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.