内容来源：https://research.google/blog/research-2026-10-ways-were-amplifying-human-ingenuity/

内容总结：

谷歌研究部门发布十大科研突破，AI助力全球挑战应对

在近日举行的谷歌“Research@”旗舰活动上，谷歌研究与谷歌研究部门副总裁兼总经理约西·马蒂亚斯分享了十项最新科研进展，涵盖地球智能、医疗健康、科学发现、灾害预警及教育工具等多个领域。这些成果旨在将此前难以实现的能力交到全球用户手中，以人工智能放大人类智慧，推动科学发现进入黄金时代。

地球智能：预测公共卫生威胁与推动清洁能源投资

谷歌地球AI（Google Earth AI）进一步扩展早期访问权限，邀请合作伙伴探索三项实验性新功能。同时，谷歌数据集搜索（Google Dataset Search）现已支持查询超过1亿个公共数据集，TimesFM模型则用于地球即时预报与预测迭代。这些能力依托70余种地理空间工具，包括遥感基础模型（RSFM）和人口动态基础模型（PDFM），并经过今日发布的地球AI技术报告中的实证基准验证，可帮助开发者、卫生领导者和企业团队自动化复杂的地理空间工作流程。

为进一步支持社会公益，Google.org将提供1000万美元资金，用于危机韧性和公共卫生领域的组织。其中200万美元拨给米尔肯研究所，用于打通经济适用房、清洁能源、宽带、安全供水等关键基础设施的瓶颈。

科学发现：多智能体系统加速研究进程

谷歌构建了多智能体编排器，使科学家能够复现先前的方法和分析，并将其应用于新场景以解决重要研究难题。例如，该工具帮助研究人员将流体力学中求解逆向设计问题的新方法应用于导尿管设计，在模拟中发现了更能预防细菌感染的导管设计方案。

这一工具与现有加速科学工作流的工具形成互补。Empirical Research Assistance使科学家能够并行生成和测试数千种代码变体，已帮助研究人员在从生物学到宇宙学再到经济学的多个领域取得成果。与谷歌DeepMind合作构建的Co-Scientist是一种多智能体工具，可生成新颖假设，已协助斯坦福大学等合作伙伴发现可用于治疗肝纤维化的老药新用方案。

同行评审：Deep Review向公众开放

谷歌创建了Deep Review，这是一种智能体系统，可对复杂文档进行严格验证。随着AI越来越多地被用于帮助专家起草技术文档，这一工具尤为重要。许多谷歌研究人员和外部作者已使用该系统的早期版本评审了提交给顶级计算机科学会议的16000余篇研究论文。该系统通过检查逻辑一致性和事实依据来减轻认知负担，同时科学家仍对创作方向保持主导权。即日起，Deep Review向公众开放，可帮助人们验证从法律协议到财务报告等各种技术文档。

边缘AI：新一代低功耗芯片即将发布

随着可穿戴设备兴起，硬件制造商需要开发高效边缘应用的工具。今年早些时候，谷歌推出了首款搭载谷歌Coral NPU加速器核心的Synaptics Coralboard。下周，谷歌将发布由Synaptics使用其开源IP共同设计的下一代低功耗边缘AI芯片，为设计工程师构建下一代智能眼镜、手表、联网摄像头、流媒体设备及工业视觉和语音系统提供更高效率和性能。

数值推理：TimesFM与TabFM集成至Gemini商业版

语言模型擅长处理文本，但商业和运营决策还依赖数字。TimesFM和TabFM是专为推理原始数值分布而设计的基础模型，分别针对时间序列流和结构化表格数据。两个模型均能开箱即用，无需训练即可提供准确预测，在各自领域的领先公共基准中排名靠前，并已向所有Cloud BigQuery用户开放。谷歌今日宣布即将把这两个模型集成到Gemini商业版中，企业主、分析师和运营负责人可基于复杂运营表格和数百万行数据预测产品销售或医院床位容量等趋势，敏感数据保留在组织边界内，用户可在数秒内获得所需答案。

