By "composability" I mean you'd be able to train models separately and then perhaps slightly refine the composed model as a whole on the final task(s). And mode

By "composability" I mean you'd be able to train models separately and then perhaps slightly refine the composed model as a whole on the final task(s). And models would be using each other's outputs, sort of like today you can take an ImageNet backbone and put an object detection head on top, except at a higher level. I.e. an optical (+IR) multi-camera object tracking model, fused with lidar and radar, feeding into a path planning model. As of today, I have just described a billion dollars worth of modeling at least. That needs to be simpler and cheaper. Currently stuff like this has to be trained end to end, which, as you've pointed out, is very difficult to do, to the point where there are only a handful of companies that can convincingly pull it off (Tesla, Google, OpenAI, _maybe_ Facebook), and even they are arguably at a local minimum this approach allows. And this local minimum won't get us to "real" AI, which I think was the point of this rather rambling article.