Sony removes 135,000 'deepfakes' of its artists' music Music giant Sony Music says it has requested the removal of more than 135,000 songs by fraudsters impersonating its artists on streaming services. The so-called deepfakes were created using generative AI, and targeted some of the company's biggest acts, who include Beyoncé, Queen and Harry Styles In the worst cases, [the deepfakes] potentially damage a release campaign or tarnish the reputation of an artist, said Dennis Kooker, president of Sony's global digital business. The company says the number of songs generated in this fashion is only increasing as artificial intelligence technology becomes cheaper and easier to access. It believes the 135,000 tracks it has discovered to date represents just a percentage of the total uploaded to streaming services. Since last March alone, it has identified some 60,000 songs falsely purporting to feature artists from their roster.

We have listened, Technology Secretary Liz Kendall said on Wednesday, saying the government no longer favours that approach. However, the government's position is now unclear, saying it no longer has a preferred option for what to do next. Kendall said the government had engaged extensively with people in the creative and AI industries. It is attempting to balance the interests of the two sectors by giving creatives control how their work is used, while recognising AI models need to be trained on work such as writing, music and video. In a report published on Wednesday, the government said there was no consensus on how these objectives should be achieved.

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Yellowstone's ravens may memorize wolf hunting hotspots--to feast The birds will fly over 90 miles to dine where wolves have drawn blood. Breakthroughs, discoveries, and DIY tips sent six days a week. When wolves are on the hunt, a kill rarely goes unnoticed for long. In the elk-and deer-rich areas of northern Yellowstone National Park, ravens are often among the first scavengers to arrive on the scene, swooping down to feast on scraps left behind by the howling canines. Field biologists have long assumed that the birds simply follow wolves as they track and take down their prey.

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We derive a tighter inequality than Cauchy-Schwarz Inequality, leverage it to refine Pearson's

As inthe classical problem, weights are fixed by an adversary and elements appear in random order. In contrast to previous variants of predictions, our algorithm only has access toamuch weakerpiece ofinformation: anadditive gapc.

We study adversarially robust transfer learning, wherein, given labeled data on multiple (source) tasks, the goal is to train a model with small robust error on a previously unseen (target) task. In particular, we consider a multi-task representation learning (MTRL) setting, i.e., we assume that the source and target tasks admit a simple (linear) predictor on top of a shared representation (e.g., the final hidden layer of a deep neural network). In this general setting, we provide rates on the excess adversarial (transfer) risk for Lipschitz losses and smooth nonnegative losses. These rates show that learning a representation using adversarial training on diverse tasks helps protect against inference-time attacks in data-scarce environments. Additionally, we provide novel rates for the single-task setting.