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#研究
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  1. Microsoft Research

    Forecasting space weather risks on power grids

    中文摘要

    极端的太空天气事件可能损坏地球上的电力系统，并降低GPS精度和卫星运行。一个新的机器学习系统可以在风暴到达前30至60分钟预测损坏可能发生的位置。文章《预测电网上的太空天气风险》最先发表于微软研究院。

    英文原文

    Extreme space-weather events can damage power systems on Earth and degrade GPS accuracy and satellite operations. A new machine learning system can predict where damage is likely to occur 30-60 minutes before a storm arrives. The post Forecasting space weather risks on power grids appeared first on Microsoft Research .

  1. Microsoft Research

    Introducing Quine: An AI research system designed for the complexity of biology

    中文摘要

    生物学不是在孤岛中运行的，生物的AI表示也不应如此。Quine 是一项早期研究项目，旨在创建一个生物的多模态世界模型。通过连接不同生物尺度和模态的见解，Quine 帮助科学家在计算上搜索远超直觉允许范围的空间，并在实验前对假设进行优先排序。实验结果提供了重要的反馈，帮助研究人员明确未来的研究方向。该文章《介绍 Quine：一种为生物学复杂性设计的AI研究系统》最先发布于微软研究院。

    英文原文

    Biology doesn't operate in silos, and neither should the AI representation of it. Quine is an early-stage research effort to create a multimodal world model of biology. By connecting insights across biological scales and modalities, Quine helps scientists computationally search a space far larger than intuition allows and prioritize hypotheses before they reach the lab. Experimental results provide important feedback, helping researchers sharpen future research directions. The post Introducing Quine: An AI research system designed for the complexity of biology appeared first on Microsoft Research .

  2. Microsoft Research

    One year in: How Microsoft Research Asia – Singapore is advancing research, partnership and talent for real-world impact

    中文摘要

    自去年成立以来，微软亚洲研究院——新加坡实验室已打下了坚实的基础，加深了政府、学术界和产业界之间的合作，并探索了前沿人工智能研究如何创造实际价值。文章《一年之后：微软亚洲研究院——新加坡如何推动研究、合作与人才发展，以实现实际影响》最先发布于微软研究院。

    英文原文

    Since launching a year ago, the Microsoft Research Asia — Singapore lab has established a strong foundation, deepened collaboration across government, academia, and industry, and explored how frontier AI research can create real-world value. The post One year in: How Microsoft Research Asia – Singapore is advancing research, partnership and talent for real-world impact appeared first on Microsoft Research .

  1. Microsoft Research

    Offloaded inference for real-world physical AI robotics

    中文摘要

    机器人正在变得越来越聪明，但它们的硬件如何跟上这种进步呢？微软最新研究发现，将人工智能推理从机器人本身移出，可以提高任务成功率，提升效率，并支持更复杂的物理人工智能工作负载。《将推理任务卸载至现实世界中的物理人工智能机器人》一文最先发布于微软研究院。

    英文原文

    Robots are getting smarter, but how can their hardware match that growth? New Microsoft Research findings show that moving AI inference beyond the robot can improve task success, boost efficiency, and support more advanced physical AI workloads. The post Offloaded inference for real-world physical AI robotics appeared first on Microsoft Research .

  1. Microsoft Research

    Improving synthesis prediction of small molecules at scale with RetroChimera

    中文摘要

    定制分子正在推动医学、材料和农业的发展，但它们的生产过程缓慢且成本高昂。一篇新的《自然》论文介绍了RetroChimera，这是一种预测模型，有助于加速化学合成，帮助研究人员探索各种分子。《通过RetroChimera大规模改进小分子合成预测》一文最先发表于微软研究院。

    英文原文

    Custom-made molecules are advancing medicine, materials, and agriculture, but producing them is slow and expensive. A new Nature paper highlights RetroChimera, a predictive model that helps accelerate chemical synthesis, helping researchers explore a wide range of molecules. The post Improving synthesis prediction of small molecules at scale with RetroChimera appeared first on Microsoft Research .

  2. Microsoft Research

    GigaPath-Flash and GigaTIME-Flash: Toward population-scale discovery with efficient pathology foundation models

    中文摘要

    如果病理学基础模型能够以更少的资源做更多呢？GigaPath-Flash 和 GigaTIME-Flash 在降低计算需求的同时保持了强大的性能，为更大规模的研究和更广泛的探索打开了大门。帖子《GigaPath-Flash 和 GigaTIME-Flash：通过高效的病理学基础模型实现群体规模的发现》最先出现在微软研究院。

    英文原文

    What if pathology foundation models could do more with less? GigaPath-Flash and GigaTIME-Flash cut computational demands while maintaining strong performance, opening the door to larger studies and broader exploration. The post GigaPath-Flash and GigaTIME-Flash: Toward population-scale discovery with efficient pathology foundation models appeared first on Microsoft Research .