对话任少卿：2025 NeurIPS 时间检验奖背后，我的学术与产业观

Group 1 - NeurIPS is recognized as the "Oscar of AI" and serves as a global annual barometer for the artificial intelligence field [1] - The NeurIPS Time-Tested Award honors foundational works that have significantly influenced the discipline over a decade [1] - The award was given to the authors of "Faster R-CNN," which has been cited over 98,000 times, making it the most cited paper by a Chinese first author at this conference [2] Group 2 - "Faster R-CNN," developed in 2015, improved object detection efficiency by over 10 times and introduced an end-to-end real-time detection model [2] - The core ideas of this model have been deeply integrated into the foundational technologies of AI, impacting key sectors such as autonomous driving and medical imaging [2] - The collaboration between the authors, including Ren Shaoqing and He Kaiming, has led to significant advancements in deep learning frameworks [2] Group 3 - Ren Shaoqing joined NIO in August 2020, focusing on building a team and developing self-research chips for autonomous driving [13][14] - NIO's first generation of vehicles utilized the Mobileye solution, while the second generation was the first globally to mass-produce the NVIDIA Orin chip [14] - The challenges faced during the development included adapting to new architectures and ensuring the stability of the new chip [15] Group 4 - NIO emphasized the importance of data collection and analysis, focusing on corner cases to improve the performance of their models [19][20] - The company established a flexible system for cloud computing and data management, allowing for rapid iteration of models [21] - NIO's approach to active safety has enabled them to achieve a standard of 200,000 kilometers per false positive, significantly improving their testing efficiency [22] Group 5 - The concept of end-to-end solutions in autonomous driving has evolved, with discussions on integrating various technologies to enhance performance [24][25] - NIO is exploring the development of world models to improve long-term decision-making capabilities in autonomous systems [27][28] - The world model approach aims to address the limitations of traditional methods by incorporating both spatial and temporal understanding [30][31]