医疗AI：MedGemma下载量超千万，AMIE推进真实临床研究

上周发表于《自然·医学》的MedGemma已被超过65000名创新者下载逾1000万次，作为开发者构建专业应用的开源基础。开发者已将其适配于当地社区需求，从拥挤诊所的快速分诊到眼病和宫颈癌筛查。谷歌最新研究推进了MedGemma的工具使用能力，使其能够调用可用的健康记录和临床医生依赖的其他应用，并正在探索乳腺X光、超声和心电图等新模态。早期迹象表明，在回答临床问题、解读放射影像、皮肤图像和病理切片等能力上有显著提升。

在AMIE研究方面，谷歌探索AI在临床推理和对话中的应用。今年发表于《自然》的研究将AMIE的能力从初步诊断推进到长期疾病管理，发表于《自然·医学》的研究展示了其跨模态数据（如医学图像和实验室结果）推理能力。发表在《柳叶刀》的新研究展示了其在真实临床环境中的应用前景：在贝斯以色列女执事医疗中心，98名患者在紧急护理就诊前安全地咨询了AMIE，临床医生报告称在75%的案例中AI摘要帮助他们做好了准备。谷歌还在推进AMIE的多模态能力向实时视频咨询发展，并与Included Health合作在全国范围内开展AI在真实虚拟护理环境中的随机研究。

灾害预警：山洪预测模型取得突破

谷歌的全球水文模型（发表于《自然》）可提前7天提供可靠的河流洪水预报，覆盖150多个国家超过20亿人。然而一年前，山洪预测仍似乎遥不可及。Groundsource是谷歌近期推出的AI驱动方法，将公开的危机信息转化为高质量历史数据集。谷歌首先将Groundsource与Gemini结合分析洪水相关新闻报道，创建了包含超过260万次洪水事件的结构化数据集。该数据集帮助克服了山洪地面真实数据缺乏的问题，并训练出新模型，可提前24小时预测城市地区发生山洪的可能性。该数据集现已纳入谷歌地球AI集合，并向公众开放以帮助科学家开展进一步研究。

教育工具：生成式UI打造互动STEM模拟

通过生成式UI，谷歌可超越静态输出，动态生成功能性交互软件。谷歌在教育领域探索了这一研究实验，通过教学护栏微调生成式UI，使教师能够创建符合课程的定制引导式STEM模拟。示例库包含30多个AI生成的学习互动内容，均经教师审核，涵盖初中和高中物理、化学、生物和数学。学校可注册谷歌教育试点计划分享反馈，帮助改进模拟并开发更多供公众使用的内容。

协作工具：Play with Putty开放等候名单

Play with Putty允许团队在共享环境中共同构建和制作原型，而不打断创作流程。它展示了当AI模型变得更加高效、能够从自然语言提示生成完整界面时的可能性。用户可直接与界面任何部分交互，多人编辑支持无缝实时协作。谷歌最初设计该工具是为了让研究人员和团队跨谷歌协作制作原型，例如探索谷歌地球AI的新解决方案和可视化。此后，谷歌在Google Labs上开放了等候名单，将人类协作的乐趣带给各地使用AI的团队。

马蒂亚斯表示，这十项更新展示了谷歌广泛研究组合在多领域的最新进展。当技术放大人类智慧时，技术才能成功。在这个研究与科学发现的黄金时代，机遇比以往任何时候都更大——开创突破并分享赋能他人的工具。

中文翻译：

2026年10月14日
尤西·马蒂亚斯，Google 副总裁兼 Google Research 总经理

今天，在我们旗舰活动 Research@ 上，我们分享了一系列研究突破，它们放大了人类的创造力。从行星级智能到先进的医疗工作流，我们正将曾经遥不可及的能力交到全世界人们手中。以下是十大亮点。

我们正身处研究与科学发现的黄金时代。对于 Google Research 的科学家和研究人员而言，机遇远不止于开创性突破。我们还在构建并分享强大的工具，赋能全球生态。AI 是人类创造力的放大器，为好奇心和严谨探索创造了蓬勃生长的条件。

