And then there were two: Of the original 11 co-founders who kickstarted xAI with Elon Musk three years ago, only two remain as the deep learning lab continues a personnel overhaul to compete with Anthropic and OpenAI. That rebuilding, insists Musk, is by design. The most immediate pressure is competitive. This week, xAI co-founders Zihang Dai and Guodong Zhang left the outfit after Musk complained that the company's AI coding tools were not effectively competing with Claude Code or Codex, rival programming assistants made by Anthropic and OpenAI, respectively. Musk said the company held an all-hands meeting on Wednesday that focused on how to catch up, which he predicted would be possible by the middle of this year. Coding tools matter so much because they're where the money is. While an early-year surge of users was powered by xAI's lax regulation of Grok's ability to produce sexual and even abusive imagery, coding tools are seen as the key revenue-generating tech for AI labs. That makes xAI's current lag in this area more than a perception issue; it's a business problem....
Characterized by weakened or damaged heart musculature, heart failure results in the gradual buildup of fluid in a patient's lungs, legs, feet, and other parts of the body. The condition is chronic and incurable, often leading to arrhythmias or sudden cardiac arrest. For many centuries, bloodletting and leeches were the treatment of choice, famously practiced by barber surgeons in Europe, during a time when physicians rarely operated on patients. In the 21st century, the management of heart failure has become decidedly less medieval: Today, patients undergo a combination of healthy lifestyle changes, prescription of medications, and sometimes use pacemakers. Yet heart failure remains one of the leading causes of morbidity and mortality, placing a substantial burden on health-care systems across the globe. 'About half of the people diagnosed with heart failure will die within five years of diagnosis,' says Teya Bergamaschi, an MIT PhD student in the lab of Nina T. and Robert H. Rubin Professor Collin Stultz and the co-first author of a new paper introducing a deep learning model for predicting heart failure. 'Understanding how a patient will fare after hospitalization is really important in allocating finite resources.'...
Flash floods are among the deadliest weather events in the world, killing more than 5,000 people each year. They're also among the most difficult to predict. But Google thinks it has cracked that problem in an unlikely way ' by reading the news. While humans have assembled a lot of weather data, flash floods are too short-lived and localized to be measured comprehensively, the way the temperature or even river flows are monitored over time. That data gap means that deep learning models, which are increasingly capable of forecasting the weather, aren't able to predict flash floods. To solve that problem, Google researchers used Gemini ' Google's large language model ' to sort through 5 million news articles from around the world, isolating reports of 2.6 million different floods, and turning those reports into a geo-tagged time series dubbed 'Groundsource.' It's the first time that the company has used language models for this kind of work, according to Gila Loike, a Google Research product manager. The research and dataset was shared publicly Thursday morning....
Let me tell you something that took me an embarrassingly long time to truly internalize: the reason deep learning works as well as it does in 2026 is only maybe 40% algorithms. The rest is hardware. We got lucky ' spectacularly lucky ' that the GPU, a chip originally designed to make triangles pretty in Quake III, turned out to be almost exactly the right computational substrate for training neural networks. But 'almost exactly right' is doing a lot of heavy lifting in that sentence. The story of AI chips is the story of closing that gap, and it's one of the most fascinating engineering stories of our time....
