Research@ 2026: 10 ways we're amplifying human ingenuity Google Research announced 10 research highlights at its Research@ event in Mountain View on October 14, 2026, including expanded early access to Google Earth AI's experimental geospatial capabilities, integration of Google Dataset Search across more than 100 million public datasets, and the TimesFM foundation model for planetary nowcasting and forecasting. The Earth AI capabilities are powered by over 70 geospatial tools, including the Remote Sensing Foundation Model (RSFM) and Population Dynamics Foundation Model (PDFM), and are validated by empirical benchmarks in the Earth AI Technical Report. Google.org is also providing $10 million in funding for crisis resilience and public health organizations, including $2 million to The Milken Institute for infrastructure bottlenecks in affordable housing, clean energy, broadband, and safe water systems. 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 https://ai.google/earth-ai/ , it becomes possible to forecast public health threats https://blog.google/innovation-and-ai/technology/health/google-earth-ai/ , accelerate https://www.youtube.com/watch?v=SlIR1 DS54Y clean energy investments, provide businesses https://www.wpp.com/en/news/2026/04/wpp-bridges-the-gap-between-digital-marketing-and-physical with foresight, and more. Today, we’re expanding early access https://forms.gle/3kp1t9zq7BqvYxwNA and inviting partners to explore three new ways to access Google Earth AI’s new, experimental capabilities https://research.google/blog/putting-earth-ai-to-work-geospatial-agents-for-our-planets-biggest-challenges/ : We’re also announcing the integration of Google Dataset Search https://datasetsearch.research.google.com/ for querying from more than 100 million public datasets, and TimesFM https://research.google/blog/timesfm-3-a-zero-shot-foundation-model-for-multivariate-forecasting/ 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 https://research.google/blog/geospatial-reasoning-unlocking-insights-with-generative-ai-and-multiple-foundation-models/ RSFM and Population Dynamics Foundation Model https://research.google/blog/insights-into-population-dynamics-a-foundation-model-for-geospatial-inference/ PDFM . They are validated by empirical benchmarks detailed in today’s Earth AI Technical Report https://arxiv.org/abs/2610.15750 . They enable developers, health leaders and enterprise teams to automate complex geospatial workflows. To further support social impact efforts, Google.org http://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 https://milkeninstitute.org/ 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 https://research.google/blog/empirical-research-assistance-era-from-nature-publication-to-catalyzing-computational-discovery/ enables scientists to generate and test thousands of code variations in parallel, and has helped researchers produce results https://research.google/blog/four-ways-google-research-scientists-have-been-using-empirical-research-assistance/ from biology to cosmology to economics. Co-Scientist https://deepmind.google/blog/co-scientist-a-multi-agent-ai-partner-to-accelerate-research/ , 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 https://pubmed.ncbi.nlm.nih.gov/40946179/ repurposed medicines to fight liver fibrosis. Thorough peer review is a core part of the scientific method. We created Deep Review https://research.google.com/deepreview/deepreview , an agentic system https://research.google/blog/analyzing-technical-documents-with-deep-review 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 http://developers.google.com/coral/products , using Google’s Coral NPU https://research.google/blog/coral-npu-a-full-stack-platform-for-edge-ai/ 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 https://research.google/blog/timesfm-3-a-zero-shot-foundation-model-for-multivariate-forecasting/ and TabFM https://research.google/blog/introducing-tabfm-a-zero-shot-foundation-model-for-tabular-data/ 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 https://cloud.google.com/blog/products/ai-machine-learning/welcome-to-gemini-at-work-2026?e=48754805 . 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 https://research.google/blog/next-generation-medical-image-interpretation-with-medgemma-15-and-medical-speech-to-text-with-medasr/ , published https://www.nature.com/articles/s41591-026-04626-w last week in Nature Medicine https://www.nature.com/nm/ , 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 https://blog.google/innovation-and-ai/technology/health/medgemma-global-healthcare/ . Developers have adapted it to meet the needs of their local communities, from enabling faster triage in overcrowded clinics to screening for eye disease https://developers.google.com/health-ai-developer-foundations/showcase/visilant and cervical cancer https://developers.google.com/health-ai-developer-foundations/showcase/dawamom . 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 https://sites.research.google/gr/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 https://www.nature.com/articles/s41586-026-10764-5 advanced AMIE’s capabilities beyond initial diagnosis toward long-term disease management, and in Nature Medicine , we showed https://www.nature.com/articles/s41591-026-04371-0 how it can reason across multimodal data like medical images and lab results. Our new study