Spotify expands managed accounts to the U.S., UK, Canada, Australia, Germany, France and Netherlands, giving parents control over children's music content.

Why do horses have eyes on the side of their head? 'You often have to teach horses something on both sides of their body for them to process the information fully.' In the animal kingdom, horses are prey. Breakthroughs, discoveries, and DIY tips sent every weekday. Have you ever noticed that horses have eyes on the sides of the head rather than the front, like we do as humans? The location of horses' eyes offer a biological advantage that helps keep them safe as prey animals.

European diplomats reveal the'tough guy' US negotiator leading the charge on Greenland: 'He hates us' A former Marine was unmasked as the'Zodiac killer' after a bombshell new investigation. I suffered a horrific side effect of a drug used by millions of Americans... and my face'melted off' The ICE backlash isn't the end of Kristi Noem It may have just saved her career FedEx driver accused of abducting and killing little girl while delivering her Christmas present says he shouldn't be executed because he has autism Senator accused of steamy affair with her bodyguard in bombshell lawsuit from his WIFE: 'Bring MDMA so I can guide you' Hunter Biden's stripper baby mama asks for him to be ARRESTED over claims he is still failing to pay her child support Family of Tyler Robinson's transgender lover speaks out for first time since Charlie Kirk assassination and reveals where he is now Dodgers agree with Kyle Tucker'on $240m deal' as champs beat out Mets, Blue Jays for top free agent World's sexiest hockey star and OnlyFans model Mikayla Demaiter spills out of little dress in latest post Nicole Richie addresses her daughter's new identity after unveiling transformation on her 18th birthday Trump gushes over'young beautiful' hockey players and teases rebranding of famed presidential wall Trump's AG secretary sparks mockery with tone-deaf $3 dinner advice as food costs soar Karoline Leavitt reveals the thinking behind Trump's call to cancel elections Microsoft's paperclip mascot delights users as it returns - 18 years after it was axed from Office It was the original virtual assistant, released years before Siri, Alexa, and Bixby. Now, almost two decades after it was axed, Microsoft's Clippy is officially back. The friendly anthropomorphic paper clip has been spotted as an Easter egg in Microsoft's latest announcement about a new AI companion called Mico. Mico - whose name is a nod to Microsoft Copilot - is a small blob with a friendly smiley face, and doesn't look much like its much-loved predecessor.

Sign up to receive it in your inbox. Aside from a temporary love, or a new friend, you could easily stumble upon fabulous stage shows that were presented with such seriousness, often, that you wondered if--while watching the amazing Duelling Bankheads, for instance, or so many people who got up so brilliantly as Stevie Nicks on the Night of 1000 Stevies--you were high on the entertainment, or on dancing with your chosen community, or just amazed by what New York had to offer by way of creativity. Looking back, I can see that, for me at least, it was the combination of all three elements together that gave such hope about Manhattan's ability to foster noncommercial glamour, and to support young performers who were trying things out and seeing what stuck. Richard Move as Martha Graham. The shows I loved the most were at Jackie 60, spearheaded by the irreplaceable Chi Chi Valenti and Johnny Dynell, the resident d.j.

Not long into his new documentary, Oobah Butler tells the cofounder of his newly minted company, Drops, that they should create a piece of luxury luggage that "looks like a bomb" and will sell for $200,000. Immediately, I'm thinking his quest to get £1 million in 90 days might have come to an early end. Butler is a British prankster documentarian who is known for his stunts, like managing to get Amazon to sell its drivers' urine as energy drinks or creating a fake restaurant called the Shed and gaming TripAdvisor to make it the top-rated London restaurant on the platform. His latest documentary, made for the UK's Channel 4, is called How I Made £1 Million in 90 Days Set in London and New York, it takes on the worlds of startups, venture capital, crypto, and what ultimately comes across as a lot of bullshitting, in the name of striking it rich quick. Butler opens the film by saying, as someone who didn't grow up with money and isn't particularly motivated by it, he's fascinated by the fact that people "idolize" wealthy entrepreneurs.

Large Language Models (LLMs) have inherent limitations of faithfulness and factuality, commonly referred to as hallucinations. Several benchmarks have been developed that provide a test bed for factuality evaluation within the context of English-centric datasets, while relying on supplementary informative context like web links or text passages but ignoring the available structured factual resources. To this end, Knowledge Graphs (KGs) have been identified as a useful aid for hallucination mitigation, as they provide a structured way to represent the facts about entities and their relations with minimal linguistic overhead. We bridge the lack of KG paths and multilinguality for factual language modeling within the existing hallucination evaluation benchmarks and propose a KG-based multilingual, multihop benchmark called MultiHal framed for generative text evaluation. As part of our data collection pipeline, we mined 140k KG-paths from open-domain KGs, from which we pruned noisy KG-paths, curating a high-quality subset of 25.9k. Our baseline evaluation shows an absolute scale improvement by approximately 0.12 to 0.36 points for the semantic similarity score, 0.16 to 0.36 for NLI entailment and 0.29 to 0.42 for hallucination detection in KG-RAG over vanilla QA across multiple languages and multiple models, demonstrating the potential of KG integration. We anticipate MultiHal will foster future research towards several graph-based hallucination mitigation and fact-checking tasks.

