In other words, it sees a pattern it thinks it recognises, such as a face or a door, and makes the picture look a little bit more like that thing. The neural network then repeats the process with the altered image. If that happens enough times, it will change a picture radically. In this particular instance, the neural network had largely been trained by pictures of animals, so any image sent through the feedback loop would be returned as a disturbing collage of animal faces. "The results are intriguing—even a relatively simple neural network can be used to over-interpret an image, just like as children we enjoyed watching clouds and interpreting the random shapes," Google engineers Alexander Mordvintsev, Christopher Olah and Mike Tyka said in a blogpost. "This network was trained mostly on images of animals, so naturally it tends to interpret shapes as animals. But because the data is stored at such a high abstraction, the results are an interesting remix of these learned features." As well as animals, the neural network often interprets points on a long-range image as pagodas or towers. "The results vary quite a bit with the kind of image, because the features that are entered bias the network towards certain interpretations," the authors said. "For example, horizon lines tend to get filled with towers and pagodas. Rocks and trees turn into buildings. Birds and insects appear in images of leaves." Google is using the artificial intelligence software in its own products (Photos being one example), but the engineers said the techniques could one day be used as a new artform - a new way to remix visual concepts. While that might not be to everyone's tastes, the results are undoubtedly intriguing. The full list of photos are here. Here's what happens to the Google logo. (All photos: Google)
Google has been one of the world 's biggest backers of artificial intelligence development , investing heavily in machine learning technology , including with last year 's acquisition of British company DeepMind . The company is developing " neural networks " that can spot patterns in pictures to identify them . The technology already allows it to capture animals and faces in its new photos app , with some interesting results .