Why do the midterms matter? Who is ahead in the polls? Texas Republican group seeks to block certification of state's election Share Texas Republican group seeks to block certification of state's election on social media Days before the United States' pivotal midterm elections, a Texas group affiliated with the Republican Party has asked a federal judge to stop certification of the state's upcoming election results. In a lawsuit filed on Tuesday, the Potter County Republican Party and its chairman, Daniel Rogers, claim potential election interference by artificial intelligence (AI) or "malicious actors" that could compromise the results of next Tuesday's elections in Texas. "If a component is tampered with during manufacturing, it could serve as a point of entry for unlawful and malfeasance hackers, allowing for the possibility of synchronized attacks across multiple election machines and jurisdictions," the lawsuit read.

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Your Next Great Read Might Be Certified'Organic' Amid a sea of AI writing slop, a new stamp for "organic literature" will help readers to pick out books authored by real, corn-fed, free-range humans. As scandals involving the use of artificial intelligence by professional authors pile up, a British startup has established a way for publishers to certify their books as "organic"--written by real people, no machines in sight. On Wednesday, Books by People confirmed it had received approval from the UK's Intellectual Property Office to begin distributing a mark that indicates a published book has been authored by a human. The stamp, which looks like a thumbprint, will be displayed on the cover. "If people are going to be writing AI books or seeking it out for their own enjoyment, that's their prerogative," Esme Dennys, cofounder of Books by People, tells WIRED.

When you purchase through links in our articles, we may earn a small commission. IT certification training usually runs hundreds of dollars per exam track, and they only get more expensive as you add new credentials. If you want a smarter option, the All-in-One CompTIA Certification Prep Bundle gives you 10 prep courses for industry certifications, and lifetime access is on sale now for $24.99 (reg. Beginners can start with the Tech+ fundamentals course and the A+ hardware track, then move on to Network+ and Security+ after learning the basics. Each course uses practice tests, quizzes, and hands-on exercises instead of plain memorization.

An Ebike Company Was Sued Over Alleged Safety Misinformation. Several Chinese businesses associated with the ebike brand Aipas settled a lawsuit with Amazon and the safety certification company UL in July. Amazon and UL, a company that tests and certifies that products meet safety standards, settled a lawsuit in July with several Chinese companies that manufacture ebikes under the Aipas brand name. The US companies accused the Chinese firms in January of claiming in Amazon listings and on Aipas' website that the ebikes had been certified by UL, giving recognizable, brand-name assurance to online shoppers worried about risks like ebike lithium-ion battery fires . On July 15, a federal judge signed off on a permanent injunction in which the companies agreed that they would no longer use the UL mark.

NVIDIA introduces Halos for Robotics, which the company calls the industry's first full-stack safety system for robotics and physical AI operating near people.

Data errors, corruptions, and poisoning attacks during training pose a major threat to the reliability of modern AI systems. While extensive effort has gone into empirical mitigations, the evolving nature of attacks and the complexity of data require a more principled, provable approach to robustly learn on such data--and to understand how perturbations influence the final model. Hence, we introduce MIBPCert, a novel certification method based on mixed-integer bilinear programming (MIBP) that computes sound, deterministic bounds to provide provable robustness even under complex threat models. By computing the set of parameters reachable through perturbed or manipulated data, we can predict all possible outcomes and guarantee robustness. To make solving this optimization problem tractable, we propose a novel relaxation scheme that bounds each training step without sacrificing soundness. We demonstrate the applicability of our approach to continuous and discrete data, as well as different threat models--including complex ones that were previously out of reach.

We consider the problem of certified robustness for sequence classification against edit distance perturbations. Naturally occurring inputs of varying lengths (e.g., sentences in natural language processing tasks) present a challenge to current methods that employ fixed-rate deletion mechanisms and lead to suboptimal performance. To this end, we introduce AdaptDel methods with adaptable deletion rates that dynamically adjust based on input properties. We extend the theoretical framework of randomized smoothing to variable-rate deletion, ensuring sound certification with respect to edit distance. We achieve strong empirical results in natural language tasks, observing up to 30 orders of magnitude improvement to median cardinality of the certified region, over state-of-the-art certifications.

Rendering produces 2D images from 3D scene representations, yet how continuous variations in camera pose and scenes influence these images--and, consequently, downstream visual models--remains underexplored.