MAROKO133 Hot ai: King Gizzard Responds to Being Impersonated by AI on Spotify Terbaru 202

📌 MAROKO133 Eksklusif ai: King Gizzard Responds to Being Impersonated by AI on Spo

Acclaimed Australian prog rock band King Gizzard & the Lizard Wizard made headlines earlier this year when it quit Spotify, protesting the platform’s CEO, Daniel Ek, who heavily invested in an AI weapons company.

The band was one of several music acts to pull their music from Spotify over ethical concerns. Many of them have taken issue with artists earning very little money per stream on the platform, or the company donating a sizable sum to president Donald Trump’s inauguration ceremony.

Next, something extremely dark happened: an impostor created a band on Spotify with the extremely similar band name of “King Lizard Wizard” and used AI to generate songs with the same titles as actual King Gizzard songs that ripped off their entire lyrics and sound, accumulating tens of thousands of streams while remaining on the streaming service for weeks without detection.

Outspoken King Gizzard & The Lizard Wizard frontman Stu Mackenzie has now excoriated the platform after finding out about the ruse.

“[I’m] trying to see the irony in this situation,” he said in a statement quoted by The Music. “But seriously wtf we are truly doomed.”

Spotify has since pulled down the offending material, with a spokesperson telling Futurism in a statement that it “strictly prohibits any form of artist impersonation.”

“The content in question was removed for violating our platform policies, and no royalties were paid out for any streams generated,” the spokesperson added.

But the company’s reactive cat-and-mouse game isn’t exactly assuring artists, given Mackenzie’s reaction.

The incident highlights how Spotify is seriously struggling to keep AI slop at bay on its platform. While the company announced new policies to protect artists against “spam, impersonation, and deception” in September, we continue to see offending AI impersonations landing in users’ Release Radar and Discover Weekly playlists, which the company prominently recommends to them.

Worse yet, as  Platfomer reported last month, a separate King Gizzard impersonator had previously attempted to cash in on the band’s royalties using AI — meaning that if there was one band that Spotify should have been manually screening for impostors, it should have been King Gizzard.

In short, Spotify has a major PR headache to clean up as it reels from an onslaught of AI slop.

And a growing number of artists, including King Gizzard, have finally had enough and are looking for greener pastures. Who could blame them?

More on the incident: King Gizzard Pulled Their Music From Spotify in Protest, and Now Spotify Is Hosting AI Knockoffs of Their Songs

The post King Gizzard Responds to Being Impersonated by AI on Spotify appeared first on Futurism.

🔗 Sumber: futurism.com

📌 MAROKO133 Breaking ai: Quilter's AI just designed an 843‑part Linux compute

A Los Angeles-based startup has demonstrated what it calls a breakthrough in hardware development: an artificial intelligence system that designed a fully functional Linux computer in one week — a process that would typically consume nearly three months of skilled engineering labor.

Quilter, which has raised more than $40 million from investors including Benchmark, Index Ventures, and Coatue, used its physics-driven AI to automate the design of a two-board computer system that booted successfully on its first attempt, requiring no costly revisions. The project, internally dubbed "Project Speedrun," required just 38.5 hours of human labor compared to the 428 hours that professional PCB designers quoted for the same task.

The announcement also marks the first public disclosure that Tony Fadell, the engineer who led development of the iPod and iPhone at Apple and later founded Nest, has invested in the company and serves as an advisor.

"We didn't teach Quilter to draw; we taught it to think in physics," said Sergiy Nesterenko, Quilter's chief executive and a former SpaceX engineer, in an exclusive interview with VentureBeat. "The result wasn't a simulation — it was a working computer."

Circuit board design remains the forgotten bottleneck that delays nearly every hardware product

The announcement shines a light on an unglamorous but critical chokepoint in technology development: printed circuit board layout. While semiconductors and software have received enormous attention and investment, the green fiberglass boards that connect chips, memory, and components in virtually every electronic device remain stubbornly manual to design.

"Besides auto-routers, the technology really hadn't changed since the early '90s," Fadell told VentureBeat. "The best boards are still made by hand. You go to Apple, they've got the tools, and these guys are just pushing traces, checking everything, doing flood fills—and you're like, there's got to be a better way."

