When the rapper Fenix Flexin débuted a new accent on his single "Rubberz," he stumbled into a century-old fight over musical fraudulence. Startups including Suno allow users to create songs with A.I., and some A.I. entities have started to attract followings on Spotify. Earlier this summer, a small night club in Passaic, New Jersey, hosted a brief performance by one of the year's most talked-about musicians. A post on Instagram had promised attendees "an unforgettable night," although not necessarily a long one: the only assurance was that the Los Angeles rapper known as Fenix Flexin would be "performing his viral hit'Rubberz' live!" Until recently, Fenix was known for high-spirited and slightly mush-mouthed hip-hop tracks, but "Rubberz," which was released in June, marked a dramatic change: it was sad but sprightly synth pop, like an unheard outtake from the nineteen-eighties; the vocals were crisp, with a hint of vibrato and a faintly British accent. Listeners seemed to delight in the incongruity--a semipopular rapper suddenly sounded nothing like himself.

Look Up Switch Off Mashable's Best: E-readers, robovacs, laptops, earbuds, smart home and more Creator Playbook Mashable Voices Trending Now Say More Mashable Selects Safety Net Versus Gift Ideas For Everyone On Your List In My Bag All Series We are armed to the teeth! Conan O'Brien's podcast interview with John Cena is a deeply wholesome watch John Oliver takes a deep dive into Trump's many pardons On Mashable's, hosts Kristy Puchko (Mashable's Entertainment Editor) and Mark Stetson (Senior Creative Producer) bring humor and their trusted insights to the biggest shows, films, digital trends, and cultural moments. From viral-worthy rants and passionate raves to smart recaps and first-look teasers, they cover what everyone is talking about. Celebrity guests join the conversation for real talk about their careers, upcoming projects, and what's trending online. The only thing more explosive than a Looney Tunes movie, is when the cast plays Choose Your Squad.

Use BBC.com or the new BBC App to listen to BBC podcasts, Radio 4 and the World Service outside the UK. Is China Replacing Workers With Robots? Today, we discuss China's £14.7bn investment into AI and robotics in the hopes of overtaking the US as the world leader in innovation. China has two million robots already working in factories, and is investing heavily in the hopes that one day they could bridge gaps in an already sluggish economy. What can these robots do?

'Digging the grave of my profession': the Hollywood creatives training AI to do their jobs H ollywood creatives are taking gig work to train AI models to replicate their skills in a bid to offset tightening earnings in a trend one compared to being "handed a shovel and asked to dig the grave of my profession". Experienced and award-winning writers, directors and producers are being paid from $12 to $200 an hour to teach AI models the intricacies of their jobs, from writing a screenplay to devising a shooting schedule. With feelings ranging from fatalism to guilt, the creatives have signed up with some of the booming training agencies, which have contracts with the biggest AI companies including Anthropic and OpenAI, to pass on hard-won human skills in industries such as finance, health, law and social work. While they wait for entertainment industry work, they give notes on the AI systems' attempts to do the tasks they would hope to be paid for themselves. It comes amid a slump in jobs in the motion picture and sound recording industries, where AI is starting to take roles from actors, editors and special effects artists.

