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Exciting advancements in private training for on-device language models are transforming how we interact with technology! With innovations in Gboard, these models enhance our typing experience through features like next word prediction and smart suggestions, all while prioritizing user privacy and reducing latency. The ability to train models directly on our devices means that our data stays secure, and the performance improves significantly. Personally, I believe this shift towards on-device processing is a game changer for user privacy and efficiency. Have you noticed any changes in your typing experience with features like Smart Compose? I’d love to hear your thoughts! #LanguageModels #OnDeviceAI #PrivacyFirst #TypingExperience #InnovationInTech
Exciting advancements in private training for on-device language models are transforming how we interact with technology! With innovations in Gboard, these models enhance our typing experience through features like next word prediction and smart suggestions, all while prioritizing user privacy and reducing latency. The ability to train models directly on our devices means that our data stays secure, and the performance improves significantly. Personally, I believe this shift towards on-device processing is a game changer for user privacy and efficiency. Have you noticed any changes in your typing experience with features like Smart Compose? I’d love to hear your thoughts! #LanguageModels #OnDeviceAI #PrivacyFirst #TypingExperience #InnovationInTech
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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
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