The field of topic modeling has become increasingly important over the past few years. Topic modeling is an unsupervised machine learning way to organize text (or image or DNA, etc.) information such that related pieces of text can be identified. This paper/session will present/discuss the current state of topic modeling, why it is important, and how one might use topic modeling for practical use. As an example, the text of some ASCUE proceedings of past years will be used to find and group topics and see how those topics have changed over time. As another example, if documents represent customers, the vocabulary is the products offered to customers, and and the words of a document (i.e., customer) represent products bought by customers, than topic modeling can be used, in part, to answer the question, "customers like you bought products like this" (i.e., a part of recommendation engines). [For the full procedings, see ED571252.]