Brit Award-winning singer Jorja Smith's record label has said it wants a share of the royalties for a song it claims was created using an artificial intelligence clone of the singer's voice. I Run by British dance act Haven went viral on TiKTok in October thanks, in part, to smooth soul vocals by an uncredited female singer. Although I Run has now been re-released with new vocals, Smith's label FAMM said it believes the track was made with AI trained on her work, and is seeking compensation. It's bigger than one artist or one song, FAMM wrote in a statement on Instagram . The label said it believes both versions of the track infringe on Jorja's rights and unfairly take advantage of the work of all the songwriters with whom she collaborates.

Prince William says he's'not in a calm state' as he arrives at the BAFTAs amid Andrew arrest drama: Prince of Wales says he's not in right frame of mind to watch weepy contender Hamnet - as Kate reveals it left her in floods of tears Who is Austin Tucker Martin? It's sensational, but William and Kate are the real King and Queen now. Read what my royal insiders are saying... it's the only way: MAUREEN CALLAHAN Tulsi Gabbard's personal life with mysterious videographer husband revealed in new intimate pictures I've met the man of my dreams... if he discovers my dirty little secret, he'll be disgusted: DEAR JANE JFK Jr took drugs'every single day': Everyone knows about Carolyn Bessette's cocaine snorting and cheating. But friends hid his binges, experimental sex and Jackie Kennedy's gay fears... until now Tide turns for little abandoned monkey Punch who had no one to love but his stuffed toy... as he's finally accepted into family Moment tourist minibus sinks in the world's deepest lake killing seven after crashing through the frozen ice Tucker Carlson forced to apologize to Israel's president for implying he went to Epstein's pedo island My American friends are all whispering the same rancid royal rumor. It's not just Andrew... this could bring everyone down: KENNEDY The Alexander brothers' alleged'rape playbook': Almost too monstrous to read, an exhaustive account of hideous secrets dating back to high school Vulgar squatter lazed around $2.3m mansion all day and sent child to work in BAKERY to help pay the bills... but now karma has caught up with her in the most delicious way The show must go on!

Author Simon Stålenhag has a new work out this month. December is traditionally a quieter month for new releases from publishers and that's definitely true this year, with a sparser than usual science-fiction offering to chew over. That said, there are some intriguing titles out this month, and I'm looking forward to the new book from artist and author Simon Stålenhag, another illustrated dystopia, as well as a mysterious-sounding Russian novel, and the conclusion of Bethany Jacobs's excellent space opera trilogy. Jacobs has written a piece for the New Scientist Book Club about how the late Iain M. Banks inspired her own world-building. The Book Club is currently reading Banks's classic Culture novel - do join us .

Drone video shows devastation from floods in Indonesia's Sumatra NewsFeed Drone video shows devastation from floods in Indonesia's Sumatra Drone video shows widespread destruction in part of Sumatra in Indonesia, where more than 440 people have died in flooding and landslides across the country. Hundreds of others are still missing. Pope Leo says two-state is'only solution' for Israel-Palestine Netanyahu requests Israel's president grant a pardon in corruption cases

An aerial view of an Amazon Web Services Data Center known as U.S. East 1 in Ashburn, Virginia, on Oct. 20 | REUTERS STOCKHOLM/LONDON - The global boom in data centers as companies increasingly outsource information storage and ramp up use of energy-intensive artificial intelligence is creating a key challenge for the industry -- how to keep cool. An outage at the world's biggest exchange operator CME Group from late Thursday that halted trade on its popular currency platform and in futures spanning foreign exchange, commodities, Treasuries and stocks has put a spotlight on data centers overheating. The problem was a cooling issue at data centers operated by Dallas-headquartered CyrusOne, which operates more than 55 centers in the U.S., Europe and Japan. In a time of both misinformation and too much information, quality journalism is more crucial than ever. By subscribing, you can help us get the story right.

Here's where things stand on Monday, December 1. The number of casualties from a Russian attack on Ukraine's Kyiv on Sunday rose to one person killed and 18 wounded, according to regional Governor Mykola Kalashnyk. In southern Kherson, at least two people were killed, and seven others were wounded in more Russian attacks, Governor Oleksandr Prokudin said on Telegram. In the Donetsk region, at least two people were killed, and five were injured in Russian attacks on Saturday, according to Governor Vadym Filashkin. In Russia, a Ukrainian drone attack killed two men in the Belgorod region, the region's operational headquarters said in a post on Telegram.

