Daniela Hernandez tells us: “Today’s most widely used convolutional neural nets rely almost exclusively on supervised learning. Basically, that means that if you want it to learn how to identify a particular object, you have to label more than a few examples. Yet unsupervised learning—or learning from unlabeled data—is closer to how real brains learn, and some deep learning research is exploring this area. “How this is done in the brain is pretty much completely unknown. Synapses adjust themselves, but we don’t have a clear picture for what the algorithm of the cortex is,” says [Yann] LeCun. “We know the ultimate answer is unsupervised learning, but we don’t have the answer yet.””