Though obesity remains a crisis in the United States, and a commercial version of Im2Calories would probably be hugely popular, it's how this system work that's worth a closer look. Like many deep learning applications, it marries visual analysis—in this case, determining the depth of each pixel in an image—with pattern recognition. Im2Calories can draw connections between what a given piece of food looks like, and vast amounts of available caloric data. And while it's best not to read too much into the term “deep learning,” one of those evocative AI word choices that's practically daring non-researchers to panic, Im2Calories is designed to improve itself through use. The purpose of many deep learning systems is to minimize the amount of time spent feeding or quizzing a piece of software, to improve its performance. If Im2Calories spots a burger, it's because the pixels in the image resemble those in existing shots of burgers, not because a researcher held the system's hand, so to speak, during various practice runs. For deep learning to make itself useful, primarily by extracting meaning from audio, video, still imagery and text, it has to be at least somewhat self reliant.