[AINews] Agents disprove Polysemanticity; greatly improve Interpretability & Alignment

Nick Cammarata of Goodfire challenges the concept of polysemanticity in neural networks, showing that most neurons in Qwen-32B have distinct functions, improving interpretability and alignment prospects. His findings suggest neural networks are more 'clean' and understandable than previously thought, potentially advancing AI safety research.