天桥脑科学研究院成立尖峰智能实验室 支持“发现式智能”

Group 1 - The newly established Spiking Intelligence Lab (SIL) aims to develop brain-like models and spiking neural networks, focusing on the deep integration of artificial intelligence and human intelligence [1] - The lab is led by Professor Li Guoqi and is a non-profit research institution under the Tianqiao Brain Science Research Institute, which seeks to provide key capabilities for the "discovery-based intelligence" proposed by founder Chen Tianqiao [1] - The research emphasizes the importance of neural dynamics, contrasting with mainstream AI models that rely on scaling parameters, and aims to create a comprehensive brain architecture with strong perception, memory, and thinking capabilities [1][2] Group 2 - Chen Tianqiao highlighted the limitations of the "scale path" based solely on data and computing power, advocating for a "structural path" that resembles the "cognitive anatomy" of intelligence [2] - The Tianqiao Brain Science Research Institute plans to invest over $1 billion to build dedicated computing clusters to support young scientists in exploring structural mechanisms and validating new hypotheses in neuroscience [2] - The first brain-like spiking model, "Shunxi 1.0," developed by Li Guoqi's team, demonstrates breakthroughs in brain-like computing and large model integration, providing a new technical route for the next generation of AI [2][3] Group 3 - The current mainstream model architecture, based on the Transformer framework, faces resource consumption bottlenecks and limitations in processing long sequences due to its reliance on simple point neuron models [3] - The "Shunxi 1.0" model is characterized by "small data, high performance," requiring only about 2% of the data used by mainstream models while achieving comparable performance in various language understanding and reasoning tasks [3] - The model has successfully completed full training and inference on domestic GPU platforms, showcasing the feasibility of building a new ecosystem for domestically controlled large model architectures [3]