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We present LAVA, a new scheduling algorithm that continuously re-predicts and adapts to the actual lifetimes of virtual machines to optimize resource efficiency in large cloud data centers. Imagine a puzzle game similar to Tetris with pieces rapidly falling onto a stack. Some fit perfectly. Others don’t. The goal is to pack the blocks as […]
Solving virtual machine puzzles: How AI is optimizing cloud computing Read More »
We introduce Nested Learning, a new approach to machine learning that views models as a set of smaller, nested optimization problems, each with its own internal workflow, in order to mitigate or even completely avoid the issue of “catastrophic forgetting”, where learning new tasks sacrifices proficiency on old tasks. The last decade has seen incredible
Introducing Nested Learning: A new ML paradigm for continual learning Read More »
We announce the release of JAX-Privacy 1.0, a library for differentially private machine learning on the high-performance computing library, JAX. From personalized recommendations to scientific advances, AI models are helping to improve lives and transform industries. But the impact and accuracy of these AI models is often determined by the quality of data they use.
Differentially private machine learning at scale with JAX-Privacy Read More »
New theoretical work from Google Quantum AI shows that large scale quantum computers could solve certain optimization problems that are intractable for conventional classical computers. From designing more efficient airline routes to organizing clinical trials, optimization problems are everywhere. Yet for many real-world challenges, even our most powerful supercomputers struggle to find the best solution.
Google Researchers developed a unique model to predict the probability with which an EV charging port will be available at a certain station within a certain amount of minutes from the current time, which helps EV drivers plan their trips efficiently while minimizing waiting time at the charging stations. more on Google Research Blog
Reducing EV range anxiety: How a simple AI model predicts port availability Read More »
Algorithms & Theory Google Researchers invoke AlphaEvolve, an LLM-based coding agent, to find and verify combinatorial structures that improve results on the hardness of approximately solving certain optimization problems. Recently, large language models (LLMs) have demonstrated surprising capabilities in competitive mathematics and competitive programming, demonstrating world-leading performance across both of these fields. However, their successes in mathematical discovery
