Mapping the Mind of a Neural Net: Goodfire’s Eric Ho on the Future of Interpretability
Eric Ho is building Goodfire to solve one of AI’s most critical challenges: understanding what’s actually happening inside neural networks. His team is developing techniques to understand, audit and edit neural networks at the feature level. Eric discusses breakthrough results in resolving superposition through sparse autoencoders, successful model editing demonstrations and real-world applications in genomics with Arc Institute's DNA foundation models. He argues that interpretability will be critical as AI systems become more powerful and take on mission-critical roles in society.
Hosted by…
The skinny
The skinny isn't ready yet — notes appear once the transcript is processed.