Earth system models represent our best tool to predict and prepare for future changes to Earth’s environment. However, the immense computational cost of running these models at high resolution limits their ability to make regional projections at fine scales. Indeed, a typical limiting scale for these models is comparable in size to the island of Hawai’i (~100 km). Obtaining more granular projections, for instance at the city level (~10 km), is critical for planning everything from farming...| research.google
Many real-world planning tasks involve both harder “quantitative” constraints (e.g., budgets or scheduling requirements) and softer “qualitative” objectives (e.g., user preferences expressed in natural language). Consider someone planning a week-long vacation. Typically, this planning would be subject to various clearly quantifiable constraints, such as budget, travel logistics, and visiting attractions only when they are open, in addition to a number of constraints based on personal ...| research.google
The digital age offers ever growing access to vast amounts of knowledge, yet much remains locked behind complex language and specialist jargon. While complexity is often necessary in expert discourse, it can become a barrier when users need to understand information critical to their lives, such as navigating health information, understanding legal language, or grasping financial details. Tools that let users produce a simplified version of complex text that they encounter online can empower ...| research.google
Large language models (LLMs) that have been optimized through human feedback have rapidly emerged as a leading paradigm for developing intelligent conversational agents. However, despite their strong performance across many benchmarks, LLM-based agents can still lack multi-turn conversational skills such as disambiguation — when they are faced with ambiguity, they often overhedge or implicitly guess users' true intents rather than asking clarifying questions. Yet high-quality conversation s...| research.google
Each year at Google I/O, we share some of Google’s most advanced technologies. We show how they can be helpful and provide new experiences, and how developers and other communities can use them to innovate. Many of these new technologies emerged from years of work within Google Research, many in collaboration with other teams, building on multiple successive breakthroughs in AI and other areas of computer science. This year’s I/O highlights the impact of bringing research to reality. As S...| research.google
The machine learning community has consistently found that while modern machine learning (ML) models are powerful, they often need to be fine-tuned on domain-specific data to maximize performance. This can be problematic or even impossible, as informative data is often privacy-sensitive. Differential privacy (DP) allows us to train ML models while rigorously guaranteeing that the learned model respects the privacy of its training data, by injecting noise into the training process.| research.google
Differential privacy (DP) is a mathematically rigorous and widely studied privacy framework that ensures the output of a randomized algorithm remains statistically indistinguishable even if the data of a single user changes. This framework has been extensively studied in both theory and practice, with many applications in analytics and machine learning (e.g., 1, 2, 3, 4, 5, 6, 7).| research.google
Retrieval augmented generation (RAG) enhances large language models (LLMs) by providing them with relevant external context. For example, when using a RAG system for a question-answer (QA) task, the LLM receives a context that may be a combination of information from multiple sources, such as public webpages, private document corpora, or knowledge graphs. Ideally, the LLM either produces the correct answer or responds with “I don’t know” if certain key information is lacking.| research.google
Every day, billions of people shop online, hoping to replicate the best parts of in-store shopping. Seeing something that catches your eye, picking it up and inspecting it for yourself can be a key part of how we connect with products. But capturing the intuitive, hands-on nature of the store experience is nuanced and can be challenging to replicate on a screen. We know that technology can help bridge the gap, bringing key details to your fingertips with a quick scroll. But these online tools...| research.google
Generative AI models are capable of transforming aspects of life from education to innovation globally, but their reach is not matched by the breadth of their training data, which is limited in terms of languages, topics, and geographies.| research.google
Language model–based AI systems such as Articulate Medical Intelligence Explorer (AMIE, our research diagnostic conversational AI agent recently published in Nature) have shown considerable promise for conducting text-based medical diagnostic conversations but the critical aspect of how they can integrate multimodal data during these dialogues remains unexplored. Instant messaging platforms are a popular tool for communication that allow static multimodal information (e.g., images and docum...| research.google
In the 1660s, with the help of a simple, homemade light microscope that magnified samples more than 250 times, a Dutch fabric merchant named Antoine van Leeuwenhoek became the first person to document a close-up view of bacteria, red blood cells, sperm cells, and many other scientific sights. Since then, light microscopy has solidified its place as a bedrock technique in our quest to understand living organisms. Today, it is nearly ubiquitous in life science laboratories, enabling biologists ...| research.google