January 11, 2024 | Shilin He, Liqun Li, Xu Zhang, Bo Qiao, Chaoyun Zhang, Yu Kang, Rujia Wang, Qingwei Lin 林庆维, Saravan Rajmohan, and Dongmei Zhang

AI-backed virtual assistants face challenges in handling complex data structures. TaskWeaver helps users build assistants that understand diverse domain questions, follow examples, and efficiently execute customizable algorithms on complex data structures.

January 10, 2024 | Mihaela Vorvoreanu and Kathy Walker

Editor’s note: All papers referenced here represent collaborations throughout Microsoft and across academia and industry that include authors who contribute to Aether, the Microsoft internal advisory body for AI ethics and effects in engineering and research. A surge of generative…

January 10, 2024 | Zinan Lin, Jinyu Li, Bhaskar Mitra, Siân Lindley, Liang Wang, Nan Yang, and Furu Wei

Mixture-of-linear-experts for long-term time series forecasting; Weakly-supervised streaming multilingual speech model with truly zero-shot capability; KBFormer: Diffusion model for structured entity completion; Identifying risks of AI-mediated data access:

January 4, 2024

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。 论文链接：https://arxiv.org/abs/2312.08901 (opens in new tab) 项目链接（将于近日上线）：https://github.com/microsoft/CoT-I...

January 4, 2024 | Esha Choukse, Chaojie Zhang, Íñigo Goiri, Aashaka Shah, Saeed Maleki, Rodrigo Fonseca, and Ricardo Bianchini

Expanded LLM use creates new demands on cloud GPU capacity. Splitwise presents an efficient solution by separating the two essential phases of LLM inference, achieving higher throughput within a limited power budget.

December 22, 2023

AI saw unparalleled growth in 2023, reaching millions daily. This progress owes much to the extensive work of Microsoft researchers and collaborators. In this review, learn about the advances in 2023, which set the stage for further progress in 2024.

December 20, 2023

In this issue of Research Focus: Optimized exit-augmented models for scalable efficient inference; NeurIPS LLM Efficiency Challenge; LLM-empowered automated data exploration; Boosting cloud efficiency with data-driven decision-making and optimization.