Microsoft Research Lab – Redmond
新闻与深度文章
In this edition: Privacy enhancements for multiparty deep learning; using smaller, open-source models to provide relevance judgments; new tool uses AI, data to automate innovation and development; Yasuyuki Matsushita named IEEE 2025 Computer Society Fellow.
新闻报道 | Microsoft Source Asia
Chasing peak sugar: India’s sugar cane farmers use AI to predict weather, fight pests and optimize harvests
The technology brings in weather, soil and other data from satellites as well as farm sensors onto a Microsoft data platform called Azure Data Manager for Agriculture (previously called FarmBeats), so farmers can see precisely what’s happening at their farm…
| Minghua Ma, Gagan Somashekar, Rujia Wang, Chetan Bansal, 和 Saravan Rajmohan
AIOpsLab is an open-source framework designed to evaluate and improve AI agents for cloud operations, offering standardized, scalable benchmarks for real-world testing, enhancing cloud system reliability.
| Ginny Badanes, Madeleine Daepp, 和 Robert Osazuwa Ness
As the “biggest election year in history” comes to an end, researchers Madeleine Daepp and Robert Osazuwa Ness and Democracy Forward GM Ginny Badanes discuss AI’s impact on democracy, including Daepp and Ness’s research into the tech’s use in Taiwan…
NeoMem: hardware/software co-design for CXL-native memory tiering; Chimera: accurate retrosynthesis prediction by ensembling models with diverse inductive biases; GA4GH task execution API enables multicloud task execution.
奖项 | ACM MobiCom
ACM MobiCom Best Community Paper Award
CosMAC won the Best Community Paper Award at ACM MobiCom 2024!
| Karin Strauss, Bichlien Nguyen, Jake Smith, 和 Sergey Yekhanin
Research manager Karin Strauss and members of the DNA Data Storage Project reflect on the path to developing a synthetic DNA–based system for archival data storage, including the recent open-source release of its most powerful algorithm for DNA error correction.
| Arindam Mitra, Ahmed Awadallah, 和 Yash Lara
Orca-AgentInstruct, from Microsoft Research, can generate diverse, high-quality synthetic data at scale to post-train and fine-tune base LLMs for expanded capabilities, continual learning, and increased performance.
Holistic motion-capture calibration technique without calibration, manual intervention or custom hardware; Research on AI agents for autonomous clouds; Automating proof-oriented program construction; One-to-many testing for natural language code generation.