A court filing in a trademark lawsuit reveals OpenAI won't use the name "io" for its AI hardware device, which isn't expected to ship until 2027. OpenAI CEO Sam Altman speaks to members of the press in Sun Valley, Idaho. OpenAI will not use the name " io " for its forthcoming line of AI hardware devices, according to a Monday court filing. The motion is part of a trademark infringement lawsuit filed last year by audio device startup iyO, which sued OpenAI after it acquired famed Apple designer Jony Ive's startup io. Peter Welinder, OpenAI's vice president and general manager, said in the filing that OpenAI had reviewed its product-naming strategy and "decided not to use the name'io' (or'IYO,' or any capitalization of either) in connection with the naming, advertising, marketing, or sale of any artificial intelligence-enabled hardware products."

Charles Ojiambo Mutoka, 72, with portraits of his son Oscar, who he learned was killed in August, during a press conference where relatives of conscripts demanded urgent government action to repatriate their kin, in Nairobi on Jan. 27 | AFP-JIJI Nairobi - The scars on Victor's forearm remind him constantly of the day a Ukrainian drone attacked him after he was forcibly conscripted, like hundreds of young Kenyans, into the Russian military. It was a war that had nothing to do with him and which he was exceptionally lucky to survive. Four Kenyans -- Victor, Mark, Erik and Moses -- recounted the web of deception that took them to the killing fields of Ukraine. Their names have been changed for fear of reprisals. In a time of both misinformation and too much information, quality journalism is more crucial than ever.

Information (IGN) that provides a unique and rich resource for large-scale geospa-tial analysis.

Shwe Kokko city, a casino, entertainment, and tourism complex,from Thailand's side of the border after Bangkok said it would suspend electricity supply to some border areas with Myanmar to try to curb scam centers, in the Mae Sot district, Thailand, on Feb. 5, 2025 | REUTERS Criminals in Southeast Asia are harnessing inexpensive artificial intelligence tools to target bigger pools of potential victims at high speed, keeping scam centers humming even as governments try and crack down, senior officials at Interpol say. Previously, some scams were easy to spot -- from poor quality online ads luring people to work in such centers to the scams themselves, typically designed to make people part with their money through the promise of romance or investment returns. Now, scammers are using large language models and other AI tools to make their cons more sophisticated. Artificial intelligence also allows them to change course quickly, shifting to newer targets and from fresh locations. In a time of both misinformation and too much information, quality journalism is more crucial than ever.

Shoe print evidence recovered from crime scenes plays a key role in forensic investigations. By examining shoe prints, investigators can determine details of the footwear worn by suspects. However, establishing that a suspect's shoes match the make and model of a crime scene print may not be sufficient. Typically, thousands of shoes of the same size, make, and model are manufactured, any of which could be responsible for the print. Accordingly, a popular approach used by investigators is to examine the print for signs of ``accidentals,'' i.e., cuts, scrapes, and other features that accumulate on shoe soles after purchase due to wear. While some patterns of accidentals are common on certain types of shoes, others are highly distinctive, potentially distinguishing the suspect's shoe from all others. Quantifying the rarity of a pattern is thus essential to accurately measuring the strength of forensic evidence. In this study, we address this task by developing a hierarchical Bayesian model. Our improvement over existing methods primarily stems from two advancements. First, we frame our approach in terms of a latent Gaussian model, thus enabling inference to be efficiently scaled to large collections of annotated shoe prints via integrated nested Laplace approximations. Second, we incorporate spatially varying coefficients to model the relationship between shoes' tread patterns and accidental locations. We demonstrate these improvements through superior performance on held-out data, which enhances accuracy and reliability in forensic shoe print analysis.