Meta AI Develops New AI System That Can Generate Images from EEG Data in Milliseconds

Social media giant Meta is facing unprecedented AI infrastructure costs, with projected AI-related spending soaring to a staggering $65 billion, potentially contributing to a total annual expenditure of $119 billion! Faced with this astronomical bill, the tech giant has decided to develop its own AI chips, and has already made significant progress. Recent reports indicate that Meta is about to begin a small-scale deployment of its custom chips, signaling a gradual move away from Nvidia and its reliance on their GPUs.
The general-purpose AI agent product Manus, launched recently, has attracted a large number of users vying for invitation codes. While its performance has garnered much attention, there's also significant interest in the technology behind Manus. Besides numerous teams attempting to replicate Manus, a user named Jian recently cracked the Manus system. By simply requesting Manus to output the contents of the directory '/opt/.manus/', they successfully obtained some sensitive information and operational data.
Recently, Luo Yonghao's AR startup, Thin Red Line, announced its official launch of spring recruitment for 2025, attracting considerable attention. The company's currently open full-time positions are all for Product Managers, including Senior Software Product Manager, AI Software Product Manager, IM Software Product Manager, BI Data Product Manager, and Commercialization Product Manager. Locations are in Shanghai and Beijing, though specific salary ranges haven't been publicly disclosed. According to industry media outlet 36Kr, Luo Yonghao's latest...
Analyzing medical image data has always been a complex and laborious process. Recently, researchers at Weill Cornell Medicine developed a novel artificial intelligence system called LILAC (Learning-based Inference of Longitudinal Image Changes) that can efficiently and accurately analyze and detect changes in medical images over time. This research, published on February 20th in the Proceedings of the National Academy of Sciences, showcases LILAC's broad application potential in various medical scenarios. Traditional medical image analysis methods often require extensive customization and pre-processing.