by Angela Guess
Cade Metz reports in Wired, “Computer vision is now a part of everyday life. Facebook recognizes faces in the photos you post to the popular social network. The Google Photos app can find images buried in your collection, identifying everything from dogs to birthday parties to gravestones. Twitter can pinpoint pornographic images without help from human curators. All of this ‘seeing’ stems from a remarkably effective breed of artificial intelligence called deep learning. But as far as this much-hyped technology has come in recent years, a new experiment from Microsoft Research shows it’s only getting started. Deep learning can go so much deeper.”
Metz continues, “This revolution in computer vision was a long time coming. A key turning point came in 2012, when artificial intelligence researchers from the University of Toronto won a competition called ImageNet. ImageNet pits machines against each other in an image recognition contest—which computer can identify cats or cars or clouds more accurately?—and that year, the Toronto team, including researcher Alex Krizhevsky and professor Geoff Hinton, topped the contest using deep neural nets, a technology that learns to identify images by examining enormous numbers of them, rather than identifying images according to rules diligently hand-coded by humans.”
Metz goes on, “Now, the latest ImageNet winner is pointing to what could be another step in the evolution of computer vision—and the wider field of artificial intelligence. Last month, a team of Microsoft researchers took the ImageNet crown using a new approach they call a deep residual network. The name doesn’t quite describe it. They’ve designed a neural net that’s significantly more complex than typical designs—one that spans 152 layers of mathematical operations, compared to the typical six or seven. It shows that, in the years to come, companies like Microsoft will be able to use vast clusters of GPUs and other specialized chips to significantly improve not only image recognition but other AI services, including systems that recognize speech and even understand language as we humans naturally speak it.”
photo credit: Microsoft Research