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Introducing Nested Learning: A new ML paradigm for continual learning that views models as nested optimization problems to enhance long context processing. Our proof-of-concept model, Hope, shows improved performance in language modeling. Learn more: https://t.co/fpdDlYaleL
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Introducing VaultGemma, the largest open model trained from scratch with differential privacy. Read about our new research on scaling laws for differentially private language models, download the weights, & check out the technical report on the blog →https://t.co/tvgseWTcyP
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Today on the blog we feature a new active learning method for curating high-quality data that reduces training data requirements for fine-tuning LLMs by orders of magnitude. Read all about it → https://t.co/PnLGqhAbda
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Introducing Nested Learning: A new ML paradigm for continual learning that views models as nested optimization problems to enhance long context processing. Our proof-of-concept model, Hope, shows improved performance in language modeling. Learn more: https://t.co/fpdDlYaleL
Today we announce a significant step in quantum computing by introducing Quantum Echoes, a new quantum computational algorithm measuring out-of-time-order correlators. This verifiable quantum advantage paves the way for solving real-world …
Scheduling virtual machines (VMs) is like a puzzle game with disappearing pieces. Here we present LAVA, an AI-based scheduling algorithm that continuously re-predicts & adapts to the lifetimes of VMs to optimize resource efficiency in …
Introducing Coral NPU: A full-stack, open-source platform co-designed by Google Research and @GoogleDeepMind. It provides tools for developers to build the next generation of always-on AI on low-power edge devices. Learn how you can start …
Today we describe how we leverage AlphaEvolve, a @GoogleDeepMind system for iteratively evolving code, to morph snippets of code towards better proof elements in complexity theory that can be automatically verified by a computer program. R…
Introducing VaultGemma, the largest open model trained from scratch with differential privacy. Read about our new research on scaling laws for differentially private language models, download the weights, & check out the technical repo…
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