The Stream Why does misinformation spread faster than truth? We examine how online misinformation spreads and the efforts to stop it. From bots pushing ready-made talking points, to AI generating information that isn't always reliable, to pages distorting images and bending facts - online misinformation is everywhere. So how can people tell what's real? And is it even possible to build immunity against misinformation in today's digital world?

Automatic music recommendation has become an increasingly relevant problem in recent years, since a lot of music is now sold and consumed digitally. Most recommender systems rely on collaborative filtering. However, this approach suffers from the cold start problem: it fails when no usage data is available, so it is not effective for recommending new and unpopular songs. In this paper, we propose to use a latent factor model for recommendation, and predict the latent factors from music audio when they cannot be obtained from usage data. We compare a traditional approach using a bag-of-words representation of the audio signals with deep convolutional neural networks, and evaluate the predictions quantitatively and qualitatively on the Million Song Dataset. We show that using predicted latent factors produces sensible recommendations, despite the fact that there is a large semantic gap between the characteristics of a song that affect user preference and the corresponding audio signal. We also show that recent advances in deep learning translate very well to the music recommendation setting, with deep convolutional neural networks significantly outperforming the traditional approach.

We Asked Audio Pros to Blind Test Headphones. When it comes to choosing a good pair of headphones, what happens when you take features, design, and brand awareness out of the equation, and leave it all up to the sound? All products featured on WIRED are independently selected by our editors. However, we may receive compensation from retailers and/or from purchases of products through these links. What makes a really great pair of headphones? The basic answer used to be sound quality, but modern headphones offer so much more than just audio chops.

The truth about Keith Urban's guitarist'other woman' Maggie Baugh revealed amid Nicole Kidman divorce Top plastic surgeons reveal secrets behind Taylor Swift's'changing' face: 'It is looking very full' Trump dollar coin design released by Treasury... and it's inspired by an iconic political photo Shroud of Turin mystery deepens as surgeon spots hidden detail that points to Jesus' resurrection Hollywood A-listers pay me $50,000 to cure their drug addicted nepo-babies because they can't afford for these secrets to go public I'm no longer sleeping with my husband - and never will again, says MOLLY RYDDELL. I love him, but counted down the moments until he climaxed. Then I couldn't bear it any more and the truth spilled out... so many women feel the same Fans erupt at Taylor Swift's'dig' at Travis Kelce's ex Kayla Nicole in wild The Life of a Showgirl track Taylor, your album should be'Life of a Callgirl'. KENNEDY's appalled take on Swift's new record... and its ultra-vivid sex shout outs for Travis the Sasquatch I was so happy after trying a trendy new cosmetic procedure. But 10 years later I suffered a devastating side effect... the doctor had lied Lori Loughlin's husband Mossimo Giannulli seen with mystery brunette in tiny skirt day after shock split The'middle-class kinks' saving marriages: Wives reveal the eight buzzy sex trends that revived their lagging libidos - including the fantasy husbands are secretly obsessed with I'm a woman with autism... here are the signs you might be masking, even from yourself Cake-faced 90s sitcom star looks unrecognizable as she ditches the heavy eyeshadow for an LA errand run can you guess who?

Taylor, your album should be'Life of a Callgirl'. KENNEDY's appalled take on Swift's new record... and its ultra-vivid sex shout outs for Travis the Sasquatch The truth about Keith Urban's guitarist'other woman' Maggie Baugh revealed amid Nicole Kidman divorce How I look like this at 62. I've lost 5 stone fast, 20 years off my biological age and wear size 8... without weight-loss jabs. My war with Harry & Meghan, by PIERS MORGAN: What really happened, their absurd accusations, the brutal truth about post-royal life... and how I believe their royal racism lies helped kill off woke Shroud of Turin mystery deepens as surgeon spots hidden detail that points to Jesus' resurrection I'm no longer sleeping with my husband - and never will again, says MOLLY RYDDELL. I love him, but counted down the moments until he climaxed. Then I couldn't bear it any more and the truth spilled out... so many women feel the same Fans erupt at Taylor Swift's'dig' at Travis Kelce's ex Kayla Nicole in wild The Life of a Showgirl track Trump dollar coin design released by Treasury... and it's inspired by an iconic political photo The THREE singers Keith Urban's been cosying up to revealed - now Nicole Kidman's on the warpath and has done the thing every estranged husband fears most: ALISON BOSHOFF Lori Loughlin's husband Mossimo Giannulli seen with mystery brunette in tiny skirt day after shock split Trump appears alongside Melania at dinner hosted by JD Vance and Usha after'disappearance' rumors Top plastic surgeons reveal secrets behind Taylor Swift's'changing' face: 'It is looking very full' I'm a woman with autism... here are the signs you might be masking, even from yourself Cake-faced 90s sitcom star looks unrecognizable as she ditches the heavy eyeshadow for an LA errand run can you guess who?

