This paper discusses a new method for automatic discovery and organization of descriptive concepts (labels) within large real-world corpora of user-uploaded multimedia, such as YouTube.com. Conversely, it also provides validation of existing labels, if any. While training, our method does not assume any explicit manual annotation other than the weak labels already available in the form of video title, descrip- tion, and tags. Prior work related to such auto-annotation assumed that a vocabulary of labels of interest (e.g., indoor, outdoor, city, landscape) is speciﬁed a priori. In contrast, the proposed method begins with an empty vocabulary. It analyzes audiovisual features of 25 million YouTube.com videos – nearly 150 years of video data – effectively searching for consistent correlation between these features and text metadata. It autonomously extends the label vocabulary as and when it discovers concepts it can reliably identify, eventually leading to a vocabulary with thousands of labels and growing. We believe that this work signiﬁcantly extends the state of the art in multimedia data mining, discovery, and organization based on the technical merit of the proposed ideas as well as the enormous scale of the mining exercise in a very challenging, unconstrained, noisy domain.