Software can now easily spot objects in images, but it can't always describe those objects well; "short man with horse" not only sounds awkward, it doesn't reveal what's really going on. That's where a computer vision breakthrough from Google and Stanford University might come into play. Their system combines two neural networks, one for image recognition and another for natural language processing, to describe a whole scene using phrases. The program needs to be trained with captioned images, but it produces much more intelligible output than you'd get by picking out individual items. Instead of simply noting that there's a motorcycle and a person in a photo, the software can tell that this person is riding a motorcycle down a dirt road. The software is also roughly twice as accurate at labeling previously unseen objects when compared to earlier algorithms, since it's better at recognizing patterns.