Empower your technical staff with hands-on technology training

With a vast amount of technology training and education available today, it’s difficult to know what deserves your attention and what’s just a marketing ploy. Furthermore, most training and education in technology is only offered through text or video, meaning that the learner doesn’t have an opportunity to apply the theory that they are learning. …

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VideoPrism: A foundational visual encoder for video understanding

Posted by Long Zhao, Senior Research Scientist, and Ting Liu, Senior Staff Software Engineer, Google Research An astounding number of videos are available on the Web, covering a variety of content from everyday moments people share to historical moments to scientific observations, each of which contains a unique record of the world. The right tools …

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Running machine learning in the cloud for live service games

Generative AI has become the number one technology of interest across many industries over the past year. Here at Google Cloud for Games, we think that online game use cases have some of the highest potential for generative AI, giving creators the power to build more dynamic games, monetize their games better, and get to …

Method identified to double computer processing speeds

Scientists introduce what they call ‘simultaneous and heterogeneous multithreading’ or SHMT. This system doubles computer processing speeds with existing hardware by simultaneously using graphics processing units (GPUs), hardware accelerators for artificial intelligence (AI) and machine learning (ML), or digital signal processing units to process information.

AI 101: Natural Language Processing (NLP)

Before OpenAI’s GPT-3 burst onto the scene a few years ago with the first commercially available large language model (LLM), the modern consumer’s most likely interaction with the category of AI known as natural language processing (NLP) was customer service agents like “Ask Julie.” Amtrak introduced the virtual travel assistant in 2001 as an early …

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Advances in private training for production on-device language models

Posted by Zheng Xu, Research Scientist, and Yanxiang Zhang, Software Engineer, Google Language models (LMs) trained to predict the next word given input text are the key technology for many applications [1, 2]. In Gboard, LMs are used to improve users’ typing experience by supporting features like next word prediction (NWP), Smart Compose, smart completion …