AI agents are increasingly deployed in ecosystems where they repeatedly interact not only with each other but also with humans. In this work, we study these human-AI ecosystems from a theoretical perspective, focusing on the classical framework of repeated pricing games. In our stylized model, the AI agents play equilibrium strategies, and one or more humans manually perform the pricing task instead of adopting an AI agent, thereby defecting to a no-regret strategy. Motivated by how populations of AI agents can sustain supracompetitive prices, we investigate whether high prices persist under such defections. Our main finding is that even a single human defection can destabilize collusion and drive down prices, and multiple defections push prices even closer to competitive levels. We further show how the nature of collusion changes under defection-aware AI agents. Taken together, our results characterize when algorithmic collusion is fragile--and when it persists--in mixed ecosystems of AI agents and humans.

Uncertainty quantification is vital for decision-making and risk assessment in machine learning. Mean-variance regression models, which predict both a mean and residual noise for each data point, provide a simple approach to uncertainty quantification. However, overparameterized mean-variance models struggle with signal-to-noise ambiguity, deciding whether prediction targets should be attributed to signal (mean) or noise (variance). At one extreme, models fit all training targets perfectly with zero residual noise, while at the other, they provide constant, uninformative predictions and explain the targets as noise. We observe a sharp phase transition between these extremes, driven by model regularization. Empirical studies with varying regularization levels illustrate this transition, revealing substantial variability across repeated runs. To explain this behavior, we develop a statistical field theory framework, which captures the observed phase transition in alignment with experimental results. This analysis reduces the regularization hyperparameter search space from two dimensions to one, significantly lowering computational costs. Experiments on UCI datasets and the large-scale ClimSim dataset demonstrate robust calibration performance, effectively quantifying predictive uncertainty.

Despite recent progress in multilingual speech processing, African languages remain under-represented in both research and deployed systems, particularly when it comes to strong, open-weight encoders that transfer well under low-resource supervision. Self-supervised learning has proven especially promising in such settings, yet most publicly released models targeting African speech remain at BASE scale, leaving unanswered whether larger encoders, trained exclusively on Africa-centric audio, offer tangible benefits and how model capacity interacts with data composition. This work addresses that gap by introducing SSA-HuBERT-Large (317M parameters) and SSA-HuBERT-XL (964M parameters), the first large models trained solely on African speech, alongside a BASE size counterpart. We release these models as open weights: see https://huggingface.co/collections/Orange/african-speech-foundation-models. By conducting a carefully controlled experimental study focused exclusively on Sub-Saharan languages, covering automatic speech recognition (ASR) and language identification (LID) tasks, we demonstrate that larger architectures significantly improve performance by effectively leveraging large audio datasets.

Abstract--Knee Osteoarthritis (KOA) is a musculoskeletal condition that can cause significant limitations and impairments in daily activities, especially among older individuals. T o evaluate the severity of KOA, typically, X-ray images of the affected knee are analyzed, and a grade is assigned based on the Kellgren-Lawrence (KL) grading system, which classifies KOA severity into five levels, ranging from 0 to 4. This approach requires a high level of expertise and time and is susceptible to subjective interpretation, thereby introducing potential diagnostic inaccuracies. T o address this problem a stacked ensemble model of fine-tuned Convolutional Neural Networks (CNNs) was developed for two classification tasks: a binary classifier for detecting the presence of KOA, and a multiclass classifier for precise grading across the KL spectrum. The proposed stacked ensemble model consists of a diverse set of pre-trained architectures, including MobileNetV2, Y ou Only Look Once (YOLOv8), and DenseNet201 as base learners and Categorical Boosting (CatBoost) as the meta-learner . This proposed model had a balanced test accuracy of 73% in multiclass classification and 87.5% in binary classification, which is higher than previous works in extant literature. Knee Osteoarthritis (KOA) [1] is a degenerative musculoskeletal joint disease in which the knee cartilage breaks down over time.