Most neural networks are structured around a classification task with pre-tagged data. This is fundamentally a problem that it requires pre-tagged data in order

Most neural networks are structured around a classification task with pre-tagged data. This is fundamentally a problem that it requires pre-tagged data in order to ferret out what the "true meaning" of a particular classification is. When the task moves towards determining differences between un-tagged data, you're looking at more of a clustering exercise. This is a murkier machine learning task which is relatively underdeveloped. As a quick comparison, linear regression and correlations were generally worked out by Pearson back in the late 1800's, while k-means clustering was only published openly since the 1980's.