In this episode of State of AI, we explore one of the most groundbreaking innovations in artificial intelligence — SpikingBrain, a family of brain-inspired large language models (LLMs) designed for energy-efficient, long-context computation. Developed by researchers at the Chinese Academy of Sciences, SpikingBrain challenges the Transformer dominance by introducing adaptive spiking neurons, linear attention mechanisms, and a new training pipeline that mimics how the human brain processes information.

We dive into the science behind SpikingBrain-7B and SpikingBrain-76B, two models that achieve up to 100× faster inference and 69% computational sparsity while consuming less than 2% of the training data used by traditional models