In the first episode of the Talking Language AI series, Jay Alammar converses with Maarten Grootendorst, the creator of the BERTopic open-source package, which is used for topic modeling to identify trends and topics in text collections. Maarten explains how BERTopic operates through a pipeline involving SBERT embeddings, UMAP for dimensionality reduction, HDBSCAN for clustering, and methods like cTF-IDF and MMR for topic representation. The discussion touches on BERTopic's modularity, its visualization capabilities, and its flexibility to adapt to various scenarios, with Maarten providing a demonstration of its use on a dataset of research papers. The episode also includes a Q&A where Maarten addresses questions about evaluating topic modeling tasks, the assignment of single topics to documents, handling different text lengths, and the potential integration of GPT language models, among other topics related to NLP tool development and implementation.