Neel Nanda

Neel Nanda is a final year maths undergraduate at the University of Cambridge, and a gold medalist in the International Mathematical Olympiad. He teaches regularly – from revision lectures to a recent ‘public rationality’ workshop. Neel is also an active member in rationalist and effective altruism communities. In this episode we discuss How to view self-improvement and optimising your goals Forming good habits through the 'TAPs' technique How to build effective plans by using our 'inner simulator' and 'pre-hindsight' You can read more on this episode's accompanying write-up: hearthisidea.com/episodes/neel. You can also read Neel's teaching notes for his planning workshop here. If you have any feedback or suggestions for future guests, please get in touch through our website. Also, Neel has created an anonymous feedback form for this episode, and he would love to hear any of your thoughts! Please also consider leaving a review on Apple Podcasts or wherever you're listening to this; we're just starting out and it would really help listeners find us! If you want to support the show more directly, you can also buy us a beer at tips.pinecast.com/jar/hear-this-idea. Thanks for listening!

Neel Nanda, a researcher at Google DeepMind, discusses mechanistic interpretability in AI, induction heads in models, and his journey into alignment. He explores scalable oversight, the ambitious degree of interpretability in transformer architectures, and the capability of humans to understand complex models. The podcast also covers linear representations in neural networks, the concept of superposition in models and features, Terry Matt's mentorship program, and the importance of interpretability in AI systems.

Neel Nanda, an expert in mechanistic interpretability, discusses the challenges and potential applications of mechanistic interpretability. They explore concrete projects, debunk the usefulness of mechanistic interpretability, and discuss the limitations in achieving interpretability in transformative models like GPT-4. They also delve into the concept of model safety and ablations, and discuss the potential of ruling out problematic behavior without fully understanding the model's internals. The speakers reflect on the dialogue and highlight its usefulness in advancing thinking about mechanistic interpretability.