The machine learning paradox

The O'Reilly Artificial Intelligence conference in New York is June 26-29, 2017. To train a machine learning system, you start with a lot of training data: millions of photos, for example. You divide that data into a training set and a test set. You use the training set to "train" the system so it can identify those images correctly. Then you use the test set to see how well the training works: how good is it at labeling a different set of images?