Microsoft Research
AI for Science
新闻与深度文章
新闻报道 | Royal Society of Medicine
Christopher Bishop: 2024 Ellison Cliffe Lecture
Christopher Bishop, Technical Fellow and Director of Microsoft Research AI for Science, discusses how the deep learning technology that underpins the AI revolution is advancing at an extraordinary pace, with many of the most exciting and impactful applications of this…
奖项 | American Physical Society
Frank Noé, American Physical Society Fellow
The AI for Science partner research manager received this distinction for the development of machine learning methods for advancing the physical sciences, in particular for the many-body sampling problem and the electronic structure problem.
Explore multimodal & small language models, plus advanced benchmarks for AI evaluation. Microsoft researchers are working on breakthroughs in weather prediction, materials design, even a new kind of computer for AI inference and hard optimization problems.
This talk discusses Aurora, a cutting-edge foundation model that offers a new approach to weather forecasting that could transform our ability to predict and mitigate the impacts of extreme events, air pollution, and the changing climate.
In this episode, learn about the latest multimodal AI models, advanced benchmarks for AI evaluation and model self-improvement, and an entirely new kind of computer for AI inference and hard optimization. Discover how these research breakthroughs and more can help…
Advancing time series analysis with multi-granularity guided diffusion model; An algorithm-system co-design for fast, scalable MoE inference; What makes a search metric successful in large-scale settings; learning to solve PDEs without simulated data.
| Gretchen Huizinga, Jake Smith, 和 Aniruddh Vashisth
Printed circuit boards are abundant—in the stuff we use and in landfills. Researcher Jake Smith and professor Aniruddh Vashisth discuss the development of vitrimer-based PCBs that perform comparably to traditional PCBs but have less environmental impact.
“We’re at the very early stage of generative AI and the impacts it will have on work. This is a fast-moving field, and there’s an immense opportunity to take control of the agenda and build truly globally equitable AI systems”,…
Tian Xie introduces MatterGen, a generative model that creates new inorganic materials based on a broad range of property conditions required by the application, aiming to shift the traditional paradigm of materials design with generative AI.