The PCB design process typically unfolds in three stages. Engineers first create a schematic — a logical diagram showing how components connect. Then a specialist manually draws the physical layout in CAD software, placing components and routing thousands of copper traces across multiple layers. Finally, the design goes to a manufacturer for fabrication.

That middle step — the layout — creates a persistent bottleneck. For a board of moderate complexity, the process typically consumes four to eight weeks. For sophisticated systems like computers or automotive electronics, timelines stretch to three months or longer.

"The timeline was always this elastic thing—they'd say, 'Yeah, that's two weeks minimum,'" Fadell recalled of his experience at Apple and Nest. "And we'd say, 'No, no. Work day and night. It's two weeks.' But it was always this fixed bottleneck."

The consequences ripple through hardware organizations. Firmware teams sit idle waiting for physical boards to test their code. Validation engineers cannot begin debugging. Product launches slip. According to Quilter's research, only about 10 percent of first board revisions work correctly, forcing expensive and time-consuming respins.

Project Speedrun put Quilter's AI to the test with an 843-component computer that booted on the first try

Project Speedrun was designed to push the technology to its limits while producing an easily understood result: a working computer that could boot Linux, browse the internet, and run applications.

The system consists of two boards based on NXP's i.MX 8M Mini reference platform, a processor architecture used in automotive infotainment, industrial automation, and machine vision applications.

The main system-on-module contains a quad-core ARM processor running at 1.8 gigahertz, 2 gigabytes of LPDDR4 memory, and 32 gigabytes of eMMC storage. A companion baseboard provides connectivity including Ethernet, USB, HDMI, and audio.

Together, the boards incorporate 843 components and 5,141 electrical connections, or "pins," routed across eight-layer circuit board stackups manufactured by Sierra Circuits in California. The minimum trace geometry reached 2 mils (two-thousandths of an inch) on the system-on-module — fine enough to require advanced high-density interconnect manufacturing techniques.

Quilter's AI completed the layout with approximately 98 percent routing coverage and zero design rule violations. Both boards passed power-on testing and successfully booted Debian Linux on the first attempt.

"We made an entire computer to demonstrate that this technology works," Nesterenko said. "We took something that's typically quoted at 400 to 450 hours, automated the vast majority of it, and reduced it to about 30 to 40 hours of cleanup time."

The cleanup time is work that human engineers still perform: reviewing the AI's output, fixing any issues, and preparing final fabrication files. But even with that overhead, the total elapsed time from schematic to fabricated boards collapsed from the typical 11 weeks to a single week.

Unlike ChatGPT, Quilter's AI learns by playing billions of games against the laws of physics

Quilter's technical approach differs fundamentally from the large language models that have dominated recent AI headlines. Where systems like GPT-5 or Claude learn to predict text based on massive training datasets of human writing, Quilter's AI learns by playing what amounts to an elaborate game against the laws of physics.

"Language models don't apply to us because this is not a language problem," Nesterenko explained. "If you ask it to actually create a blueprint, it has no training data for that. It has no context for that."

The company also rejected the seemingly obvious approach of training on examples of human-designed boards. Nesterenko cited three reasons: humans make frequent errors (explaining why most boards require revisions), the best designs are locked inside large companies unwilling to share proprietary data, and training on human examples would cap the AI's performance at human levels.

Instead, Quilter built what Nesterenko describes as a "game" where the AI agent makes sequential decisions — place this component here, route this trace there — and receives feedback based on whether the resulting design satisfies electromagnetic, thermal, and manufacturing constraints.

"What you're really changing is not the probability of getting a very specific outcome of the model, but the probability of choosing a certain action based on that experience," Nesterenko said.

The approach mirrors DeepMind's progression with its Go-playing systems. The original AlphaGo learned from human games, but its successor AlphaZero learned purely through self-play and ultimately surpassed human capability. Quilter harbors similar ambitions.

"In the long term, to come up with better designs for circuit boards than humans have ever tried to do," Nesterenko said.

Fadell drew a parallel to an earlier technological transition: &quo…

Konten dipersingkat otomatis.

🔗 Sumber: venturebeat.com