too fast to perform well. So they had to learn models for actions and values.
In any case, Go is a perfect information game, which as I mentioned before, is not the same as problems in the real world.