The extreme connectionist hypothesis is that nothing very much needs to be understood in order to catalyze emergent phenomena, with synthetic intelligence as an especially significant example of something that could just happen. DARPA’s Gill A. Pratt approaches the question of robot emergence within this tradition:
While the so-called “neural networks” on which Deep Learning is often implemented differ from what is known about the architecture of the brain in several ways, their distributed “connectionist” approach is more similar to the nervous system than previous artificial intelligence techniques (like the search methods used for computer chess). Several characteristics of real brains are yet to be accomplished, such as episodic memory and “unsupervised learning” (the clustering of similar experiences without instruction), but it seems likely that Deep Learning will soon be able to replicate the performance of many of the perceptual parts of the brain. While questions remain as to whether similar methods can also replicate cognitive functions, the architectures of the perceptual and cognitive parts of the brain appear to be anatomically similar. There is thus reason to believe that artificial cognition may someday be put into effect through Deep Learning techniques augmented with short-term memory systems and new methods of doing unsupervised learning. [UF emphasis]
He anticipates a ‘Robot Cambrian Explosion’.
It seems improbable that a sufficiently self-referential pattern recognition system — i.e. an intelligence — is going to be the product of a highly-specified initial design. An AI that doesn’t almost entirely put itself together won’t be an AI at all. Still, by the very nature of the thing, it’s not going to impress anybody until it actually happens. Perhaps it won’t, but we have no truly solid reasons — beyond an inflated self-regard concerning both our own neural architectures and our deliberative engineering competences — to think it can’t.