I would love to tell you that I was first introduced to dance music in underground Berlin clubs, where mysterious resident DJs blew my teenage mind performing indescribable magic with beats and synth lines. But that would be a lie. My first introduction to dance music came in the form of a futuristic 90s racing game called WipEout. Playing obsessively at a friend's house, I was introduced to the Chemical Brothers and Orbital, who both graced the soundtrack; not long after, the admirably chaotic sim Crazy Taxi introduced me to the Offspring, and Tony Hawk's Pro Skater had me grinding around to Bad Religion. I first heard Garbage on the soundtrack of an obscure PlayStation 2 DJ game, 2003's Amplitude, made by a Boston developer called Harmonix – the same developer that would later go on to create the insanely popular Guitar Hero series.

Engineering a high-performance race car requires a direct consideration of the human driver using real-world tests or Human-Driver-in-the-Loop simulations. Apart from that, offline simulations with human-like race driver models could make this vehicle development process more effective and efficient but are hard to obtain due to various challenges. With this work, we intend to provide a better understanding of race driver behavior and introduce an adaptive human race driver model based on imitation learning. Using existing findings and an interview with a professional race engineer, we identify fundamental adaptation mechanisms and how drivers learn to optimize lap time on a new track. Subsequently, we use these insights to develop generalization and adaptation techniques for a recently presented probabilistic driver modeling approach and evaluate it using data from professional race drivers and a state-of-the-art race car simulator. We show that our framework can create realistic driving line distributions on unseen race tracks with almost human-like performance. Moreover, our driver model optimizes its driving lap by lap, correcting driving errors from previous laps while achieving faster lap times. This work contributes to a better understanding and modeling of the human driver, aiming to expedite simulation methods in the modern vehicle development process and potentially supporting automated driving and racing technologies.

What exactly should we come to expect from AI-generated'artists'? Well, it depends on how comfortable you are with AI technology slowly but surely encroaching on normal human life. Nothing can replace organic artistry though...or can it? Meet FN Meka - the world's first AI robot rapper, who is currently doing his thing, releasing music on TikTok. His content online has earned him over a billion views and 9 million TikTok followers.

The music streaming service Deezer is developing an AI tool to help determine whether new songs added to its library should be flagged as explicit. The new tool is still in development, but company executives hope it will help them sort through the up to 40,000 new tracks it receives every day from record companies, most of which are unlabeled. Once finalized, the tool won't automatically label tracks, but instead flag tracks for review, which will be conducted by one of the company's executives. 'When it comes to figuring out what explicit lyrics are, there is no general consensus,' Deezer's Manuel Moussallam wrote in a blog post outlining the new project. 'It's obviously a cultural issue, with lots of considerations about the intended audience and the listening context.' 'As is the case with movies, the primary objective of tagging a piece as "explicit" is to provide guidance to determine how suitable it is for an intended audience.'