This is an unformed thought, but to me the philosophy of emergence, intelligence and deep learning have something to do with each other. In emergent systems, you have a stack of levels of phenomena, and you can describe the phenomena at one level without reference to the details of the lower levels. It's like those details have been blurred out and you're left with a compressed representation with just enough information to be useful at the new level (e.g. pixels->edges, renormalization in physics). The success of architectures with multiple layers, including bottlenecks in layer size, seems very similar, and show that this is a good way to look at the world.
This seems to be quite a natural way that humans see the world, and in fact we might not think of something that doesn't behave this way as being intelligent -- we want there to be some sort of compressed 'trick' that doesn't just brute force the data.
I am surprised you didn't use the word abstraction even once in your description of emergent systems. I admit I have some catching up to do on the state of that topic but I couldn't help but notice the symmetry between the stack of phenomena as you described it and abstraction layers of basic computational systems. APIs and programming languages expose an interface that completely abstracts away the implementation beneath it. And much of the layers added have indeed emerged in the sense that they have been built based on what we thought might be practical on top of established layers. Anyway, just some food for that unformed thought of yours ;)
This seems to be quite a natural way that humans see the world, and in fact we might not think of something that doesn't behave this way as being intelligent -- we want there to be some sort of compressed 'trick' that doesn't just brute force the data.