Topic: NeurIPS

The fourth Breaking Topics in AI conference brings researchers from Oxford, UCL, Cambridge and Imperial together to examine recent AI advances and open research challenges. Plenary speakers give overviews of developments in their fields, while paper talks discuss selected recent work from ICML and NeurIPS. The programme combines perspectives from Mark van der Wilk, Maneesh Sahani, Jacob Bamberger, Vignesh Gopakumar and Katarzyna Kobalczyk, creating opportunities to connect new methods with ongoing research across the participating institutions. This free, in-person conference takes place on 2

Jin Zhu examines statistical approaches to identifying text produced by large language models. The increasing availability of fluent generated writing raises questions about misinformation, academic integrity and the authenticity of digital content. Although detection has attracted substantial machine-learning research, the talk argues that its statistical foundations remain underdeveloped. Zhu presents recent work on principled, computationally efficient detection methods, including approaches reported at NeurIPS and ICLR. An interactive demonstration accompanies the research, illustrating on