A Duke team trained a computer to identify up to 200 species of birds from just a photo. Given a photo of a mystery bird (top), the A.I. spits out heat maps showing which parts of the image are most similar to typical species features it has seen before It can take years of birdwatching experience to tell one species from the next. But using an artificial intelligence technique called deep learning, Duke University researchers have trained a computer to identify up to 200 species of birds from just a photo. The real innovation, however, is that the A.I. tool also shows its thinking, in a way that even someone who doesn't know a penguin from a puffin can understand. The team trained their deep neural network -- algorithms based on the way the brain works -- by feeding it 11,788 photos of 200 bird species to learn from, ranging from swimming ducks to hovering hummingbirds.

Computer scientists from the University of Edinburgh and Adobe Research have come up with a novel solution to the problem of making the movements of video game characters look natural. Scientists at the University of Edinburgh in the U.K. and Adobe Research used deep learning neural networks to help digital characters in video games move more realistically. The team trained a neural network on a database of motions by a live performer on a soundstage which they recorded and digitized. The network can adapt what it learned from the database to most scenarios or settings so characters move in natural-looking ways. The network is filling the gaps between a digital character's various poses and motions, intelligently and seamlessly stitching together these elements into a whole.

As video games give players more freedom to explore complex digital worlds, it becomes more challenging for a CG character to naturally move and interact with everything in it. So to prevent those awkward transitions between pre-programmed movements, researchers have turned to AI and deep learning to make video game characters move almost as realistically as real humans do. To help make video game characters walk, run, jump, and perform other movements as realistically as possible, video game developers will often rely on human performances that are captured and translated to digital characters. It produces results that are faster and better looking than animating video game characters by hand, but it's impossible to plan for every possible way a character will interact with a digital world, according to the researchers. Game developers try to plan for as many possibilities as they can, but they ultimately have to rely on software to transition between animations of a character walking up to a chair, and then sitting down on it, and more often than not, those segues feel stilted, unnatural, and can diminish a player's experience. Computer scientists from the University of Edinburgh and Adobe Research have come up with a novel solution they'll be presenting at the ACM Siggraph Asia conference being held in Brisbane, Australia, next month.