14 articles
Google DeepMind releases AlphaCode 3, an AI system that performs at the 99th percentile on Codeforces, effectively matching the level of the world's top competitive programmers.
A Stanford-led randomized controlled trial finds that students using AI tutoring systems for 30 minutes daily scored twice as high on standardized math assessments compared to a control group, the strongest evidence yet for AI in education.
Researchers at the University of Oxford have developed CardioSense, an AI system that analyzes routine blood tests and ECG data to predict cardiac events up to a decade before they occur.
DeepMind's upgraded GenCast weather model outperforms every numerical weather system on hurricane path forecasting, with NOAA announcing immediate adoption.
Stanford researchers built an AI model called SeismoNet that predicts aftershock locations with 85% accuracy, far surpassing the 6% accuracy of traditional Coulomb stress models. USGS has announced a pilot integration for the Pacific Northwest.
Using generative AI developed with Anthropic, NASA's Mars rover autonomously planned and executed two drives covering nearly 1,500 feet.
Retrieval-Augmented Generation is the most important AI architecture you've never heard of. Here's what it does, why it matters, and how it works — without the jargon.
Retrieval-Augmented Generation has moved from research demos to production systems. Here's what actually works, what doesn't, and the lessons teams are learning the hard way.
A new study finds that AI industry compensation packages are pulling top researchers out of universities at an accelerating rate, threatening fundamental science.
University of New Hampshire researchers used AI to identify previously unknown magnetic materials, potentially reducing dependence on China-controlled rare earth supply chains.
Companies are embedding hidden manipulation prompts inside 'Summarize with AI' buttons to poison chatbot memory and steer future recommendations.
DeepMind's AlphaFold 3 goes beyond protein folding to model drug-target interactions, cutting early-stage drug screening from months to weeks. Model weights are open for researchers.
A new attention mechanism allows transformer models to process million-token inputs without the quadratic memory scaling that has limited context windows.
Stanford researchers publish a new attention mechanism that slashes transformer memory requirements by 60% while matching full-scale model quality — a potential game-changer for on-device AI.