How does Google Research decide which problems are worth solving? 💡 In our latest episode of The Google Research Podcast, host Sinéad Bovell sits down with Google Research’s Michael Brenner and Lizzie Dorfman to break down the core framework behind how Google Research selects its moonshots — and why long-term investments in computational neuroscience, genomics, and atmospheric science are paying off today. Here are a few key takeaways from the conversation: 1️⃣ The Three Core Questions Every project at Google Research is evaluated against a simple yet rigorous framework: Is it a big problem? Is it a problem technology can address? And is it something Google is uniquely positioned to solve? 2️⃣ Patience in Foundational Science Breakthroughs rarely happen overnight. Google Research invests early in long-horizon domains — like weather modeling, genomic sequencing, and neural mapping — years before their practical AI applications reach critical scale. 3️⃣ Intersecting AI with Earth & Health Sciences By applying advanced machine learning to climate patterns and complex biological systems, researchers are moving from pure data analysis to predictive modeling that impacts sustainability and global health. 🎬 Watch the cutdown below, or tune in to the full episode here: https://lnkd.in/gJK3yJy8 Explore more Google Research films → https://goo.gle/3NvR4kt Subscribe to the Google Research channel → https://lnkd.in/eeV2KKpB

When researchers gather from around the world for our yearly Research conference to share knowledge, spark bold new ideas, and open our doors to share our top work with the world, the energy is unmatched. But what happens in the sessions is only half the story. Behind every smooth demo and thoughtful session is this team — navigating endless details, solving last-minute hurdles, and creating a space where great science can thrive. Our Research conferences over the years are not just built by the people on stage. It’s also built by the incredible event organizers, program managers, and cross-functional teammates who work tirelessly behind the scenes to bring it all to life. To everyone who put their heart into making last week happen: thank you – I’m so lucky to work alongside you all. Tag someone who made the Research conference better this year 🧡

Will AI soon “solve science”? Or is scientific discovery an endless frontier? 🔬✨ In Episode 2 of The Google Research Podcast, host and AI Futurist Sinéad Bovell sits down with Google Research’s Michael Brenner and Lizzie Dorfman to break down the realistic, transformative role of AI in scientific discovery. While viral headlines often frame AI as a tool that will single-handedly solve complex fields, the real power of AI lies in its ability to amplify human curiosity and accelerate how we ask and test questions. Here are key takeaways from their discussion: 1️⃣ AI as an Accelerator, Not a Replacement. AI isn’t replacing the scientific method, it’s compressing execution timelines. By sifting through vast datasets, generating complex hypotheses, and running high-throughput simulations, AI tools allow researchers to move from idea to breakthrough faster than ever before. 2️⃣ Science Isn’t a Finite Puzzle Science isn’t something to be “solved.” Every question AI helps us answer inevitably reveals a new set of unanswered questions, expanding the boundaries of human knowledge rather than closing them. 3️⃣ The Irreplaceable Human Element No matter how advanced AI models become, intentional curiosity, critical evaluation, and intuitive judgment remain uniquely human. Humans frame the problems worth solving and direct the trajectory of discovery. 💡 Discover how AI is reshaping the scientific frontier and where human creativity fits into the future of research. 👉 Watch the full episode now: https://lnkd.in/gJK3yJy8

This week at our Research@ flagship event, we announced new updates for our experimental Geospatial Reasoning agent and Earth AI capabilities.  Following promising tests with partners, we are expanding early access to our experimental agent to more partners.  In a new technical report, we demonstrate how combining domain-specific foundation models, spatial tools, and upfront planning enables a Geospatial Reasoning agent to carry out complex spatial analysis and planetary prediction that standalone LLMs can’t.  Highlights from this work: –> The agent connects Gemini to the capabilities of Earth AI’s foundation models  (WeatherNext, Flood Forecasting, PDFM, AlphaEarth) using modular skills, dedicated spatial workspaces, and private data support (CSV, GeoJSON). –> Beyond answering complex spatial questions, the agent powers our Planetary Prediction Engine—combining satellite embeddings and population dynamics to train validated models for disease spread, crop forecasting, or environmental risk. –> Real-World Impact with Partners:  Milken Institute: Identifying the highest-impact community resilience hubs for solar energy build out—layering rooftop yield with transit access, heat vulnerability, and local demographics data to compress 6-week siting feasibility studies into 15-min reports to unlock $179 million in clean energy capital.  WHO AFRO & DRC’s INRB: During the recent Ebola outbreak response in the DRC, teams mapped remote mining corridors by cross-referencing satellite forest loss, mining locations, and mobility data—pinpointing 48 exposed settlements and 45,500+ at-risk people in minutes rather than weeks, and modeling transmission risks to plan mobile laboratory deployment. People in Need: Analyzed historical cyclone paths to identify high-exposure villages in minutes rather than days of manual GIS work, unlocking vital time for anticipatory action. GiveDirectly: Evaluated population exposure across wind-speed zones during Cyclone Maila and benchmarked tornado damage in Mississippi within a single workspace. WPP: Connected population dynamics with proprietary data to optimize geospatial marketing while preserving privacy. –> Evaluating Geospatial Reasoning: Planetary datasets are massive and multi-layered. Across four benchmarks—including two new benchmarks we are releasing to researchers, GeoExpertQueries and GeoKTools—our evaluations quantify the essential role of specialized Earth AI tools and upfront planning over out-of-the-box LLMs, enabling the agent to execute multi-step analytical workflows reliably. Congratulations to teams across Google, and thank you to our partners for collaborating with us on the ground. Read the blog by Shravya Shetty and Luke Barrington: goo.gle/4s13F25 Early access interest form: https://lnkd.in/gxt9EfqB

