Mechanistic Interpretability

In this episode of The Turing Talks, we explore the rising field of mechanistic interpretability, which seeks to unravel how neural networks internally process decisions. As AI systems enter high-stakes fields like healthcare and finance, understanding their inner workings is crucial for detecting biases and ensuring alignment with human goals. We discuss key challenges like polysemanticity—where neurons represent multiple features—and how researchers are addressing this with techniques like sparse autoencoders. Tune in for insights into the future of AI transparency and safety.