We’re partnering with NotebookLM to launch a series of Featured Notebooks backed by Google Research. Now you’ll be able to interact with the breadth and depth of our research — right on your NotebookLM home page. To start, we released two genomics-focused notebooks: “how do scientists like genetics to health” and “how can scientists know what’s in your genome”. Explore our research via chatting with NotebookLM, diving into audio & video overviews, studying infographics, and more! Check out “how do scientists link genetics to health” →https://goo.gle/49Nn5k0 Check out “how can scientists know what’s in your genome” →https://goo.gle/4nY0gNY
About us
From conducting fundamental research to influencing product development, our research teams have the opportunity to impact technology used by billions of people every day. We aspire to make discoveries that impact everyone, and sharing our research and tools to fuel progress in the field is fundamental to our approach.
- Website
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https://research.google/
External link for Google Research
- Industry
- Technology, Information and Internet
- Company size
- 1,001-5,000 employees
Updates
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Together with Google DeepMind, we announce the release of Natural Forests of the World 2020, a critical, AI-powered baseline map for deforestation and degradation monitoring. This new 10-meter resolution map distinguishes natural forests from other tree cover with 92.2% accuracy, addressing a key challenge in conservation. Natural Forests of the World 2020 is powered by a novel multi-modal temporal-spatial vision transformer (MTSViT) model, and designed to serve as a resource for: Companies conducting due diligence for new regulations like the European Union Regulation on Deforestation-free Products. Governments monitoring deforestation. Conservation groups targeting ecosystem protection. This research was developed in collaboration with International Institute for Applied Systems Analysis (IIASA) and World Resources Institute. Read the blog: goo.gle/47PPROh
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"Building a fault-tolerant quantum computer is the grand challenge of hardware. Using it is the grand challenge of applications." To map this journey from an abstract idea to a deployed, real-world tool, Ryan Babbush, Director of Research, Quantum Algorithms and Applications, and the Google Quantum AI team developed a five-stage framework, published in our paper, "The Grand Challenge of Quantum Applications." This framework provides a clear roadmap by defining the five stages of application development: Stage I: Discovery (Abstract Algorithm) Stage II: Finding the Right Problem Instances Stage III: Establishing Real-World Advantage Stage IV: Engineering for Use (Resource Estimation) Stage V: Application Deployment Learn more about where key applications stand today → https://goo.gle/3JMJoMc
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Researchers at Google Quantum AI introduce Decoded Quantum Interferometry (DQI), a new quantum algorithm that links optimization and decoding. DQI shows how a large-scale quantum computer could solve certain optimization problems that are intractable for classical methods. Learn more → https://goo.gle/43v6Nsa
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Here’s the release of JAX-Privacy 1.0, a library for #DifferentiallyPrivate #ML on the high-performance JAX computing library, making it easier for researchers & developers to build with both state-of-the-art DP algorithms & the scalability from JAX →https://goo.gle/4reb9OL
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VP and Founder of Google Quantum AI Hartmut Neven recently sat down with SVP, Research, Labs, Technology & Society, James Manyika at Research@ to discuss our latest breakthrough algorithm, Quantum Echoes and the future of quantum computing applications. Watch the fireside chat here: https://goo.gle/43YNBmF
Research@ 2025: Fireside Chat with James Manyika and Hartmut Neven
https://www.youtube.com/
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Congratulations to our team for being recognized in the 2025 PCMag Technical Excellence Awards for our breakthrough work using Willow to solve a long-established challenge for quantum error correction, namely reducing errors exponentially as more qubits are added. This opens up the possibility for quantum computers to solve-real world issues that currently aren’t possible with classic computers. Learn more ↓ https://goo.gle/3JOGxlS
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Today, we introduce Nested Learning, a groundbreaking approach to machine learning that redefines how we view and design AI models. By treating ML architecture and optimization as interconnected, multi-level learning problems, Nested Learning offers a new dimension for building more capable AI. This paradigm helps address critical challenges like "catastrophic forgetting", enabling models to continually acquire new knowledge without sacrificing old proficiencies. Our proof-of-concept model, Hope, is built on a self-modifying architecture and demonstrates superior performance in language modeling and long-context memory management. Learn more: https://goo.gle/49Fozww
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Learn about Google’s approach to building AI to help improve learning outcomes for everyone in this new paper, co-authored by Yossi Matias, our VP and Head of Google Research ↓
Today, we introduce our position paper “AI and the Future of Learning”, outlining Google’s approach to building AI to help improve learning outcomes for everyone. 📄 Our core focus at Google Research is driving breakthrough research and bridging fundamental scientific advancement into tangible solutions that address critical global needs. This is the magic cycle of research in action. This paper looks at how Google is leveraging its world leadership in machine learning to responsibly enable AI for learning. Our approach is grounded in pedagogical principles and the very best of learning science. 📄 Customized Learning at Scale: Google is actively developing AI models like Gemini, guided by our LearnLM efforts, to create deeply personalized teaching and tutoring experiences at scale. This shifts learning from passive consumption to active, deep understanding for everyone. 📄 Empowering Educators: AI is designed to serve as a powerful teaching assistant, alleviating administrative tasks and freeing up teachers' time for the essential human aspects of the job: mentoring, inspiring curiosity, and fostering connections. 📄 Addressing Critical Challenges: AI presents an immense opportunity to reduce barriers to quality education and help unlock human potential globally. However, realizing this requires confronting risks like "metacognitive laziness" and ensuring equal access, designing tools that promote critical thinking, not replace it. 📄 Commitment to Collaboration: To realize this vision, we remain committed to a research and evidence-based approach, involving continuous collaboration with educators and experts. The greatest potential of AI is helping everyone reach theirs, with AI as an amplifier of human ingenuity. Read the full report: https://lnkd.in/dHetuCnJ Blog announcement: https://lnkd.in/d4UEEsUX
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DS-STAR is a state-of-the-art data science agent designed to autonomously solve complex data science problems. It automates tasks from analysis to data wrangling across diverse data types to achieve top performance on challenging benchmarks. Learn more: https://goo.gle/3WEJAjx
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