Prediction markets like Kalshi and Polymarket are booming, and so is a fight among regulators, lawmakers, and advocates over their legality. Former New Jersey governor Chris Christie, who currently serves as an advisor to the American Gaming Association, has criticized prediction markets. The political fight in the US over the future of prediction markets like Polymarket and Kalshi has escalated into a full-blown war, and battle lines aren't being neatly drawn along party lines. Instead, conservative Mormons have aligned themselves with Las Vegas bigwigs and MAGA royalty is siding with liberal Democrat lobbyists. One side argues that the platforms are breaking the law by operating as shadow casinos.

House Democrats are demanding answers from the National Institute of Standards and Technology and urging it to halt rumored changes they say could undermine its mission. One of the US government's top scientific research labs is taking steps that could drive away foreign scientists, a shift lawmakers and sources tell WIRED could cost the country valuable expertise and damage the agency's credibility. The National Institute of Standards and Technology (NIST) helps determine the frameworks underpinning everything from cybersecurity to semiconductor manufacturing. Some of NIST's recent work includes establishing guidelines for securing AI systems and identifying health concerns with air purifiers and firefighting gloves. Many of the agency's thousands of employees, postdoctoral scientists, contractors, and guest researchers are brought in from around the world for their specialized expertise.

On TikTok, Chinese manufacturers are advertising signal-blocking weapons with the breezy cadence of consumer lifestyle advertising. "Pew, pew, pew!" a woman wearing sneakers and high-waisted pink trousers says cheerfully in a video uploaded to TikTok. She is standing on what appears to be an industrial rooftop while demonstrating how to use a black device resembling an oversized laser tag gun. "Jamming gun, good," she adds, flashing a thumbs up. These days, nearly any product imaginable is available for purchase on TikTok straight from Chinese factories, ranging from industrial chemicals to mystical crystals and custom pilates reformers.

Scout AI is using technology borrowed from the AI industry to power lethal weapons--and recently demonstrated its explosive potential. Like many Silicon Valley companies today, Scout AI is training large AI models and agents to automate chores. The big difference is that instead of writing code, answering emails, or buying stuff online, Scout AI's agents are designed to seek and destroy things in the physical world with exploding drones. In a recent demonstration, held at an undisclosed military base in central California, Scout AI's technology was put in charge of a self-driving off-road vehicle and a pair of lethal drones. The agents used these systems to find a truck hiding in the area, and then blew it to bits using an explosive charge.

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Security experts have urged people to be cautious with the viral agentic AI tool, known for being highly capable but also wildly unpredictable. Last month, Jason Grad issued a late-night warning to the 20 employees at his tech startup. "You've likely seen Clawdbot trending on X/LinkedIn. While cool, it is currently unvetted and high-risk for our environment, he wrote in a Slack message with a red siren emoji. "Please keep Clawdbot off all company hardware and away from work-linked accounts." Grad isn't the only tech executive who has raised concerns to staff about the experimental agentic AI tool, which was briefly known as MoltBot and is now named OpenClaw. A Meta executive says he recently told his team to keep OpenClaw off their regular work laptops or risk losing their jobs. The executive told reporters he believes the software is unpredictable and could lead to a privacy breach if used in otherwise secure environments. He spoke on the condition of anonymity to speak frankly.

In Table 1, we report the cross-task testbed results in two transfer learning settings, i.e., (a) node classification to link prediction (Table 1a) and (b) link prediction to node classification (Table 1b).