Is OpenAI Developing A Music Generator?

OpenAI is reportedly constructing a generative music model that may compose or enhance tracks from text or audio prompts, marking its most ambitious creative push since 2020’s Jukebox. The system can generate full compositions or add specific instrumental layers, like guitar or percussion, based on user direction.

To refine musical structure and phrasing, OpenAI has enlisted Juilliard students to annotate sheet music utilized in training. The model draws on prior breakthroughs in text-to-speech and sound synthesis and might be integrated into ChatGPT or Sora’s media pipeline.

Speaking with Rowan Cheung at DevDay, Altman said work soon to be overtaken by AI isn’t “real work.” He argued that technological progress continually redefines what counts as precious effort: “A farmer 50 years ago would take a look at what you and I do and say, ‘that’s not real work.’ Altman’s comment, each fatalistic and forward-looking, suggested that AI might erase tens of millions of jobs yet also create recent, less tangible types of productivity.

The corporate’s Startup Fund also recently backed Valthos, a biodefense enterprise using machine learning to detect pathogens and update vaccines in real time. Founded by former Palantir and Oxford researchers, Valthos claims its models can compress response times from months to hours, an advance OpenAI calls vital for “national resilience.” Read more.

Google’s upcoming Gemini Visual Layout might be probably the most visible leap yet. As a substitute of static text, you’ll get a modular feed of cards filled with tables, sliders, and interactive visuals. It feels more like a live dashboard than a chat window. It’s a shot across the bow of OpenAI’s Pulse and a signal that Google wants Gemini to handle serious workflow logic before its long-awaited “Agent Mode” automates execution.

Over at DeepMind, the brand new Dreamer 4 agent is teaching itself to think in worlds that don’t exist. It learns entirely inside a simulated “world model,” predicting what would occur as a substitute of trying things for real. It trained on Minecraft gameplay videos and have become the primary AI to mine diamonds without ever playing the sport. A transformer core predicts actions and rewards, while a “shortcut forcing” technique makes generation 25 times faster than conventional video-based learning. DeepMind says that very same tech could help train robots safely in simulation before they ever touch hardware.

Google’s physical world ambitions are only as daring. The corporate is backing a 400-megawatt carbon-capture power plant in Illinois to power its data centers while trapping as much as 90 percent of its emissions. It’s an enormous bet on a technology that’s still hit-or-miss, and one which critics say can’t offset the methane leaks that include burning natural gas.

And in an indication of where that is heading, Google’s own AI team is teasing a future where coding feels more like collaboration than syntax. Developers may soon “vibe code,” describing video games and tools in natural language while AI builds them in real time. Read more.

Researchers from the National University of Singapore, Princeton, and Illinois Urbana-Champaign have identified three key levers that make AI agents significantly smarter: data quality, algorithm designand reasoning style. Their findings show that a well-trained 4-billion-parameter model can match and even outperform competitors with as much as 32 billion parameters, highlighting that training strategy can outweigh sheer scale.