| Eric Horvitz, Harsha Nori, and Naoto Usuyama
Discover the most effective run-time strategies on the OpenAI o1-preview model, improving accuracy in medical language tasks.
| Yingce Xia, Pan Deng, Shufang Xie, Haiguang Liu, and Tao Qin
TamGen uses generative AI to design new drug candidate compounds to treat TB, going beyond traditional methods by generating novel chemical structures. Learn how a collaboration with the Global Health Drug Discovery Institute is making this possible.
| Darren Edge, Ha Trinh, and Jonathan Larson
Introducing a new approach to graph-enabled RAG. LazyGraphRAG needs no prior summarization of source data, avoiding prohibitive up-front indexing costs. It’s inherently scalable in cost and quality across multiple methods and search mechanisms.
In the news | The Economic Times
Microsoft Research India plans to expand its Shiksha Copilot, aimed at improving learning outcomes with engaging content, with multimodal, multilingual and multi-device dimensions to help teachers with vision impairments create lesson plans. “The possibilities for AI to positively impact accessibility…
In the news | The Seattle Times
Microsoft unveiled Aurora in June, an AI model that draws on atmospheric data. The company said in a blog post introducing the model that a team of researchers developed it to forecast in the most challenging conditions, for regions without…
编者按:欢迎阅读“科研上新”栏目!“科研上新”汇聚了微软亚洲研究院最新的创新成果与科研动态。在这里,你可以快速浏览研究院的亮点资讯,保持对前沿领域的敏锐嗅觉,同时也能找到先进实用的开源工具。 本期内容速览 01. 自我进化实现Rust自动形式化证明 02. 基于图模式的理解基准测试 03. IGOR: 通过学习统一的动作表示空间让机械臂模仿人类动作 论文链接:https://arxiv.org/p...
Awards | ACM MobiCom
CosMAC won the Best Community Paper Award at ACM MobiCom 2024!
“It is difficult to make predictions, especially about the future” – Yogi Berra (perhaps apocryphal) How well can experiments be used to predict the future? At Microsoft’s Experimentation Platform (ExP), we pride ourselves on ensuring the trustworthiness of our experiments.…
| Karin Strauss, Bichlien Nguyen, Jake Smith, and 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.