Collaborators: Healthcare Innovation to Impact
| Matthew Lungren, Jonathan M. Carlson, Smitha Saligrama, Will Guyman, and Cameron Runde
In this discussion, Matthew Lungren, Jonathan Carlson, Smitha Saligrama, Will Guyman, and Cameron Runde explore how teams across Microsoft are working together to generate advanced AI capabilities and solutions for developers and clinicians around the globe.
In the news | Microsoft Build 2025
Inside Azure innovations with Mark Russinovich
Join Mark Russinovich, CTO, Deputy CISO, Technical Fellow of Microsoft Azure. Mark will take you on a tour of the latest innovations in Azure architecture and explain how Azure enables intelligent, modern, and innovative applications at scale in the cloud,…
Magentic-UI, an experimental human-centered web agent
| Hussein Mozannar, Gagan Bansal, Cheng Tan, Adam Fourney, Victor Dibia, Friederike Niedtner, Jack Gerrits, Jacob Alber, Jingya Chen, Griffin Bassman, Erkang (Eric) Zhu, Peter Chang, Ricky Loynd, Maya Murad, Rafah Hosn, Ece Kamar, and Saleema Amershi
Magentic-UI, new from Microsoft Research, is an open-source research prototype of a human-centered AI agent, designed to work with people to complete complex, web-based tasks in real time over a web browser.
In the news | Industry
Developing next-generation cancer care management with multi-agent orchestration
Philip Rosenfield, Alex X. Lu, Ava P. Amini, Lorin Crawford, Kasia Z. Kedzierska Single-cell foundation models are an exciting paradigm for biologists, as they may accelerate the understanding of complex cell data and reveal previously unknown biology. Single-cell foundation models…
Coauthor roundtable: Reflecting on real world of doctors, developers, patients, and policymakers
| Peter Lee, Carey Goldberg, and Dr. Isaac Kohane
Peter Lee and his coauthors, Carey Goldberg and Dr. Zak Kohane, reflect on how generative AI is unfolding in real-world healthcare, drawing on earlier guest conversations to examine what’s working, what’s not, and what questions still remain.
微软亚洲研究院新著问世：《无界——透视微软创新研究之境》
以人工智能大模型的崛起为标志，计算机科学踏入了一望无垠的未知之境。我们对技术变革带来的无限可能满怀期待，却也在颠覆性的重塑中遭遇诸多困惑与挑战： 新的研究范式已然到来，如何发现并投身于具有持久影响力的研究方向？面对无先例可循的技术“无人区”，突破性创新需要怎样的思维破壁？AI时代，需要什么样的人才？什么样的成长环境能够激发人才的潜能？企业如何构建持续创新的内生动力？前沿成果又该以何种方式、何种形态...
从直觉到“深度思考”：多维进化的大模型推理能力
编者按：尽管近年来人工智能的能力迅速增强，但在复杂的推理任务中仍存在不足。微软亚洲研究院的研究员们从多个角度对此展开研究，不断探索提升大模型推理能力的新途径。从利用蒙特卡洛树搜索模拟人类“深度思考”过程的 rStar-Math，到基于规则的强化学习方法 Logic-RL；从融合大语言模型数学直觉与符号方法的 LIPS，到提升自动形式化准确性的新框架；再到自动生成高质量、有监督数学数据的神经符号框架...