加州大学伯克利Dr. Allen Yang：物理AI的分水岭时刻尚未到来｜CES 2026

Core Insights - The artificial intelligence industry is currently engaged in a "GPU race," with a focus on cloud-based AI applications, but there is a call to shift attention towards physical AI and its potential breakthrough moments [1][5][16] - Dr. Allen Yang emphasizes that while AI has made significant strides with models like AlphaGo, physical AI is still awaiting its own "watershed moment" due to unique challenges rooted in the complexities of the physical world [2][6][12] Group 1: Challenges in Physical AI - Physical AI lacks comprehensive training data for extreme scenarios, unlike language models that can leverage vast internet data [2][13] - Real-time decision-making with millisecond-level latency is critical for applications like autonomous driving, where delays can lead to failures [2][14] - Many cutting-edge scenarios lack reliable cloud connectivity, necessitating the use of edge AI deployed locally on devices [2][15] Group 2: Innovations and Competitions - The Berkeley AI Racing Team has achieved significant milestones, including a top speed of 163 miles per hour in autonomous driving competitions, showcasing the need for complex real-time perception and planning [4][18] - The upcoming Tianmen Mountain humanoid robot challenge aims to test robots' mobility and decision-making in unstructured terrains, further pushing the boundaries of physical AI [4][29] - The collaboration with nine universities in China for the Tianmen Mountain challenge highlights the importance of interdisciplinary cooperation and real-world experience in advancing physical AI [4][26] Group 3: Future Directions - The focus on physical AI is expected to grow, with the potential for new breakthroughs that could redefine the field, similar to past milestones in AI history [2][18][29] - The upcoming competitions and challenges are designed to foster innovation and collaboration among institutions, aiming to discover the next "AlphaGo moment" in physical AI [25][29]