The immense processing power of Google's global computing network and the brainpower of its secretive Google X research labs remain largely hidden from a curious world. But this week we were given a glimpse of what the company's great minds, human and electronic, are thinking about: cats.
Google scientists built the world's biggest electronic simulation of a brain, running on 16,000 computer processors, and discovered what it would learn when exposed to 10m clips randomly selected from YouTube videos. Unprompted, the computer brain taught itself to identify the feline face.
That might seem a trivial accomplishment, demonstrating little more than the obsession of cat owners with posting videos of their pets. But in fact Google has made a significant advance in artificial intelligence, a research field that has promised much but delivered little to computer users.
In their presentation at a machine learning conference in Edinburgh, the Google researchers demonstrated the company's ambitions in AI as well as the strength of its computing resources.
Standard machine learning and image recognition techniques depend on initial "training" of the computer with thousands of labelled pictures, so it starts off with an electronic idea of what, say, a cat's face looks like. Labelling, however, requires a lot of human labour and, as the Google researchers say, "there is comparatively little labelled data out there".
"Google needs to master what it calls "self-taught learning" or "deep learning", if it is to extend its search capabilities to recognise images among the vast volume of unstructured and unlabelled data. That would enable someone who, for example, owned an unidentified portrait painted by an unknown artist to submit a photograph of it to a future Google – and stand a reasonable chance of having both the scene and the painter identified through comparison with billions of images across the internet.
The study presented this week is a step towards developing such technology. The researchers used Google data centres to set up an artificial neural network with 1bn connections and then exposed this "newborn brain" to YouTube clips for a week, without labelling data of any sort.