Kevin K. Yang, Sarah Alamdari, Alex J. Lee, Kaeli Kaymak-Loveless, Samir Char, Garyk Brixi, Carles Domingo-Enrich, Chentong Wang, Suyue Lyu, Nicolo Fusi, Philip Rosenfield, Neil Tenenholtz, Ava P. Amini “The body of data available in protein sequences is something fundamentally…
Navigating medical education in the era of generative AI
| Peter Lee, Dr. Morgan Cheatham, and Daniel Chen
Next-generation physicians Morgan Cheatham and Daniel Chen discuss how generative AI is transforming medical education, exploring how students and attending physicians integrate new tools while navigating questions on trust, training, and responsibility.
In the news | Microsoft Source Asia
Microsoft Research Asia launches Singapore lab to drive AI innovation, industrial transformation, and talent development
微软亚洲研究院（新加坡）正式成立
今天，微软宣布在新加坡成立微软亚洲研究院（新加坡），这是微软在东南亚设立的首个研究实验室，得到了新加坡经济发展局的大力支持。新加坡实验室旨在推动人工智能领域的前沿研究，携手合作伙伴共创面向关键行业的 AI 解决方案，并培养新一代 AI 人才。 新加坡人力部长兼贸工部主管能源与科技部长陈诗龙（Tan See Leng）与微软全球资深副总裁、微软研究院院长、微软全球研究与创新孵化负责人 Peter L...
在微软亚洲研究院（新加坡）用真实场景验证AI的 “破圈” 力量
编者按：当人工智能浪潮席卷全球，算法如何突破实验室围墙，在真实世界中创造价值？微软亚洲研究院（新加坡）首席研究员徐新兴博士用十年求索给出了自己的答案。从南洋理工大学的学术研究，到新加坡科技研究局的跨领域实践，再到成为微软亚洲研究院在新加坡的首位研究员，他始终在“算法研究”与“产业实践”的交汇中寻找创新的突破口。 尽管近年来人工智能持续快速发展，但始终面临一个难题：如何将算法模型从理论实验推向产业落...
Xinxing Xu bridges AI research and real-world impact at Microsoft Research Asia – Singapore
Xinxing Xu is helping shape the work of Microsoft Research Asia – Singapore by turning advanced AI research into real-world solutions. Learn how he collaborates across sectors and disciplines to drive responsible innovation throughout Southeast Asia.
Technical approach for classifying human-AI interactions at scale
| Amber Hoak, David Tittsworth, Kate Lytvynets, Scott Counts, Weiwei Yang, Ben Cutler, and Jonathan McLean
Semantic Telemetry helps LLMs run efficiently, reliably, and in near real-time. Learn about the engineering behind that system, including the trade-offs and lessons learned along the way—from batching strategies to token optimization and orchestration.
Given a language model, can we tell whether it is truly reasoning, or if its performance owes only to pattern recognition and memorization?
CoR：多范式思维加持，大模型也能学会“多思路解题”
编者按：大语言模型（LLMs）在语言生成与基础推理中已展现出强大的能力，但它们在数学解题上的能力仍存在明显短板，尤其是难以兼顾复杂计算与定理证明。这背后的关键原因在于，现有模型普遍依赖于单一的推理范式（如自然语言、代码或符号推理），缺乏人类思考问题时那种灵活的推理能力。 为此，微软亚洲研究院与清华大学联合提出了“推理链”（Chain-of-Reasoning, CoR）框架，引入了自然语言、代码与...