US prosecutors just charged a man with smuggling NVIDIA chips to China, and government officials are saying the company isn't doing all it could to stop the illegal activities, Bloomberg reported. Earlier this year, officials released a surveillance video showing someone using a red hair dryer to peel serial number stickers off NVIDIA server boxes in an effort to sneak AI chips to China. And yesterday, federal authorities arrested a man named Greg Lui for smuggling servers containing 300 million worth of AI chips to China, in violation of US export laws. Those efforts reportedly show how an expanded network of criminals is attempting to reroute NVIDIA's AI chips to China, profiting from Washington's export controls that make the contraband silicon valuable. NVIDIA says it has abided by the rules and the company has never been accused of abetting smuggling.

Warren, Michigan residents say Muslim immigrants and a nearby Islamic center have created dangerous traffic and parking chaos, claiming police and Mayor Lori Stone refuse to help.

Follow this section to personalize your feed and get instant alerts. Follow Go to your personalized feed WHY FOLLOW? Smart Alerts: Get notified about major news as it happens. Follow this tag to personalize your feed and get instant alerts. Follow Go to your personalized feed WHY FOLLOW?

Sen. Elizabeth Warren and Biden FTC Chair Lina Khan are facing backlash after regulatory opposition helped derail Amazon’s bid for iRobot, resulting in the Roomba maker falling into Chinese ownership.

What are the implications of Trump's Somali'garbage' comments? What happens if the US attacks Venezuela? Does'America First' make the US weaker? What we know about the DC pipe bomb suspect Brian Cole Jr. US President Donald Trump has cleared the way for tech giant Nvidia to sell its advanced H200 chip to China, in a significant easing of Washington's export controls targeting Chinese tech. Trump said on Monday that he had informed Chinese President Xi Jinping of the decision to allow the export of the chip under an arrangement that will see 25 percent of sales paid to the US government.

Scientific discoveries are often made by finding a pattern or object that was not predicted by the known rules of science. Oftentimes, these anomalous events or objects that do not conform to the norms are an indication that the rules of science governing the data are incomplete, and something new needs to be present to explain these unexpected outliers. The challenge of finding anomalies can be confounding since it requires codifying a complete knowledge of the known scientific behaviors and then projecting these known behaviors on the data to look for deviations. When utilizing machine learning, this presents a particular challenge since we require that the model not only understands scientific data perfectly but also recognizes when the data is inconsistent and out of the scope of its trained behavior. In this paper, we present three datasets aimed at developing machine learning-based anomaly detection for disparate scientific domains covering astrophysics, genomics, and polar science. We present the different datasets along with a scheme to make machine learning challenges around the three datasets findable, accessible, interoperable, and reusable (FAIR). Furthermore, we present an approach that generalizes to future machine learning challenges, enabling the possibility of large, more compute-intensive challenges that can ultimately lead to scientific discovery.

Russian Internet Trolls use fake personas to spread disinformation through multiple social media streams. Given the increased frequency of this threat across social media platforms, understanding those operations is paramount in combating their influence. Using Twitter content identified as part of the Russian influence network, we created a predictive model to map the network operations. We classify accounts type based on their authenticity function for a sub-sample of accounts by introducing logical categories and training a predictive model to identify similar behavior patterns across the network. Our model attains 88% prediction accuracy for the test set. Validation is done by comparing the similarities with the 3 million Russian troll tweets dataset. The result indicates a 90.7% similarity between the two datasets. Furthermore, we compare our model predictions on a Russian tweets dataset, and the results state that there is 90.5% correspondence between the predictions and the actual categories. The prediction and validation results suggest that our predictive model can assist with mapping the actors in such networks.

Sen. Elizabeth Warren said AI should be regulated to protect privacy and safety following a closed door hearing with tech leaders. Following a closed Senate AI forum with tech giants, union leaders and artificial intelligence experts, Sen. Elizabeth Warren, D-Mass., told reporters Wednesday AI should be regulated to protect privacy. She also criticized the decision to keep media and the public from viewing the hearing. "I do not understand why the press has been barred from this meeting," Warren said. "What most of the people have said is we want innovation, but we have got to protect safety."

Machine learning has been used to quickly discover some of the most promising materials for fluoride-ion batteries. The work could accelerate development of these batteries, which are tipped by some to rival, or even replace, lithium-based ones. In theory, fluoride-ion systems are ideal for batteries in everything from electric vehicles to consumer electronics. That's because fluoride ions are lightweight, small and highly stable. Fluoride is also cheaper than lithium and cobalt that are required for lithium-ion batteries.

In an increasingly digitized world, the artificial intelligence (AI) boom is only getting started. But could the risks of artificial intelligence outweigh the potential benefits these technologies might lend to society in the years ahead? In this segment of Backstage Pass, recorded on Dec. 14, Fool contributors Asit Sharma, Rachel Warren, and Demitri Kalogeropoulos discuss. Asit Sharma: We had two questions that we were going to debate. Well, I'll have to choose one.