今天，在位于山景城的 Research@ 活动上，我们正在展示如何将前所未有的洞察与能力交到医生、科学家、开发者、公共卫生官员等人手中。

以下是十大亮点：

应对全球性挑战需要准确、真实的行星级智能。借助 Google Earth AI，人们可以预测公共卫生威胁、加速清洁能源投资、为企业提供前瞻洞察，等等。今天，我们正在扩大早期访问范围，并邀请合作伙伴探索三种新方式，来使用 Google Earth AI 全新的实验性能力：

我们还宣布集成 Google Dataset Search，可查询超过 1 亿个公开数据集，以及 TimesFM，用于行星级临近预报和预报迭代。所有这些能力由 70 多种地理空间工具驱动，包括遥感基础模型（RSFM）和人口动态基础模型（PDFM）等行星级基础模型。它们已通过今日发布的《Earth AI 技术报告》中详述的实证基准验证。它们使开发者、卫生领域领导者和企业团队能够自动化复杂的地理空间工作流。

为进一步支持社会公益事业，Google.org 将提供 1000 万美元资金，资助致力于危机韧性和公共卫生的组织。其中包括向米尔肯研究所提供 200 万美元，用于打通经济适用房、清洁能源、宽带、安全供水系统及其他关键基础设施的瓶颈。

现有文献是推动新科学发现的巨大知识来源。我们构建了一个多智能体编排系统，使科学家能够复现此前的方法和分析，然后将其应用于新场景，以解决重要的研究挑战。例如，这帮助研究人员将一种用于解决流体力学逆向设计问题的新方法应用于导尿管设计。他们发现了一些导尿管设计方案，在模拟中能更好地预防细菌感染。

这补充了我们加速科学工作流的现有工具。Empirical Research Assistance 使科学家能够并行生成并测试数千种代码变体，已帮助研究人员在从生物学到宇宙学再到经济学等领域产出成果。Co-Scientist 与 Google DeepMind 合作构建，是一个多智能体工具，能够生成新颖假设，并已帮助斯坦福大学等合作伙伴发现可用于抗击肝纤维化的再利用药物。

Thorough peer review is a core part of the scientific method. 我们创建了 Deep Review，这是一个智能体系统，可对复杂文档进行严格验证。随着 AI 越来越多地被用于帮助专家起草技术文档，这一点尤为重要。许多 Google 研究人员和外部作者已经使用该系统的早期版本，审阅了提交给顶级计算机科学会议的 16000 多篇研究论文。它通过检查逻辑一致性和事实依据来承担繁重的认知工作，而科学家仍然掌握创作方向的最终决定权。今天，我们正在向公众扩大 Deep Review 的访问范围，帮助人们验证从法律协议到财务报告等各种技术文档。

随着可穿戴设备的兴起，硬件制造商需要工具来开发高效的边缘应用，以满足客户对全天候性能的高期望。今年早些时候，我们推出了 Synaptics 的首款 Coralboard，采用 Google 的 Coral NPU 加速器核心。它使 AI 和 ML 工程师能够快速制作原型并构建设备。下周，我们将揭晓我们联合设计的下一代低功耗 Edge AI 芯片，该芯片由 Synaptics 使用我们的开源 IP 打造。该芯片为设计工程师构建下一代智能眼镜和手表、联网摄像头、流媒体设备以及工业视觉和语音系统，带来更高的效率和性能。

语言模型擅长处理文本，但业务和运营决策还依赖数字。TimesFM 和 TabFM 是专为推理原始数值分布而设计的基础模型——时间序列流（随时间变化的数据）和结构化表格数据（如电子表格）。这两个模型无需训练即可开箱即用地提供准确预测。它们在其领域的领先公开基准中名列前茅，并已向所有 Cloud BigQuery 用户开放。今天，我们宣布即将把这些模型集成到面向企业的 Gemini 中。很快，企业主、分析师、运营负责人或协调人员就能基于复杂的运营表格和数百万行数据，预测产品销售或医院床位容量等趋势。敏感数据安全地保留在组织边界内，人们可在数秒内获得所需答案。

上周发表在《自然·医学》上的 MedGemma，已被超过 65000 名独立创新者下载超过 1000 万次。它作为开放基础，供开发者构建专业应用。开发者已对其进行调整，以满足当地社区的需求，从在过度拥挤的诊所中实现更快分诊，到筛查眼病和宫颈癌。

