Optical computing has emerged as a powerful approach for high-speed and energy-efficient information processing. Diffractive ...

Abstract: Federated Learning (FL) is a promising distributed machine learning framework that allows clients to collaboratively train a global model without data leakage. The synchronous FL suffers ...

The new reinforcement learning system lets large language models challenge and improve themselves using real-world data instead of curated training sets. Meta researchers have unveiled a new ...

Abstract: Due to the complex physical properties of granular materials, research on robot learning for manipulating such materials predominantly either disregards the consideration of their physical ...

A new framework developed by researchers at Google Cloud and DeepMind aims to address one of the key challenges of developing computer use agents (CUAs): Gathering high-quality training examples at ...

Learn Your Way is now available in Google Labs. Google's education-centered AI models personalize material. AI companies continue to market study tools to younger users. Google is introducing a new ...

Experts estimate that the global production and disposal of plastics emits nearly 2 billion tons of greenhouse gases per year. The vast majority of these materials end up in landfills, but what if we ...

A new learning paradigm developed by University College London (UCL) and Huawei Noah’s Ark Lab enables large language model (LLM) agents to dynamically adapt to their environment without fine-tuning ...

Creative Commons (CC): This is a Creative Commons license. Attribution (BY): Credit must be given to the creator. Graph neural networks for crystal property prediction typically require precise atomic ...

Physical learning environments (PLEs)—including classrooms, schools, and networks of facilities—play a critical role in shaping educational outcomes. The World Bank’s RIGHT+ framework offers guidance ...