26 stories
We're rapidly transitioning to a computing landscape defined by highly general, increasingly autonomous agents. Driven by large language models (LLMs) that can dynamically generate plans and invoke external tools, these systems offer the potential for AI to seamlessly handle complex, multi-step…
The rapid advancement of text-to-image AI models, such as Nano Banana, Stable Diffusion and Flux, has fundamentally transformed creative design, allowing anyone to synthesize photorealistic, high-fidelity images from textual descriptions.
Recent advancements in video diffusion demonstrate remarkable high-fidelity generation with models that can render realistic scenes in seconds. However, while diffusion models generate high-fidelity video clips, transforming them into coherent long storytelling engines remains challenging.
Digital educational tools have transformed how students around the world access information, from online textbooks to video libraries. Yet, for all the remarkable leaps in technology and accessibility, digital learning can often feel like a passive experience.
AI agents have shown great potential in automating real-world tasks, such as conducting a Google Search, reading local computer files, or executing generated Python scripts. To achieve such agentic workflows, LLMs need to learn how to use tools correctly and efficiently.
Polygenic risk scores (PRSs) are used to predict disease risk from genetic variants. They usually incorporate the influence of hundreds to millions of genetic variants.
We partnered with HHMI Janelia and collaborators in Cambridge, U.K., to publish a complete map of the male fruit fly’s brain and central nervous system, creating the largest brain map to date.
Methane is a potent greenhouse gas; over a 100-year timeframe, its warming potential is 30 times greater than that of carbon dioxide. In fact, it has driven approximately 25% of human-induced warming since the start of the industrial era.
We introduce TimesFM-3, a state-of-the-art time series foundation model that enables highly accurate multivariate time series forecasting in a single forward pass, significantly outperforming other forecasting models across major benchmarks.
As part of Google Earth AI, we introduce the planetary prediction engine (PPE), an experimental research capability that autonomously executes the full geospatial modeling workflow — from data discovery to model training — achieving improvements across diverse prediction tasks in public health,…
Consumer wearables use motion and physiological sensors to estimate activity and sleep, but these signals provide only an indirect view of glucose regulation.
As AI assistants evolve from simple text interfaces to multimodal companions, we are seeing a shift toward more proactive, situated assistance. Recent innovations like Project Astra and Gemini 3.1 Flash Live already allow users to discuss their physical surroundings in real time, often utilizing…
We introduce a dynamic, mobility-informed framework that allows AI models to understand the temporal activity rhythms of places over time, and, in doing so, significantly improve predictions about real-world attributes like opening hours, price levels, and busyness.
Insulin resistance is one of the most critical yet underdiagnosed drivers of modern metabolic disease. Predating the clinical onset of type 2 diabetes by years, impaired insulin sensitivity stealthily impairs vascular health, liver function, and energy metabolism long before fasting blood sugar…
When a physician meets a patient, the consultation extends far beyond the words exchanged. The physician observes the patient's gait, registers visible signs of discomfort, notes their breathing, and guides the patient through physical examination maneuvers.
Large language models (LLMs) are increasingly being deployed not just as coding assistants but as autonomous agents capable of conducting end-to-end scientific research workflows.
Imagine a symphony orchestra performing a complex masterpiece. If the violins drifted out of tune every few measures, the ensemble would constantly have to stop and retune their instruments. Thankfully, this doesn't happen in an orchestra because the instruments reliably stay in tune.
Diffusion models are currently one of the most powerful types of tools for generative tasks that require complex and local structures, such as image generation and molecular discovery.
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