随着开发者针对更多专科和护理场景微调 MedGemma，我们正在推动开放健康 AI 的前沿。我们的最新研究通过工具使用推进了 MedGemma，使其能够调用可用的健康记录以及临床医生依赖的其他应用。我们正在探索乳腺钼靶、超声和心电图等新模态。早期迹象表明，其在回答临床问题和解读放射影像、皮肤图像及病理切片等能力上将取得显著提升。借助这些进展，我们希望拓展创新者能够构建的范围，将有效医疗带给全球更多社区。

通过我们对 AMIE 的研究，我们正在探索 AI 在临床推理和对话中的应用，以了解它如何赋能临床医生并加强医患关系。我们今年发表在《自然》上的研究将 AMIE 的能力从初步诊断推进到长期疾病管理，而在《自然·医学》上，我们展示了它如何跨医学图像和实验室结果等多模态数据进行推理。

我们发表在《柳叶刀》上的新研究，为其能力如何在真实临床环境中落地提供了令人鼓舞的前景。在贝斯以色列女执事医疗中心，98 名患者在紧急护理就诊前安全地咨询了 AMIE，临床医生报告称，在 75% 的案例中，AI 摘要帮助他们做好了准备。我们还在推进 AMIE 的多模态能力，以实现实时视频问诊，并与 Included Health 合作，启动了一项在全国范围内针对真实虚拟护理场景中 AI 应用的随机研究。

AMIE 是我们可追溯到里程碑式 MedPalm 论文的严谨研究旅程的一部分，而现在仍处于早期阶段。

AI 可以帮助我们以更高精度和更长提前量预测灾害，让社区有更好的机会做好准备并拯救生命。我们一直在朝着危机韧性的北极星目标取得进展：一个没有人会对自然灾害感到意外的世界。

以洪水预报为例。我们发表在《自然》上的全球水文模型，使我们能够为最具影响力和破坏性的河流洪水提供覆盖 150 多个国家、超过 20 亿人口的可靠河流预报。然而就在一年前，预测山洪似乎仍遥不可及。Groundsource 是我们最近推出的一种 AI 驱动方法，可将关于危机的公开信息转化为高质量的历史数据集，使研究人员能够构建更准确的预测模型。

我们最初将 Groundsource 与 Gemini 结合，用于分析洪水相关新闻报道，并创建了一个包含超过 260 万次洪水事件的结构化数据集。该数据集帮助我们克服了山洪缺乏地面真值数据的问题，并训练出一个新模型，能够提前最多 24 小时预测城市地区发生山洪的可能性。该数据集现已成为 Google Earth AI 集合的一部分，我们已将其公开，以帮助科学家开展进一步分析和研究。

借助生成式 UI，我们可以超越静态输出，动态生成功能性、交互式软件。我们的研究实验在教育领域对此进行了探索。我们用教学护栏微调生成式 UI，使教师能够创建符合其课程的自定义引导式 STEM 模拟。示例库包含 30 多个 AI 生成的学习互动内容，均由教师审核，涵盖初中和高中物理、化学、生物和数学。学校可以报名参加 Google for Education 试点项目并分享反馈。这将帮助我们改进这些模拟，并开发更多供公众使用的内容，使练习更加互动和引人入胜。

Play with Putty 让团队能够在共享环境中共同构建和制作原型，而不会打断他们的创作流。它展示了当 AI 模型变得更高效、并能够根据自然语言提示生成完整界面时所可能实现的能力。人们可以直接与界面的任何部分交互，而多人编辑支持无缝实时协作。我们最初设计 Play with Putty，是为了让 Google 内部的研究人员和团队能够共同制作原型——例如，使用 Google Earth AI 探索新解决方案和可视化。此后，我们已在 Google Labs 开放候补名单，将人类协作的乐趣带给各地使用 AI 的团队。

这 10 项更新凸显了我们跨多个领域的广泛研究组合的最新进展。当我们放大人类创造力时，技术才会成功。在这个研究与科学发现的黄金时代，机遇比以往任何时候都更大——去开创突破，并分享赋能他人的工具，与众多伙伴携手合作。我们对未来的旅程充满期待。

