Your point about equating the neocortex to the whole body led me to write this out:
I don't think I have any basis for this besides a gut feeling and some daydreams, but I think each of the major methodologies like reinforcement learning, transformers, graph nns etc need to be combined into some larger type of ensemble and worked together into a cohesive system with feedback loops for online and offline learning for a shot at AGI.
I've been doing ML projects for like 6 years, mostly in NLP but have dipped into reinforcement learning because it interests me and my gut feeling has been much that there are a lot of complimentary learning systems that can handle different problems really well and cover for limitations in others and I'd like to see what happens if we smash them together towards the goal of generally beating baselines for as many benchmarks as possible.
This is exactly my personal intuition as well, almost to a T. Here's to the satisfying consilience of independent thinking reaching the same hypotheses.
I was reading through George Lakoff and Mark Johnson's Philosophy in the Flesh last night and had a very similar thought. Their model of embodied cognition is necessarily decentralized in a really interesting way.
I don't think I have any basis for this besides a gut feeling and some daydreams, but I think each of the major methodologies like reinforcement learning, transformers, graph nns etc need to be combined into some larger type of ensemble and worked together into a cohesive system with feedback loops for online and offline learning for a shot at AGI.
I've been doing ML projects for like 6 years, mostly in NLP but have dipped into reinforcement learning because it interests me and my gut feeling has been much that there are a lot of complimentary learning systems that can handle different problems really well and cover for limitations in others and I'd like to see what happens if we smash them together towards the goal of generally beating baselines for as many benchmarks as possible.