Not necessarily. One thing that a wide-net dataset can be extremely useful for is improving the neural net models to better recognize people outside the traditi

Not necessarily. One thing that a wide-net dataset can be extremely useful for is improving the neural net models to better recognize people outside the traditional academic dataset a lot of older-generation neural nets were trained on, which is to say: the sorts of people who have the time and need-awareness to put their faces in some university's dataset for training neural nets (which is to say: white guys ;) ). You can use faces without names attached to improve the engine's modeling for recognizing human faces in general (and more importantly: improve the system's ability to distinguish human and animal faces). (Your first comment is pretty interesting by itself, incidentally: both NBC News and your comment make an assumption that is not universially true about the technology. Face recognition is a much wider space than "recognize an individual by their face." Clustering of similar faces, emotion analysis, camera targeting, human presence / absence can all be done without name labels).