AI Interpretability Research Hits Dead End as Major Techniques Fail to Decode Neural Networks
Major AI interpretability research approaches including feature visualizations, saliency maps, and sparse autoencoders fail to decode how neural networks actually work after over a decade of investment, prompting Google DeepMind to abandon leading techniques and researchers to shift toward higher-level analysis methods.