I think if we can get a really good, probably sparsely activated, multimodal model that can do continual learning that shows transfer - ala Pathways, many white collar jobs are done.

Any system that has continual learning I think would also have short/medium/whatever term memory, and a context window that can handle enough at once that rivals what we can handle at any given time.

But the thing is I think that unlike biological systems, there are many different inefficient ways to get us there as well. A very dense model that is big enough, with a better fine tuning process might be all we need. Or maybe the bottle neck is currently really context, as in-context learning is quite powerful, what if we suddenly have an efficiency breakthrough with a Transformer 2.0 that can allow for context windows of 1 million tokens?

Also maybe we don't need multimodal per se, maybe a system that is trained on pixels would cover all bases.