Deep Learning
The Download: how AI is used for military targeting, and the Pentagon's war on Claude
The Download: how AI is used for military targeting, and the Pentagon's war on Claude Plus: an ex-DOGE staffer has been accused of stealing social security data. The US military might use generative AI systems to rank targets and recommend which to strike first, according to a Defense Department official. A list of possible targets could first be fed into a generative AI system that the Pentagon is fielding for classified settings. Humans might then ask the system to analyze the information and prioritize the targets. They would then be responsible for checking and evaluating the results and recommendations. OpenAI's ChatGPT and xAI's Grok could soon be at the center of exactly these sorts of high-stakes military decisions.
China's OpenClaw Boom Is a Gold Rush for AI Companies
China's OpenClaw Boom Is a Gold Rush for AI Companies Hype around the open source agent is driving people to rent cloud servers and buy AI subscriptions just to try it, creating a windfall for tech companies. George Zhang thought OpenClaw could make him rich, even though he didn't really understand how the viral AI agent software worked. But he saw a video of a Chinese social media influencer demonstrating how it could be deployed to manage stock portfolios and make investment decisions autonomously. Zhang, who works in cross-border ecommerce in the Chinese city of Xiamen, was intrigued enough that he decided to try installing OpenClaw in late February. Zhang is one of the many people in China who got swept up in the craze over OpenClaw recently.
Inside the Dirty, Dystopian World of AI Data Centers
This story appears in the April 2026 print edition. While some stories from this issue are not yet available to read online, you can explore more from the magazine . Get our editors' guide to what matters in the world, delivered to your inbox every weekday. The race to power AI is already remaking the physical world. Three Mile Island's cooling towers have until recently served as grave markers for America's nuclear-power industry. A s we drove through southwest Memphis, KeShaun Pearson told me to keep my window down--our destination was best tasted, not viewed. Along the way, we passed an abandoned coal plant to our right, then an active power plant to our left, equipped with enormous natural-gas turbines. Pearson, who directs the nonprofit Memphis Community Against Pollution, was bringing me to his hometown's latest industrial megaproject.
MacNet: Transferring Knowledge from Machine Comprehension to Sequence-to-Sequence Models
Boyuan Pan, Yazheng Yang, Hao Li, Zhou Zhao, Yueting Zhuang, Deng Cai, Xiaofei He
Machine comprehension (MC) has gained significant popularity over the past few years and it is a coveted goal in the field of natural language understanding. Its task is to teach the machine to understand thecontent ofagivenpassage andthenanswer arelated question, which requires deep comprehension and accurate information extraction towards the text.