WIRED goes on location--and on camera--with the cult hit. On March 16, 2026, at 5:45 pm in a leafy suburb of Atlanta called Sandy Springs, police pound on the door of a neglected French Country-style mansion, rifles at the ready, bodycams rolling. Minutes earlier, a distress call came from someone claiming to be hiding from a gunman in the mansion's downstairs bathroom. The dispatcher heard a gunshot ring out in the distance, then the line disconnected. "Open the door!" an officer yells. A calm young man with a mullet and woolly eyebrows steps out, hands raised. The police ask him who else is in the house. "Just my friends," he replies, as seven other young people, men and women, silently file out behind him, less evidently relaxed. They remain outside while two officers search the house. Inside the mansion there are no immediate signs of a massacre, but the decor alone arouses suspicion. All of the windows are frosted over, so only a chilly light leaks in. The place is a mess, and the walls are adorned with lurid, seemingly AI-generated art: a frowning baby holding an assault rifle, a rubber ducky bobbing in a mug of what looks like black coffee, a lidless and levitating eyeball crying into a martini glass. The rooms are painted primary colors, grass green and cherry red, like a kindergarten class. A vape dangles from a doorframe by a chain, suspended at mouth level. The pantry is practically empty. The bedroom is a dormitory featuring seven identical twin beds. No one is hiding in the bathroom. The call, it seems, was a prank. The police return to the driveway and ask, "What is it that you guys are doing here?" "We're just livestreaming," says a man in a camo hat named Matt. "You guys don't have any firearms or anything inside the house?" There are guns in the house, Matt says, for self-defense. Fans of their livestream can be obsessive, he explains, and tend to have perverse ideas about jokes. The officer asks to see their weapons, and they go downstairs. The room is cluttered with ergonomic swivel chairs, desks strewn with takeout containers and energy drinks, two flatscreen TVs, and a dozen computer monitors.

This position paper argues that there is an urgent need to restructure markets for the information that goes into AI systems. Specifically, small-to-medium sized producers of information (such as journalists, news organizations, researchers, and creative professionals) need to be able to appoint representatives who can carry out collective bargaining with AI product builders in order to receive a reasonable terms and a fair return on the informational value they contribute. Obstacles to this market structure can be removed through technical work that facilitates collective bargaining in the information economy (e.g., explainable data value estimation and federated data management tools) and regulatory/policy interventions (e.g., support for trusted data intermediary organizations that represent guilds or syndicates of information producers). We argue that without collective bargaining in the information economy, AI will exacerbate a large-scale information market failure that will lead not only to undesirable concentration of capital, but also to a potential ecological collapse in the informational commons. On the other hand, collective bargaining in the information economy can create market conditions necessary for a pro-social AI future. We provide concrete actions that can be taken to support a coalition-based approach to achieve this.

Use BBC.com or the new BBC App to listen to BBC podcasts, Radio 4 and the World Service outside the UK. The world's largest data centre (62sq miles) has been approved in Utah, but there is growing opposition towards the project. At twice the size of Manhattan with promises to create thousands of jobs, we look at the bi partisan opposition against it. In this episode, Justin and Anthony discuss the enormous buildings being built across rural America, to house the huge amounts of data that A.I companies work with. Tech bosses say the centres are essential to the growth of Artificial Intelligence.

Two-sided marketplaces are standard business models of many online platforms (e.g., Amazon, Facebook, LinkedIn), wherein the platforms have consumers, buyers or content viewers on one side and producers, sellers or content-creators on the other. Consumer side measurement of the impact of a treatment variant can be done via simple online A/B testing. Producer side measurement is more challenging because the producer experience depends on the treatment assignment of the consumers. Existing approaches for producer side measurement are either based on graph cluster-based randomization or on certain treatment propagation assumptions. The former approach results in low-powered experiments as the producer-consumer network density increases and the latter approach lacks a strict notion of error control. In this paper, we propose (i) a quantification of the quality of a producer side experiment design, and (ii) a new experiment design mechanism that generates high-quality experiments based on this quantification.

Two-sided marketplaces are standard business models of many online platforms (e.g., Amazon, Facebook, LinkedIn), wherein the platforms have consumers, buyers or content viewers on one side and producers, sellers or content-creators on the other. Consumer side measurement of the impact of a treatment variant can be done via simple online A/B testing. Producer side measurement is more challenging because the producer experience depends on the treatment assignment of the consumers. Existing approaches for producer side measurement are either based on graph cluster-based randomization or on certain treatment propagation assumptions. The former approach results in low-powered experiments as the producer-consumer network density increases and the latter approach lacks a strict notion of error control. In this paper, we propose (i) a quantification of the quality of a producer side experiment design, and (ii) a new experiment design mechanism that generates high-quality experiments based on this quantification.

Our approach leads to better use of hardware resources.