Tilly Norwood: how scared should we be of the viral AI'actor'? Emily Blunt and Sag-Aftra join film industry condemnation of'AI actor' Tilly Norwood It takes a lot to be the most controversial figure in Hollywood, especially when Mel Gibson still exists. And yet somehow, in a career yet to even begin, Tilly Norwood has been inundated with scorn. This is for the simple fact that Tilly Norwood does not exist. Despite looking like an uncanny fusion of Gal Gadot, Ana de Armas and High School Musical-era Vanessa Hudgens, Norwood is the creation of an artificial intelligence (AI) talent studio called Xicoia. And if Xicoia is to be believed, then Norwood represents the dazzling future of the film industry.

Detecting AI-generated text is an increasing necessity to combat misuse of LLMs in education, business compliance, journalism, and social media, where synthetic fluency can mask misinformation or deception. While prior detectors often rely on token-level likelihoods or opaque black-box classifiers, these approaches struggle against high-quality generations and offer little interpretability. In this work, we propose DivEye, a novel detection framework that captures how unpredictability fluctuates across a text using surprisal-based features. Motivated by the observation that human-authored text exhibits richer variability in lexical and structural unpredictability than LLM outputs, DivEye captures this signal through a set of interpretable statistical features. Our method outperforms existing zero-shot detectors by up to 33.2% and achieves competitive performance with fine-tuned baselines across multiple benchmarks. DivEye is robust to paraphrasing and adversarial attacks, generalizes well across domains and models, and improves the performance of existing detectors by up to 18.7% when used as an auxiliary signal. Beyond detection, DivEye provides interpretable insights into why a text is flagged, pointing to rhythmic unpredictability as a powerful and underexplored signal for LLM detection.

Large language models (LLMs) are increasingly used in modern search and answer systems to synthesize multiple, sometimes conflicting, texts into a single response, yet current pipelines offer weak incentives for sources to be accurate and are vulnerable to adversarial content. We introduce Truthful Text Summarization (TTS), an incentive-aligned framework that improves factual robustness without ground-truth labels. TTS (i) decomposes a draft synthesis into atomic claims, (ii) elicits each source's stance on every claim, (iii) scores sources with an adapted multi-task peer-prediction mechanism that rewards informative agreement, and (iv) filters unreliable sources before re-summarizing. We establish formal guarantees that align a source's incentives with informative honesty, making truthful reporting the utility-maximizing strategy. Experiments show that TTS improves factual accuracy and robustness while preserving fluency, aligning exposure with informative corroboration and disincentivizing manipulation.

We present Paired by the Teacher (PbT), a two-stage teacher-student pipeline that synthesizes accurate input-output pairs without human labels or parallel data. In many low-resource natural language generation (NLG) scenarios, practitioners may have only raw outputs, like highlights, recaps, or questions, or only raw inputs, such as articles, dialogues, or paragraphs, but seldom both. This mismatch forces small models to learn from very few examples or rely on costly, broad-scope synthetic examples produced by large LLMs. PbT addresses this by asking a teacher LLM to compress each unpaired example into a concise intermediate representation (IR), and training a student to reconstruct inputs from IRs. This enables outputs to be paired with student-generated inputs, yielding high-quality synthetic data. We evaluate PbT on five benchmarks-document summarization (XSum, CNNDM), dialogue summarization (SAMSum, DialogSum), and question generation (SQuAD)-as well as an unpaired setting on SwitchBoard (paired with DialogSum summaries). An 8B student trained only on PbT data outperforms models trained on 70 B teacher-generated corpora and other unsupervised baselines, coming within 1.2 ROUGE-L of human-annotated pairs and closing 82% of the oracle gap at one-third the annotation cost of direct synthesis. Human evaluation on SwitchBoard further confirms that only PbT produces concise, faithful summaries aligned with the target style, highlighting its advantage of generating in-domain sources that avoid the mismatch, limiting direct synthesis.

The performance of unified multimodal models for image generation and editing is fundamentally constrained by the quality and comprehensiveness of their training data. While existing datasets have covered basic tasks like style transfer and simple object manipulation, they often lack the systematic structure and challenging scenarios required for real-world applications. To address this bottleneck, we introduce OpenGPT-4o-Image, a large-scale dataset constructed using a novel methodology that combines hierarchical task taxonomy with automated data generation. Our taxonomy not only includes fundamental capabilities such as text rendering and style control but also introduces highly practical yet challenging categories like scientific imagery for chemistry illustrations and complex instruction editing requiring simultaneous execution of multiple operations. Through an automated pipeline leveraging structured resource pools and GPT-4o, we generate 80k high-quality instruction-image pairs with controlled diversity, covering 11 major domains and 51 subtasks. Extensive experiments show that fine-tuning leading models on our dataset achieves significant performance gains across multiple benchmarks, with improvements of up to 18\% on editing tasks (UniWorld-V1 on ImgEdit-Bench) and 13% on generation tasks (Harmon on GenEval). Our work demonstrates that systematic data construction is key to advancing multimodal AI capabilities.