英文来源：

October 14, 2026
Yossi Matias, Vice President, Google & GM, Google Research
Today, at our flagship Research@ event, we're sharing research breakthroughs that amplify human ingenuity. From planetary intelligence to advanced medical workflows, we’re putting capabilities once out of reach in the hands of people worldwide. Here are 10 highlights.
We are living through a golden age of research and scientific discovery. For the scientists and researchers at Google Research, the opportunity extends beyond pioneering breakthroughs. We’re also building and sharing robust tools that empower the global ecosystem. AI acts as an amplifier of human ingenuity, creating the conditions for curiosity and rigorous inquiry to flourish.
Today, at our Research@ event in Mountain View, we are demonstrating how we put never-before-possible insights and capabilities into the hands of doctors, scientists, developers, public health officials, and others.
Here are 10 highlights:
Addressing global challenges requires accurate, real-world planetary intelligence. With Google Earth AI, it becomes possible to forecast public health threats, accelerate clean energy investments, provide businesses with foresight, and more. Today, we’re expanding early access and inviting partners to explore three new ways to access Google Earth AI’s new, experimental capabilities:
We’re also announcing the integration of Google Dataset Search for querying from more than 100 million public datasets, and TimesFM for planetary nowcasting and forecasting iterations. All these capabilities are powered by over 70 geospatial tools including planetary foundation models like Remote Sensing Foundation Model (RSFM) and Population Dynamics Foundation Model (PDFM). They are validated by empirical benchmarks detailed in today’s Earth AI Technical Report. They enable developers, health leaders and enterprise teams to automate complex geospatial workflows.
To further support social impact efforts, Google.org is providing $10 million in funding to organizations working on crisis resilience and public health. This includes $2 million to The Milken Institute to unlock infrastructure bottlenecks for affordable housing, clean energy, broadband, safe water systems, and other essential infrastructure.
Existing literature offers a great source of knowledge for driving new scientific discoveries. We built a multi-agent orchestrator, enabling scientists to reproduce previous methods and analyses and then apply them in new settings to tackle important research challenges. As an example, this helped researchers apply a new methodology for solving inverse design problems in fluid mechanics to the design of urinary catheters. They discovered catheter designs that, in simulation, were better at preventing bacterial infections.
This complements our existing tools to accelerate scientific workflows. Empirical Research Assistance enables scientists to generate and test thousands of code variations in parallel, and has helped researchers produce results from biology to cosmology to economics. Co-Scientist, built in collaboration with Google DeepMind, is a multi-agent tool that can generate novel hypotheses and has already helped partners, like researchers at Stanford University, uncover repurposed medicines to fight liver fibrosis.
Thorough peer review is a core part of the scientific method. We created Deep Review, an agentic system that provides rigorous verification across complex documents. This is particularly important as AI is increasingly being used to help experts draft technical documents. Many Google researchers and external authors have already used an early version of this system to review over 16,000 research publications submitted to top-tier computer science conferences. It helps with the heavy cognitive lifting by checking logical consistency and grounding facts, while scientists maintain authority over creative direction. Today, we’re expanding access to Deep Review to the general public to help people verify a variety of technical documents from legal agreements to financial reports.
With the rise of wearable devices, hardware manufacturers need the tools to develop efficient edge applications to meet the high expectations of customers for all-day performance. Earlier this year, we launched the first Coralboard from Synaptics, using Google’s Coral NPU accelerator core. It enables AI and ML engineers to rapidly prototype and build devices. Next week, we’ll be revealing our co-designed, next-gen, low-power Edge AI chip, created by Synaptics using our open source IP. The chip delivers greater efficiency and performance for design engineers building the next generation of smart glasses and watches, connected cameras, streamers, and industrial vision and voice systems.
While language models excel with text, business and operational decisions also rely on numbers. TimesFM and TabFM are foundational models engineered specifically to reason over raw numerical distributions — time-series streams (data that changes over time) and structured tabular data (like spreadsheets). Both models deliver accurate predictions out of the box with zero training. They rank at the top of leading public benchmarks in their fields and are available to all Cloud BigQuery users. Today, we are announcing our upcoming integration of these models into Gemini for business. Soon, business owners, analysts, operation leads or coordinators can forecast trends like product sales or hospital bed capacity, based on complex operational tables and millions of rows of data. Sensitive data stays safely within an organization’s perimeter, and people get the answers they need in seconds.