Accelerating scientific discovery starts with empowering the researchers already leading it. Last week we announced the 17 organizations selected for the $36M Google.org Impact Challenge: AI for Science. These academic and nonprofit teams from around the world are tackling complex challenges in health and life sciences and in climate resilience and environmental science. Over the next six months, they will receive pro bono engineering support from Google experts and $3M in Google Cloud credits as part of a dedicated Google.org Accelerator program, helping them turn complex research into robust and scalable solutions. Their teams will be creating models, agents, and datasets that peers in the fields can also use to drive further innovation and discovery. See how they are shaping the future of science: https://lnkd.in/gQuTKMMK

We are bringing agents to the wet-lab bench at Google Research to navigate complex biological landscapes. Learn how we are grounding agents in physical lab robotics to run fast closed-loop molecular design cycles, ultimately discovering single-digit nanomolar protein binders for cancer targets →https://goo.gle/4dKgNTw

Published today in Science: The Google Quantum AI team demonstrates that magic state cultivation works in practice on the Willow processor. Generating magic states is necessary to implement T-gates for a universal gate set. By filtering noisy inputs to cultivate exceptionally pure states, the researchers achieved a 40x error reduction, compared to without cultivation and 99.99% fidelity. In a proof of concept, the team also demonstrated that these states can be transferred onto standard encodings for practical use. This work addresses a critical resource bottleneck for scalable quantum computing. Read the full paper in Science: https://goo.gle/4BabdUn Learn more on News with Google: https://goo.gle/4s9pOeS Watch the explainer: https://goo.gle/47gvBFV

In an emergency like an earthquake even a few seconds of advance warning can save lives. To make early warnings possible at a global scale, our teams built the Android Earthquake Alerts System—turning active Android devices into a crowdsourced network of mini-seismometers to detect early shaking. Since launching in 2021, over one billion alerts were sent to help warn people a few seconds before the shaking starts. 🔔 Today, we’re expanding Android Earthquake Alerts from 98 countries and regions to nearly 200. We’re also improving our latest “Be Ready” alert to give you more information in seconds. This expansion is another step towards our north star for crisis resilience✨:  No one should be surprised by a natural disaster.  Our crisis resilience efforts also help people prepare for other natural disasters – from flood forecasting to wildfire detection to extreme weather. When I started working on crisis resilience over a decade ago, predicting crises like floods, at scale, was considered impossible. Our global hydrologic model, published in Nature, now allows us to provide forecasts up to 7 days in advance, covering over 2B people in 150 countries for significant river flood events. And with our novel Groundsource methodology we’re making progress with flash floods, which just one year ago seemed out of reach.    We’re also making progress with near-real-time fire boundary maps, now live in 34 countries. This summer 3 satellites were launched in space as part of the FireSat initiative with our partners. FireSat, a global satellite constellation, is designed to detect and track fires as small as a classroom within 20 minutes once completed.  From earthquakes, flood forecasting, to wildfire boundary tracking and cyclone forecasting with WeatherNext, our goal is to extend warning horizons for communities everywhere. In 2025 alone, Google helped connect people with crisis information more than 10 million times per day, on average.   We dive deeper into our crisis resilience work and how we are working with partners and communities around the world to better prepare for natural disasters in our new white paper co-authored with Kate Brandt: https://lnkd.in/gwykAsNc Read the Android Earthquake announcement by Boone Spooner: https://lnkd.in/g7NWk-d3

The technical execution of an idea can often be so time-consuming that it gets mistaken for the science itself. But what happens when AI is there to support? In the latest cutdown of The Google Research Podcast, host Sinéad Bovell sits down with Google Research’s Michael Brenner and Lizzie Dorfman to discuss how AI is shifting the scientific workflow — giving researchers more space to focus on framing the right questions, rather than just executing the process. 🎙️✨ Key Takeaways from the Discussion: 1️⃣ Redefining Where Science Happens: When technical implementation, baseline modeling, and data synthesis are streamlined by AI, researchers can elevate their focus to high-level conceptual thinking, intentional curiosity, and defining the core problem. 2️⃣ AI as a Time Multiplier: By automating routine compute and repetitive steps, AI acts as a leverage tool — compressing execution timelines and freeing up mental bandwidth for true breakthroughs. 3️⃣ Framing the Question vs. Executing the Task: The most critical part of discovery isn’t just grinding through the mechanics; it’s posing the right hypothesis. AI provides the speed, but human insight supplies the direction. 💡 Watch the clip below for a glimpse into how AI is redefining scientific exploration. 👉 Watch the full episode: https://goo.gle/4hLVF1D