Luke Bailey and Eric Jenner, both leading experts on AI safety, dive into their research on obfuscated activation attacks. They dissect methods for bypassing latent-based defenses in AI while examining the vulnerabilities these systems face. The conversation highlights complex topics like backdoor attacks, the importance of diverse datasets, and the ongoing challenge of enhancing model robustness. Their work sheds light on the cat-and-mouse game between attackers and defenders, making it clear that the future of AI safety is as intricate as it is essential.

Erik Jenner, a third-year PhD student at UC Berkeley's Center for Human Compatible AI, dives into the fascinating world of neural networks in chess. He explores how these AI models exhibit learned look-ahead abilities, questioning whether they strategize like humans or rely on clever heuristics. The discussion also covers experiments assessing future planning in decision-making, the impact of activation patching on performance, and the relevance of these findings to AI safety and X-risk. Jenner's insights challenge our understanding of AI behavior in complex games.