Hackers made death threats against this security researcher. In April 2024, a mysterious someone using the online handles "Waifu" and "Judische" began posting death threats on Telegram and Discord channels aimed at a cybersecurity researcher named Allison Nixon. These anonymous personas targeted Nixon because she had become a formidable threat: As chief research officer at the cyber investigations firm Unit 221B, named after Sherlock Holmes's apartment, she had built a career tracking cybercriminals and helping get them arrested. Though she'd done this work for more than a decade, Nixon couldn't understand why the person behind the accounts was suddenly threatening her. And although she had taken an interest in the Waifu persona in years past for crimes he boasted about committing, he hadn't been on her radar for a while when the threats began, because she was tracking other targets. Now Nixon resolved to unmask Waifu/Judische and others responsible for the death threats--and take them down for crimes they admitted to committing.

'Music is not a problem to solve' Mikey Shulman, co-founder and CEO of Suno. 'Music is not a problem to solve' Mikey Shulman, co-founder and CEO of Suno. Is this man the future of music - or its executioner? AI evangelist Mikey Shulman says he's making pop, not slop Worth a staggering $2.45bn, Suno is an AI music company that can create a track with just a few prompts. Why is its CEO happy to see it called'the Ozempic of the music industry'?

AI struggles to write a good jingle. You'd be forgiven if you can't hum the 18th-century Cumbrian folk song "Do Ye Ken John Peel." But in 1942, a version of that tune, reworked with lyrics about Pepsi-Cola, was the most recognized melody in America. Three years earlier, two men walked into the office of Pepsi-Cola's president, carrying a phonograph. They played a demo of what would become one of America's earliest advertising jingles.

AI-generated music is flooding streaming platforms. AI-generated music is flooding streaming platforms. 'Music needs a human component to be of any value': Guardian readers on the growing use of AI in music AI promises to have far-reaching effects in music-making. While some welcome it as a compositional tool, many have deep concerns. A I-generated music is flooding streaming platforms, and it seems to be here to stay.

Musicians are deeply concerned about AI. So why are the major labels embracing it? Companies such as Udio, Suno and Klay will let you use AI to make new music based on existing artists' work. T his was the year that AI-generated music went from jokey curiosity to mainstream force. Velvet Sundown, a wholly AI act, generated millions of streams; AI-created tracks topped Spotify's viral chart and one of the US Billboard country charts; AI "artist" Xania Monet "signed" a record deal. BBC Introducing is usually a platform for flesh-and-blood artists trying to make it big, but an AI-generated song by Papi Lamour was recently played on the West Midlands show.

Music improvisation is fascinating to study, being essentially a live demonstration of a creative process. In jazz, musicians often improvise across predefined chord progressions (leadsheets). How do we assess the creativity of jazz improvisations? And can we capture this in automated metrics for creativity for current LLM-based generative systems? Demonstration of emotional involvement is closely linked with creativity in improvisation. Analysing musical audio, can we detect emotional involvement? This study hypothesises that if an improvisation contains more evidence of emotion-laden content, it is more likely to be recognised as creative. An embeddings-based method is proposed for capturing the emotional content in musical improvisations, using a psychologically-grounded classification of musical characteristics associated with emotions. Resulting 'emovectors' are analysed to test the above hypothesis, comparing across multiple improvisations. Capturing emotional content in this quantifiable way can contribute towards new metrics for creativity evaluation that can be applied at scale.

That New Hit Song on Spotify? Aspiring musicians are churning out tracks using generative artificial intelligence. Some are topping the charts. Nick Arter, a thirty-five-year-old in Washington, D.C., never quite managed to become a professional musician the old-fashioned way. He grew up in Harrisburg, Pennsylvania, in a music-loving family.

This work demonstrates "slimmable Neural Amp Models", whose size and computational cost can be changed without additional training and with negligible computational overhead, enabling musicians to easily trade off between the accuracy and compute of the models they are using. The method's performance is quantified against commonly-used baselines, and a real-time demonstration of the model in an audio effect plug-in is developed.

Improvisation--the art of spontaneous creation that unfolds moment-to-moment without a scripted outcome--requires practitioners to continuously sense, adapt, and create anew. It is a fundamental mode of human creativity spanning music, dance, and everyday life. The open-ended nature of improvisation produces a stream of novel, unrepeatable moments--an aspect highly valued in artistic creativity. In parallel, open-endedness (OE)--a system's capacity for unbounded novelty and endless "interestingness"--is exemplified in natural or cultural evolution and has been considered "the last grand challenge" in artificial life (ALife). The rise of generative AI now raises the question in computational creativity (CC) research: What makes a "good" improvisation for AI? Can AI learn to improvise in a genuinely open-ended way? In this work-in-progress paper, we report insights from in-depth interviews with 6 experts in improvisation across dance, music, and contact improvisation. We draw systemic connections between human improvisa-tional arts and the design of future experiential AI agents that could improvise alone or alongside humans--or even with other AI agents--embodying qualities of improvisation drawn from practice: active listening (umwelt and awareness), being in the time (mindfulness and ephemerality), embracing the unknown (source of randomness and serendipity), non-judgmental flow (acceptance and dynamical stability, balancing structure and surprise (unpredictable criticality at edge of chaos), imaginative metaphor (synaesthesia and planning), empathy, trust, boundary, and care (mutual theory of mind), and playfulness and intrinsic motivation (maintaining interestingness).

This paper investigates the importance of personal ownership in musical AI design, examining how practising musicians can maintain creative control over the compositional process. Through a four-week ecological evaluation, we examined how a music variation tool, reliant on the skill of musicians, functioned within a composition setting. Our findings demonstrate that the dependence of the tool on the musician's ability, to provide a strong initial musical input and to turn moments into complete musical ideas, promoted ownership of both the process and artefact. Qualitative interviews further revealed the importance of this personal ownership, highlighting tensions between technological capability and artistic identity. These findings provide insight into how musical AI can support rather than replace human creativity, highlighting the importance of designing tools that preserve the humanness of musical expression.