Group 1 - The article highlights that Professor Su Weijie from the University of Pennsylvania received the COPSS Presidents' Award for his significant contributions to AI deployment, privacy protection, and statistical frameworks [1][7][10] - This award marks the return of a Chinese scholar to the highest honor in statistics after 14 years [2] - Professor Su believes that statistics will become increasingly important in the AI era, providing a solid theoretical foundation for AI applications [4][6] Group 2 - Professor Su's work includes formalizing issues like traceability of AI-generated content and aligning human preferences within a rigorous statistical framework [9] - He proposed a Gaussian differential privacy framework that was applied in the 2020 U.S. Census to enhance the utility of private data [9] - He introduced a quality ranking mechanism for authors' submissions, which was officially implemented at ICML in 2026 [9] Group 3 - The article discusses the need for a new mathematical language for AI, as current mathematical frameworks may not adequately describe AI's underlying structures [12][82] - Professor Su compares the evolution of AI to a "new physics," emphasizing that AI's structure differs fundamentally from classical physics [13][82] - He invites mathematically trained individuals to contribute to creating a more suitable mathematical framework for AI, which could have a significant impact comparable to classical mechanics or relativity [14][85] Group 4 - The article addresses the challenges of fully understanding AI's black box nature, suggesting that a complete white-box approach may be unrealistic [5][49] - It proposes a probabilistic approach to AI behavior, focusing on performance rather than internal mechanisms, which could help manage risks in real-world applications [56][57] - Professor Su emphasizes the importance of combining evidence from mechanisms and behavioral performance to find a balanced solution in the context of AI [62] Group 5 - The article discusses privacy protection as a critical area of focus, highlighting the challenges posed by neural networks in maintaining privacy while ensuring model effectiveness [64][65] - Professor Su suggests a tiered approach to privacy goals, advocating for a balance between privacy and utility in various contexts [70] - He proposes creating an incentive structure similar to blockchain to transform privacy protection into an intrinsic motivation for companies [73]