Yeah, I agree with that and I still feel there’s something missing from that discussion?
Like, there’s some degree that to have good planning capacity you want to have good world model to plan over in the future. You then want to assign relative probabilities to your action policies working out well. To do this having a clear self-environment boundary is quite key, so yes memory enables in-context learning but I do not believe that will be the largest addition, I think the fact that memory allows for more learning about self-environment boundaries is a more important part?
There’s stuff in RL, Active Inference and Michael levin’s work I can point to for this but it is rather like a bunch of information spread out over many different papers so it is hard to give something definitive on it.
Yeah, I agree with that and I still feel there’s something missing from that discussion?
Like, there’s some degree that to have good planning capacity you want to have good world model to plan over in the future. You then want to assign relative probabilities to your action policies working out well. To do this having a clear self-environment boundary is quite key, so yes memory enables in-context learning but I do not believe that will be the largest addition, I think the fact that memory allows for more learning about self-environment boundaries is a more important part?
There’s stuff in RL, Active Inference and Michael levin’s work I can point to for this but it is rather like a bunch of information spread out over many different papers so it is hard to give something definitive on it.