SIASUN(SZ:300024) Hu Xiu·2025-10-23 07:06Core Insights - The article discusses the evolution of AI from "cognition" to "action," highlighting the transition of Tang Jian from academia to industry, particularly in the fields of autonomous driving and embodied intelligence [1][2] - Tang Jian emphasizes the importance of experience-driven control methods over traditional mathematical modeling in complex environments, suggesting that AI systems can learn from historical data to make effective decisions [4][5] - The concept of a "world model" is introduced as essential for embodied intelligence, enabling robots to understand and predict their environment, thus enhancing their operational capabilities [13][14] Summary by Sections Transition from Academia to Industry - Tang Jian, a former tenured professor, shifted focus to practical applications of AI in industry, particularly in autonomous driving and robotics [1][3] - His experience in various companies, including Didi and Midea, has informed his approach to AI-driven system control [3][6] Experience-Driven Control - The article outlines the difference between traditional control methods and experience-driven approaches, with the latter relying on data and historical experiences rather than precise mathematical models [4][5] - This experience-driven philosophy is evident in autonomous driving applications, where end-to-end control merges perception, planning, and control into a single learning process [6][7] Embodied Intelligence and World Models - Tang Jian argues that embodied intelligence presents a higher complexity than autonomous driving, requiring robots to manage multiple joints and navigate dynamic environments [7][8] - The world model is described as a critical component for robots to understand and interact with the physical world, enabling them to perform tasks that require nuanced understanding and adaptability [14][15] - The article highlights the need for a world model to facilitate the development of robots that can generalize across various tasks and environments, which is crucial for their deployment in real-world scenarios [21][22] Future Directions and Challenges - The discussion includes the potential for world models to achieve a "DeepSeek moment" in embodied intelligence, drawing parallels to breakthroughs in AI performance under limited resources [9][10] - Tang Jian acknowledges the current limitations in data and model architecture, indicating that further iterations and improvements are necessary for the field to progress [2][13] - The article concludes with the assertion that the world model is not just a technical choice but a fundamental requirement for the advancement of embodied intelligence [13][22]