Episode 12: Jacob Steinhardt, UC Berkeley, on machine learning safety, alignment and measurement

Jacob Steinhardt (Google Scholar) (Website) is an assistant professor at UC Berkeley.  His main research interest is in designing machine learning systems that are reliable and aligned with human values.  Some of his specific research directions include robustness, rewards specification and reward hacking, as well as scalable alignment. Highlights: 📜“Test accuracy is a very limited metric.” 👨‍👩‍👧‍👦“You might not be able to get lots of feedback on human values.” 📊“I’m interested in measuring the progress in AI capabilities.”