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Suno Says It Will Start Watermarking Songs

2 months 4 weeks ago
Suno says it will begin watermarking and fingerprinting AI-generated songs, tighten download policies to curb mass distribution, and explicitly prohibit deceptive audio and unauthorized voice or likeness cloning. The changes come as the company faces mounting copyright lawsuits, a recent adverse ruling in Germany, and scrutiny over a past data breach that revealed training-data sources. TechCrunch reports: In a blog post, co-founder and CEO Mikey Shulman shared core principles and said that the platform wants to promote original creation while enabling more people to make music with its AI tools. One of the key points of contention involved users uploading AI-generated songs on other streaming platforms and gaming the system to earn revenue. Suno said that now it will use audio watermarking and fingerprinting to prevent misuse on other streaming platforms. It's not clear if Suno will use an existing system like Google's Synth ID or adopt a new one, and the company did not say when contacted by TechCrunch about this. The startup also signed an agreement with lyrics provider Musixmatch to use its Sentinel system for copyright detection, the blog post said. [...] The company added that it plans to add a new download policy to bar mass distribution on streaming platforms, but declined to provide details on the record. Suno has also changed its community guidelines to explicitly prohibit "deceptive audio presented as real" and "using a real person's voice or likeness without permission" to prevent copycats.

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ByteDance Is Training a 10-Trillion-Parameter Model To Chase the Frontier

2 months 4 weeks ago
ByteDance is reportedly training an AI model with roughly 10 trillion parameters as it tries to close the gap with leading frontier systems such as Anthropic's Mythos. The model is still in early pre-training, and its eventual performance will depend on more than scale alone, but the project underscores how aggressively Chinese firms are pushing frontier AI despite limits on access to advanced chips. The Next Web reports: The size is itself the statement. At roughly 10 trillion parameters, the model would be more than three times as large as Moonshot's Kimi K3, which sits among the biggest Chinese models today at about 2.8 trillion. [...] Parameter count is not everything, of course. Bigger models are not automatically better, and the industry has learned that data quality, training technique and efficiency often matter as much as raw scale. Even so, committing the compute to train a model this size is a declaration in its own right, a signal that ByteDance wants to compete at the very top rather than ship a capable also-ran.

Read more of this story at Slashdot.

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