"Singularity is near"? "Holy grail"? You may be getting a little carried away here.
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.
If the algorithm keeps increasing in accuracy as you scale up computation and add more unlabeled data that is pretty amazing. You might get something that matches human performance on vision/speech recognition etc.
If you extrapolate that way you'd conclude that naive Bayes is the solution to AI. Improvements tend to tail off fairly quickly as you add more data and computation, unfortunately.
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.