可扩展人工智能系统的基础
The Foundations of Scalable AI
美国麻省理工学院科技评论的EmTech Future会议探讨了构建可扩展人工智能系统所需的基础，包括数据、工程、平台、运营模式和治理的整合。Vanguard的AI和洞察中心负责人Jing Wang强调了在高度监管环境中构建这一基础的重要性，以及如何将包括智能体系统在内的新兴能力转化为对投资者持久的价值。
A scalable AI system requires a strong foundation. Data, engineering, platforms, operating models, and governance must converge to work as one. This session from MIT Technology Review’s EmTech Future conference explores what it takes to build that foundation in a highly regulated environment and how a responsible, trustworthy implementation can turn emerging capabilities, including agentic systems, into lasting value for investors.
About the speaker
Jing Wang, Principal, Center for AI and Insights, Vanguard
Jing Wang leads Vanguard’s Center for AI & Insights (CAI), advancing the firm’s AI, machine learning, generative AI, and behavioral science capabilities. Her team drives enterprise-wide innovation, research, and delivery, enabling Vanguard to translate emerging technologies into trusted solutions that enhance client experiences, improve investor outcomes, and create measurable business value.
Before her current position, Jing served in a range of leadership roles across Vanguard’s Financial Services, Workplace Solutions, International, and Corporate Services divisions. Throughout her tenure, she has led teams and enterprise initiatives that advanced strategic priorities, strengthened client outcomes, and drove business performance across the firm.
She joined Vanguard in 2009 after several years as a Principal at the Boston Consulting Group. Jing earned a B.S. in Physics from the University of Science and Technology of China, a Ph.D. from the University of Pennsylvania, and an M.B.A. in Finance and Marketing from the Wharton School of Business at the University of Pennsylvania. Jing holds Series 7, 24, and 65 licenses.
来源：MIT Technology Review · AI · technologyreview.com