Jesse Hoogland

Jesse Hoogland and Daniel Murfet, co-founders of Timaeus, pioneer AI safety with their focus on developmental interpretability based on Singular Learning Theory. They discuss the complex, jagged landscapes of neural networks and how their Local Learning Coefficient can identify critical training phase changes. This innovative approach aims to catch safety issues early, providing a more structured methodology for AI development. Their insights reveal the intricate relationships between training data, model behavior, and alignment, pushing for a principled engineering discipline in AI.

Jesse Hoogland, co-author of the influential LessWrong post on AI research fleets, dives into the exciting shift from individual AI scientists to collaborative systems. He discusses how research automation requires rethinking workflows, much like past technological revolutions did. Hoogland emphasizes the importance of institutional changes and community actions in embracing AI-augmented science. His insights challenge outdated expectations and propose innovative strategies for creating efficient, specialized research ecosystems.

Jesse Hoogland, executive director of Timaeus and researcher in singular learning theory (SLT), shares fascinating insights on AI alignment. He dives into the concept of the refined local learning coefficient (LLC) and its role in uncovering new circuits in language models. The conversation also touches on the challenges of interpretability and model complexity. Hoogland emphasizes the importance of outreach efforts in disseminating research and fostering interdisciplinary collaboration to enhance understanding of AI safety.