The outcome shows a very nice improvement on an unsupervised classification and feature detection task, but it also highlights that unsupervised machine learning still has a long way to go. 16% accuracy from a network with 1bn connections and 100m inputs using (if my math is right) 1.15m hours of CPU time. Which of these would be the easiest way to continue making gains: investing more time/hardware, increasing the complexity of the model, or developing a new and improved algorithm altogether? All of these sound pretty intensive to me.