Recommendation systems have become essential in modern music streaming platforms, shaping how users discover and engage with songs. One common approach in recommendation systems is collaborative filtering, which suggests content based on the preferences of users with similar listening patterns to the target user. However, this method is less effective on media where interactions are sparse. Music is one such medium, since the average user of a music streaming service will never listen to the vast majority of tracks. Due to this sparsity, there are several challenges that have to be addressed with other methods. This review examines the current state of research in addressing these challenges, with an emphasis on the role of content filtering in mitigating biases inherent in collaborative filtering approaches. We explore various methods of song classification for content filtering, including lyrical analysis using Large Language Models (LLMs) and audio signal processing techniques. Additionally, we discuss the potential conflicts between these different analysis methods and propose avenues for resolving such discrepancies.

Video-to-Audio (V2A) Generation achieves significant progress and plays a crucial role in film and video post-production. However, current methods overlook the cinematic language, a critical component of artistic expression in filmmaking. As a result, their performance deteriorates in scenarios where Foley targets are only partially visible. To address this challenge, we propose a simple self-distillation approach to extend V2A models to cinematic language scenarios. By simulating the cinematic language variations, the student model learns to align the video features of training pairs with the same audio-visual correspondences, enabling it to effectively capture the associations between sounds and partial visual information. Our method not only achieves impressive improvements under partial visibility across all evaluation metrics, but also enhances performance on the large-scale V2A dataset, VGGSound.

Unfairness is a well-known challenge in Recommender Systems (RSs), often resulting in biased outcomes that disadvantage users or items based on attributes such as gender, race, age, or popularity. Although some approaches have started to improve fairness recommendation in offline or static contexts, the issue of unfairness often exacerbates over time, leading to significant problems like the Matthew effect, filter bubbles, and echo chambers. To address these challenges, we proposed a novel framework, Hypergraph Contrastive Multi-Interest Learning for Fair Conversational Recommender System (HyFairCRS), aiming to promote multi-interest diversity fairness in dynamic and interactive Conversational Recommender Systems (CRSs). HyFairCRS first captures a wide range of user interests by establishing diverse hypergraphs through contrastive learning. These interests are then utilized in conversations to generate informative responses and ensure fair item predictions within the dynamic user-system feedback loop. Experiments on two CRS-based datasets show that HyFairCRS achieves a new state-of-the-art performance while effectively alleviating unfairness. Our code is available at https://github.com/zysensmile/HyFairCRS.

The Velvet Sundown's indie ballads, with guitar music and male vocals, is fairly easy, if bland, on the ear. With lyrics such as "eyes like film in faded light, dreams walk barefoot into the night" and "ash and velvet, smoke and flame, calling out in freedom's name", it could all feasibly be either AI-generated or penned by humans. Deezer, a rival music streaming platform, said that its AI detector tool had flagged the music as being "100% AI generated". Spotify did not respond to a request for comment. CEO Daniel Ek has previously told the BBC that he did not intend to ban AI-generated music from the platform but added that he did not agree with using the tech to mimic real artists.

Rep. Marjorie Taylor Greene, R-Ga., joins'Sunday Morning Futures' to discuss whether the government should regulate artificial intelligence, and how AI ties into President Donald Trump's spending bill. As large tech companies continue to take the lead implementing artificial intelligence (AI) into their platforms and workplaces, the latest Fox News national survey finds that while positive reviews of AI have increased, many remain skeptical about its role in society. The survey, released Thursday, finds 43% view AI technology as a good thing for society, up 5 points from April 2023. Still, nearly half of voters, 47%, think AI is bad for society -- about where it was two years ago (46% bad in April 2023). Overall, urban voters (60%), nonwhite voters (56%), voters under age 45 (53%), and men (52%) are those most likely to say AI is a good thing, while rural voters (55%), White voters (51%), voters ages 45 and over (49%), and women (55%) are likely to say it's a bad thing.

