26 stories
Estimates suggest that billions of wearable devices are now in use, precisely tracking heart rate, movement, skin temperature, blood-oxygen levels, and sleep across days, weeks, and months.
We’ve seen a massive shift in how people handle time-series forecasting since we launched TimesFM. Now, we’re bringing that same "zero-shot" logic to tabular data.
Having powerful Large Language Models (LLMs) right in your pocket is now a reality with on-device models like Gemini Nano and Gemma. This technology enables everyday features on your phone — such as instantly summarizing a flurry of notifications or proofreading an important text message — all…
It is well-established that allowing large language models (LLMs) to generate step-by-step reasoning traces, commonly known as chain-of-thought (CoT), enhances performance on complex tasks.
We developed a high-resolution deep learning framework to reveal fine-scale ecological features, like hedgerows and copses, that are typically invisible to standard satellite detection.
People use tables every day to organize and interpret complex information in a structured, easily accessible format. Due to the ubiquity of such tables, reasoning over tabular data has long been a central topic in natural language processing (NLP).
Large language models (LLMs) have significantly improved the state of the art for solving tasks specified using natural language, often reaching performance close to that of people.
Posted by Long Zhao, Senior Research Scientist, and Ting Liu, Senior Staff Software Engineer, Google Research An astounding number of videos are av...
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