  We present a novel technique based on deep learning and set theory which
yields exceptional classification and prediction results. Having access to a
sufficiently large amount of labelled training data, our methodology is capable
of predicting the labels of the test data almost always even if the training
data is entirely unrelated to the test data. In other words, we prove in a
specific setting that as long as one has access to enough data points, the
quality of the data is irrelevant.