MedGemma, published last week in Nature Medicine, has been downloaded more than 10 million times by more than 65,000 unique innovators. It serves as an open foundation for developers building specialized applications. Developers have adapted it to meet the needs of their local communities, from enabling faster triage in overcrowded clinics to screening for eye disease and cervical cancer.
As developers fine-tune MedGemma for more specialities and care settings, we’re pushing the frontier of open health AI. Our latest research advances MedGemma with tool use, so it can draw on available health records and other applications clinicians rely on. We’re exploring new modalities like mammography, ultrasound and electrocardiograms. Early signs point to substantial gains in capabilities, such as answering clinical questions and interpreting radiology scans, skin images and pathology slides. With these advances, we hope to expand what innovators can build, bringing effective healthcare to more communities around the world.
With our research on AMIE, we’re exploring the use of AI in clinical reasoning and conversations, to understand how it could empower clinicians and strengthen the patient-clinician relationship. Our research this year in Nature advanced AMIE’s capabilities beyond initial diagnosis toward long-term disease management, and in Nature Medicine, we showed how it can reason across multimodal data like medical images and lab results.
Our new study in The Lancet offers a promising look into how its capabilities could come to life in real-world clinical settings. At Beth Israel Deaconess Medical Center, 98 patients safely consulted AMIE ahead of urgent care visits, and clinicians reported that the AI summaries helped them prepare in 75% of cases. We’re also advancing AMIE’s multimodal capabilities towards real-time video consultations and have embarked on a nationwide randomized study of AI in real-world virtual care settings in partnership with Included Health.
AMIE is part of a rigorous research journey going back to our landmark MedPalm paper, and it’s still early days.
AI can help us predict disasters with greater accuracy and lead time, giving communities a better chance to prepare and save lives. We’ve been making progress towards our crisis resilience north star: a world where no one is surprised by a natural disaster.
Take flood forecasting. Our global hydrologic model, published in Nature, allows us to provide reliable river forecasts up to 7 days in advance covering more than 2 billion people in over 150 countries for the most significant and impactful riverine floods. Yet just one year ago, predicting flash floods still seemed out of reach. Groundsource is our recent AI-powered methodology that turns publicly available information about crises into high-quality historical datasets, allowing researchers to build more accurate predictive models.
We first applied Groundsource with Gemini to analyze flood-related news reports and created a structured dataset with over 2.6 million flood events. This dataset helped us overcome a lack of ground-truth data for flash floods and train a new model that can predict the likelihood of flash flooding in urban areas up to 24 hours in advance. The dataset is now part of the Google Earth AI collection and we've made it openly available to help scientists conduct further analysis and research.
With generative UI, we can go beyond static outputs to dynamically generate functional, interactive software. Our research experiment explores this in the domain of education. We fine-tune generative UI with pedagogical guardrails to enable teachers to create custom, guided STEM simulations tailored to their curriculum. The sample library features over 30 AI-generated learning interactives, all vetted by teachers, for middle and high school physics, chemistry, biology, and math. Schools can sign up to the Google for Education Pilot Program to share feedback. This will help us improve the simulations and develop more for public use, making practice far more interactive and engaging.
Play with Putty allows teams to build and prototype together in a shared environment without breaking their creative flow. It demonstrates what’s possible as AI models become more efficient and capable of generating entire interfaces from natural language prompts. People can interact directly with any part of the interface, while multi-player editing supports seamless, real-time collaboration. We first designed Play with Putty to empower our researchers and teams across Google to prototype together — for example, exploring new solutions and visualizations with Google Earth AI. We’ve since opened up the waitlist on Google Labs to bring the joy of human collaboration to teams using AI everywhere.
These 10 updates highlight recent progress from our extensive research portfolio across many domains. Technology succeeds when we amplify human ingenuity. In this golden age of research and scientific discovery, the opportunity is greater than ever — to pioneer breakthroughs and share tools that empower others, in collaboration with many. We’re excited about the journey ahead.