After more than three decades in entertainment, Robbie Williams is back on the road and ready to celebrate. His new album, Britpop, is his 16th number one, breaking the previous record set by the Beatles. The singer, whose Long 90s tour begins this week, is taking a moment to mark his achievement. I think as British people we're very good at piercing the balloon of our own success and undercutting it and devaluing ourselves, he tells BBC News. It's what we do best.

A concert is raging underneath the sea ice. But will we drown it out? Breakthroughs, discoveries, and DIY tips sent every weekday. Earth's oceans have always been a wild world of sound . A symphony of chatter between creatures, rain hitting the surface, the boom of calving ice, the thunders of waves and fizz of bubbles, the rumble of undersea earthquakes, and even mysterious quacking sounds .

After President Trump announced a 25 percent tariff on nearly all Canadian imported goods (and Canada announced its own 25 percent tariff on American imported goods), Doug Ford, the premier of Ontario -- and a former supporter of President Trump -- announced the Canadian territory would be "ripping up" a 100 million contract with Elon Musk's Starlink. The contract was signed in November last year. Musk, boss of Starlink and the richest man in the world, is a close confidant of Trump and has control over the so-called Department of Government Efficiency, or DOGE (urgh), tasked with cost-cutting and deregulation in government. Ford believed this was enough to link Musk (and his businesses) to Trump's tariffs. He said Ontario "won't do business with people hellbent on destroying our economy" and that Musk wants to "take food off the table" of hard-working Canadians.

With the help of modern machine learning technology, The Beatles were able to release their song " Now and Then" in late 2023. The song contains vocals recorded from around 50 years ago and a guitar track from 1995, but technological limitations at the time prevented it from seeing the light of day without serious audio issues. Today, after being nominated in November 2024 for two Grammys, "Now and Then" won one for Best Rock Performance. When the demo was first recorded, John Lennon's singing and piano were on the same audio track, and separating them was impossible. Fortunately, AI can now do that without much bleed or loss.

Their final song was mixed with John Lennon's voice. The Beatles' return to the Grammys has come with an assist from artificial intelligence. "Now and Then" is nominated for record of the year and best rock performance at the 2025 Grammy Awards, making it the first nominated song ever to use AI in its production. The song utilized AI to clean up old demo recordings of John Lennon singing and playing piano, recorded in the late 1970s, as well as a guitar track from George Harrison, recorded six years before his death in 2001. "To me, this is a cool example of how AI can function in our current environment," Recording Academy CEO Harvey Mason Jr. said in a statement to Fox News Digital.

While reading through the list of Grammy nominees earlier I came across quite a surprise. There, competing for record of the year alongside the likes of Beyoncé's Texas Hold'Em and Chappell Roan's Good Luck Babe, was Now and Then by The Beatles. So, here's the story of how The Beatles got nominated for two Grammys -- they also snagged a best rock performance nod -- 50 years after formally breaking up. It starts with a demo John Lennon recorded in the 1970s that was given to Paul McCartney, Ringo Starr and George Harrison for inclusion on the The Beatles Anthology, released in 1995. While other tracks like Free as a Bird and Real Love made it on, technology wasn't advanced enough to separate Lennon's vocals and piano without reducing the recording's quality. But, last year McCartney and Starr used modern machine learning technology to pull Lennon's vocals for a new track.

TikTok user and travel agent Taylor Moore shared details of her plane ride next to Taylor Swift's dad, including his proud papa moments and approval of Travis Kelce. The Beatles, Taylor Swift and Beyoncé are facing off at the 2025 Grammy Awards. On Friday, the Recording Academy released its full list of Grammy nominations, and the Beatles earned their first nod since 1997 for their latest song, "Now and Then." The Fab Four also earned a nomination for the same song in the best rock performance category. The Beatles' last new song, the AI-assisted "Now and Then," was released in 2023.

Prototype noise-cancelling headphones allow you to select which background noises to drown out, letting you put an "audio spotlight" on one specific voice so you can concentrate on it. Conventional noise-cancelling headphones reduce unwanted sounds like the rumble of a bus engine, but because the technology cancels out certain frequencies entirely, it can also suppress sounds we want to hear. Now, Shyam Gollakota at the University of Washington in Seattle and his colleagues have created headphones that can remove any unwanted noises while leaving others intact, regardless of their frequencies. It can also be trained with the press of a button to home in on a specific person's voice and exclude all other noise. The researchers are presenting their prototype at a joint meeting of the Acoustical Society of America and the Canadian Acoustical Association this week.

Modular self-reconfigurable robots (MSRRs) offer enhanced task flexibility by constructing various structures suitable for each task. However, conventional terrestrial MSRRs equipped with wheels face critical challenges, including limitations in the size of constructible structures and system robustness due to elevated wrench loads applied to each module. In this work, we introduce an Aerial MSRR (A-MSRR) system named BEATLE, capable of merging and separating in-flight. BEATLE can merge without applying wrench loads to adjacent modules, thereby expanding the scalability and robustness of conventional terrestrial MSRRs. In this article, we propose a system configuration for BEATLE, including mechanical design, a control framework for multi-connected flight, and a motion planner for reconfiguration motion. The design of a docking mechanism and housing structure aims to balance the durability of the constructed structure with ease of separation. Furthermore, the proposed flight control framework achieves stable multi-connected flight based on contact wrench control. Moreover, the proposed motion planner based on a finite state machine (FSM) achieves precise and robust reconfiguration motion. We also introduce the actual implementation of the prototype and validate the robustness and scalability of the proposed system design through experiments and simulation studies.

Large Language Models (LLMs) demonstrate an impressive capacity to recall a vast range of common factual knowledge information. However, unravelling the underlying reasoning of LLMs and explaining their internal mechanisms of exploiting this factual knowledge remain active areas of investigation. Our work analyzes the factual knowledge encoded in the latent representation of LLMs when prompted to assess the truthfulness of factual claims. We propose an end-to-end framework that jointly decodes the factual knowledge embedded in the latent space of LLMs from a vector space to a set of ground predicates and represents its evolution across the layers using a temporal knowledge graph. Our framework relies on the technique of activation patching which intervenes in the inference computation of a model by dynamically altering its latent representations. Consequently, we neither rely on external models nor training processes. We showcase our framework with local and global interpretability analyses using two claim verification datasets: FEVER and CLIMATE-FEVER. The local interpretability analysis exposes different latent errors from representation to multi-hop reasoning errors. On the other hand, the global analysis uncovered patterns in the underlying evolution of the model's factual knowledge (e.g., store-and-seek factual information). By enabling graph-based analyses of the latent representations, this work represents a step towards the mechanistic interpretability of LLMs.