Large language models (LLMs) are now ubiquitous in user-facing applications, yet they still generate undesirable toxic outputs, including profanity, vulgarity, and derogatory remarks. Although numerous detoxification methods exist, most apply broad, surface-level fixes and can therefore easily be circumvented by jailbreak attacks. In this paper we leverage sparse autoencoders (SAEs) to identify toxicity-related directions in the residual stream of models and perform targeted activation steering using the corresponding decoder vectors. We introduce three tiers of steering aggressiveness and evaluate them on GPT-2 Small and Gemma-2-2B, revealing trade-offs between toxicity reduction and language fluency. At stronger steering strengths, these causal interventions surpass competitive baselines in reducing toxicity by up to 20%, though fluency can degrade noticeably on GPT-2 Small depending on the aggressiveness. Crucially, standard NLP benchmark scores upon steering remain stable, indicating that the model's knowledge and general abilities are preserved. We further show that feature-splitting in wider SAEs hampers safety interventions, underscoring the importance of disentangled feature learning. Our findings highlight both the promise and the current limitations of SAE-based causal interventions for LLM detoxification, further suggesting practical guidelines for safer language-model deployment.

Recent advances in large language models (LLMs) and audio language models have significantly improved music generation, particularly in lyrics-to-song generation. However, existing approaches still struggle with the complex composition of songs and the scarcity of high-quality data, leading to limitations in audio quality, musicality, instruction following, and vocal-instrument harmony. To address these challenges, we introduce LeVo, a language model based framework consisting of LeLM and Music Codec. LeLM is capable of parallel modeling of two types of tokens: mixed tokens, which represent the combined audio of vocals and accompaniment to achieve better vocal-instrument harmony, and dual-track tokens, which separately encode vocals and accompaniment for high-quality song generation. It employs two decoder-only transformers and a modular extension training strategy to prevent interference between different token types. To further enhance musicality and instruction following ability, we introduce a multi-preference alignment method based on Direct Preference Optimization (DPO). This method handles diverse human preferences through a semi-automatic data construction process and post-training. Experimental results demonstrate that LeVo significantly outperforms existing open-source methods in both objective and subjective metrics, while performing competitively with industry systems. Ablation studies further justify the effectiveness of our designs. Audio examples and source code are available at https://levo-demo.github.io and https://github.com/tencent-ailab/songgeneration.

Motion blur caused by camera shake, particularly under large or rotational movements, remains a major challenge in image restoration. We propose a deep learning framework that jointly estimates the latent sharp image and the underlying camera motion trajectory from a single blurry image. Our method leverages the Projective Motion Blur Model (PMBM), implemented efficiently using a differentiable blur creation module compatible with modern networks. A neural network predicts a full 3D rotation trajectory, which guides a model-based restoration network trained end-to-end. This modular architecture provides interpretability by revealing the camera motion that produced the blur. Moreover, this trajectory enables the reconstruction of the sequence of sharp images that generated the observed blurry image. To further refine results, we optimize the trajectory post-inference via a reblur loss, improving consistency between the blurry input and the restored output. Extensive experiments show that our method achieves state-of-the-art performance on both synthetic and real datasets, particularly in cases with severe or spatially variant blur, where end-to-end deblurring networks struggle. Code and trained models are available at https://github.com/GuillermoCarbajal/Blur2Seq/

Recent studies on hallucination in large language models (LLMs) have been actively progressing in natural language processing. However, the impact of negated text on hallucination with LLMs remains largely unexplored. In this paper, we set three important yet unanswered research questions and aim to address them. To derive the answers, we investigate whether LLMs can recognize contextual shifts caused by negation and still reliably distinguish hallucinations comparable to affirmative cases. We also design the NegHalu dataset by reconstructing existing hallucination detection datasets with negated expressions. Our experiments demonstrate that LLMs struggle to detect hallucinations in negated text effectively, often producing logically inconsistent or unfaithful judgments. Moreover, we trace the internal state of LLMs as they process negated inputs at the token level and reveal the challenges of mitigating their unintended effects.