I agree that the big wins in machine learning/(weak)AI are probably going to come more from figuring out how to better apply existing models and algorithms to

I agree that the big wins in machine learning/(weak)AI are probably going to come more from figuring out how to better apply existing models and algorithms to real problems rather than from improving the performance of the algorithms themselves. That said one shouldn't underestimate the amount of commonality between problems that to some people may appear unrelated. For example this post talks about the gains in machine translation performance from including larger contexts. The same principle applies to many other sequence learning problems. For example you have a very similar issue with handwriting recognition where it is often not possible (even for a human) to determine the correct letter classification for a given handwritten character without seeing it within the context of the word.