Electronic music artist and tech creator Look Mum No Computer has been chosen to represent the UK at this year's Eurovision Song Contest in Vienna, the BBC has announced. Look Mum No Computer is a solo artist, songwriter and YouTuber, who is also described as an inventor of unique musical machines. The singer first arrived on the music scene back in 2014 as Sam Battle, frontman of indie rock band Zibra. The group performed at Glastonbury in 2015 for BBC Introducing. Since then, he has been performing and recording under his solo name.

However, existing music datasets suffer from limited size, accessibility, and lack of audio resources.

'It was like a phone call suddenly connecting' Ai Weiwei. 'It was like a phone call suddenly connecting' Ai Weiwei. 'Was I scared going back to China? He has been jailed, tracked and threatened by China's government. What was it like pay a visit home?

AI is asbestos in the walls of our tech society, stuffed there by monopolists run amok. What I do not do is predict the future. No one can predict the future, which is a good thing, since if the future were predictable, that would mean we couldn't change it. Now, not everyone understands the distinction. They think science-fiction writers are oracles. Even some of my colleagues labor under the delusion that we can "see the future". Then there are science-fiction fans who believe that they are the future. A depressing number of those people appear to have become AI bros. These guys can't shut up about the day that their spicy autocomplete machine will wake up and turn us all into paperclips has led many confused journalists and conference organizers to try to get me to comment on the future of AI. That's something I used to strenuously resist doing, because I wasted two years of my life explaining patiently and repeatedly why I thought crypto was stupid, and getting relentlessly bollocked by cryptocurrency cultists who at first insisted that I just didn't understand crypto.

The mysterious singer with millions of streams - but who (or what) is she? Sienna Rose is having a good month. Three of her dusky, jazz-infused soul songs are in Spotify's Viral Top 50. The most popular, a dreamy ballad called Into The Blue, has been played more than five million times. If she continues on this trajectory, Rose could become one of the year's hottest new stars.

We investigate algorithmic collective action in transformer-based recommender systems. Our use case is a collective of fans aiming to promote the visibility of an underrepresented artist by strategically placing one of their songs in the existing playlists they control. We introduce two easily implementable strategies to select the position at which to insert the song and boost recommendations at test time. The strategies exploit statistical properties of the learner to leverage discontinuities in the recommendations, and the long-tail nature of song distributions. We evaluate the efficacy of our strategies using a publicly available recommender system model released by a major music streaming platform. Our findings reveal that even small collectives (controlling less than 0.01\% of the training data) can achieve up to $40\times$ more test time recommendations than songs with similar training set occurrences, on average. Focusing on the externalities of the strategy, we find that the recommendations of other songs are largely preserved, and the newly gained recommendations are distributed across various artists. Together, our findings demonstrate how carefully designed collective action strategies can be effective while not necessarily being adversarial.

Text-to-image diffusion models benefit artists with high-quality image generation. Yet their stochastic nature hinders artists from creating consistent images of the same subject. Existing methods try to tackle this challenge and generate consistent content in various ways. However, they either depend on external restricted data or require expensive tuning of the diffusion model. For this issue, we propose a novel one-shot tuning paradigm, termed OneActor.

Denoising diffusion models (DDMs) have shown promising results in 3D point cloud synthesis. To advance 3D DDMs and make them useful for digital artists, we require (i) high generation quality, (ii) flexibility for manipulation and applications such as conditional synthesis and shape interpolation, and (iii) the ability to output smooth surfaces or meshes. To this end, we introduce the hierarchical Latent Point Diffusion Model (LION) for 3D shape generation. LION is set up as a variational autoencoder (VAE) with a hierarchical latent space that combines a global shape latent representation with a point-structured latent space. For generation, we train two hierarchical DDMs in these latent spaces.

Latent image representations arising from vision-language models have proved immensely useful for a variety of downstream tasks. However, their utility is limited by their entanglement with respect to different visual attributes. For instance, recent work has shown that CLIP image representations are often biased toward specific visual properties (such as objects or actions) in an unpredictable manner. In this paper, we propose to separate representations of the different visual modalities in CLIP's joint vision-language space by leveraging the association between parts of speech and specific visual modes of variation (e.g.

Speaking with popular AI content creators convinces me that "slop" isn't just the internet rotting in real time, but the early draft of a new kind of pop culture. Lately, everywhere I scroll, I keep seeing the same fish-eyed CCTV view: a grainy wide shot from the corner of a living room, a driveway at night, an empty grocery store. JD Vance shows up at the doorstep in a crazy outfit. A car folds into itself like paper and drives away. A cat comes in and starts hanging out with capybaras and bears, as if in some weird modern fairy tale. This fake-surveillance look has become one of the signature flavors of what people now call AI slop. For those of us who spend time online watching short videos, slop feels inescapable: a flood of repetitive, often nonsensical AI-generated clips that washes across TikTok, Instagram, and beyond. For that, you can thank new tools like OpenAI's Sora (which exploded in popularity after launching in app form in September), Google's Veo series, and AI models built by Runway. Now anyone can make videos, with just a few taps on a screen.