The GPUs were placed in servers that were supposed to be shipped from Taiwan to companies in Southeast Asia. The US Attorney's Office for the Southern District of New York has charged three people with illegally exporting NVIDIA GPUs to China in violation of the Export Control Reform Act. NVIDIA's chips have become a critical component in the rush to train and run increasingly complex artificial intelligence models, one the US has sought to manipulate with export controls and profit-sharing schemes with NVIDIA. The three people, Yih-Shyan Wally Liaw, Ruei-Tsang Steven Chang and Ting-Wei Willy Sun, two employees and one contractor working for US IT company Super Micro Computer, allegedly circumvented export control laws via a multi-step scheme that involved creating fake orders for servers with NVIDIA chips from Southeast Asian companies, that were then secretly sent to China. The plan involved paying a logistics company to repackage the servers in Taiwan, staging dummy servers to be inspected by Super Micro Computer's compliance team and falsifying records so Liaw, Chang and Sun's employer was unaware where the servers were actually being sent.

Federal prosecutors charged three men linked to Supermicro with allegedly smuggling $2.5 billion in U.S. AI technology to China using fake documents, dummy servers, and shell companies.

A trio linked with a US technology supplier have been charged over a ploy to smuggle American artificial intelligence (AI) chips to China, the Department of Justice said on Thursday. The individuals allegedly conspired to sell billions of dollars' worth of technology to buyers in China by faking documents and using dummy equipment to slip past audits, according to the DOJ. The goods in question included Nvidia-made semiconductors, highly coveted AI chips which are subject to export controls. In August 2025, two Chinese nationals were also arrested and charged with illegally shipping millions of dollars' worth of Nvidia chips to China. The DOJ said in a statement on Thursday that it had arrested US-citizen Yih-Shyan Wally Liaw and Taiwanese citizen Ting-Wei Willy Sun, while Ruei-Tsang Steven Chang, a Taiwanese citizen, remains a fugitive.

We initiate a systematic investigation of distribution learning (density estimation) when the data is distributed across multiple servers. The servers must communicate with a referee and the goal is to estimate the underlying distribution with as few bits of communication as possible. We focus on non-parametric density estimation of discrete distributions with respect to the l1 and l2 norms. We provide the first non-trivial upper and lower bounds on the communication complexity of this basic estimation task in various settings of interest. Specifically, our results include the following: 1. When the unknown discrete distribution is unstructured and each server has only one sample, we show that any blackboard protocol (i.e., any protocol in which servers interact arbitrarily using public messages) that learns the distribution must essentially communicate the entire sample.

The app reads your email inbox and your meeting calendar, then gives you a short audio summary. It can help you spend less time scrolling, but of course, there are privacy drawbacks to consider.

We consider nonconvex stochastic optimization problems in the asynchronous centralized distributed setup where the communication times from workers to a server can not be ignored, and the computation and communication times are potentially different for all workers.