In a research experiment involving unsupervised machine learning, Google may have discovered the most significant image pattern on the internet, or at least on Youtube where the experiment was performed. The system was designed to detect and rank imagery patterns which could then be analyzed for what they represent by researchers. It may come at no surprise, then, that this system succeeded in detecting what is important to the users of Youtube and most of the internet in general: cats.
What was originally designed to detect significant patterns in imagery data became the world's first cat detector after the results showed that imagery of cats was some of the most detected and thus significant imagery data after being trained on Youtube videos over the course of three days. According to an article on Slate, linear tool-like objects held at about a 30 degree angle were also common features detected and after adding a round of supervised learning, the classifier could detect human faces with around 82% accuracy.
Perhaps not the most useful breakthrough in unsupervised learning on image data on the surface, the model does prove the capability of emerging methods to detect meaningful features in images and video. Perhaps with some tweaking the system will also be able to detect dogs, but no breakthroughs in this area have emerged yet.
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