In this episode, Daniel Balsam and Tom McGrath, at Goodfire, discuss the future of mechanistic interpretability in AI models. They explore the fundamental inputs like models, compute, and algorithms, and emphasize the importance of a rich empirical approach to understanding how models work. Balsam and McGrath provide insights into ongoing projects and breakthroughs, particularly in scientific domains and creative applications, as they aim to push the frontiers of AI interpretability. They also discuss the company's recent funding and their goal to advance interpretability as a critical area in AI research.
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