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AI slop is flooding every single digital platform, and music streaming services are no exception--so much so, even someone who generally avoids AI might find themselves unknowingly listening to a robot hornily singing about butts. Take the sordid saga of "Make Love to My Shitter," an AI-generated track from an artist called BannedVinylCollection. Brace Belden, a host of the popular politics podcast TrueAnon, says that Spotify recently queued up the bawdy song after he'd finished listening to alt-country legend Lucinda Williams' 1992 album Sweet Old World. "I didn't realize the song was AI at first," he says. "I thought it might've been some obscene joke record from the 80s or 90s."

We introduce a simple yet effective technique for estimating lighting from a single low-dynamic-range (LDR) image by reframing the task as a chrome ball inpainting problem. This approach leverages a pre-trained diffusion model, Stable Diffusion XL, to overcome the generalization failures of existing methods that rely on limited HDR panorama datasets. While conceptually simple, the task remains challenging because diffusion models often insert incorrect or inconsistent content and cannot readily generate chrome balls in HDR format. Our analysis reveals that the inpainting process is highly sensitive to the initial noise in the diffusion process, occasionally resulting in unrealistic outputs. To address this, we first introduce DiffusionLight, which uses iterative inpainting to compute a median chrome ball from multiple outputs to serve as a stable, low-frequency lighting prior that guides the generation of a high-quality final result. To generate high-dynamic-range (HDR) light probes, an Exposure LoRA is fine-tuned to create LDR images at multiple exposure values, which are then merged. While effective, DiffusionLight is time-intensive, requiring approximately 30 minutes per estimation. To reduce this overhead, we introduce DiffusionLight-Turbo, which reduces the runtime to about 30 seconds with minimal quality loss. This 60x speedup is achieved by training a Turbo LoRA to directly predict the averaged chrome balls from the iterative process. Inference is further streamlined into a single denoising pass using a LoRA swapping technique. Experimental results that show our method produces convincing light estimates across diverse settings and demonstrates superior generalization to in-the-wild scenarios. Our code is available at https://diffusionlight.github.io/turbo

The creativity of classical music arises not only from composers who craft the musical sheets but also from performers who interpret the static notations with expressive nuances. This paper addresses the challenge of generating classical piano performances from scratch, aiming to emulate the dual roles of composer and pianist in the creative process. We introduce the Expressive Compound Word (ECP) representation, which effectively captures both the metrical structure and expressive nuances of classical performances. Building on this, we propose the Expressive Music Variational AutoEncoder (XMVAE), a model featuring two branches: a Vector Quantized Variational AutoEncoder (VQ-VAE) branch that generates score-related content, representing the Composer, and a vanilla VAE branch that produces expressive details, fulfilling the role of Pianist. These branches are jointly trained with similar Seq2Seq architectures, leveraging a multiscale encoder to capture beat-level contextual information and an orthogonal Transformer decoder for efficient compound tokens decoding. Both objective and subjective evaluations demonstrate that XMVAE generates classical performances with superior musical quality compared to state-of-the-art models. Furthermore, pretraining the Composer branch on extra musical score datasets contribute to a significant performance gain.

This paper presents a praxeological analysis of artificial intelligence and algorithmic governance, challenging assumptions about the capacity of machine systems to sustain economic and epistemic order. Drawing on Misesian a priori reasoning and Austrian theories of entrepreneurship, we argue that AI systems are incapable of performing the core functions of economic coordination: interpreting ends, discovering means, and communicating subjective value through prices. Where neoclassical and behavioural models treat decisions as optimisation under constraint, we frame them as purposive actions under uncertainty. We critique dominant ethical AI frameworks such as Fairness, Accountability, and Transparency (FAT) as extensions of constructivist rationalism, which conflict with a liberal order grounded in voluntary action and property rights. Attempts to encode moral reasoning in algorithms reflect a misunderstanding of ethics and economics. However complex, AI systems cannot originate norms, interpret institutions, or bear responsibility. They remain opaque, misaligned, and inert. Using the concept of epistemic scarcity, we explore how information abundance degrades truth discernment, enabling both entrepreneurial insight and soft totalitarianism. Our analysis ends with a civilisational claim: the debate over AI concerns the future of human autonomy, institutional evolution, and reasoned choice. The Austrian tradition, focused on action, subjectivity, and spontaneous order, offers the only coherent alternative to rising computational social control.