A cease-and-desist letter accuses the search giant's AI tools of training on and copying protected works. The letter includes examples of images from several Disney properties including Deadpool, Moana, Star Wars and others, reproduced by Google's AI tools. Disney is demanding that Google implement guardrails within all its AI products to prevent further infringement. Today Disney with OpenAI to license its characters for use in Sora, OpenAI's video generator. The deal will see Disney invest $1 billion in OpenAI (a paltry sum by), with the option to purchase additional equity at a later date.

GPU prices could follow RAM's big rise It looks like Disney wasted no time delivering on CEO Bob Iger's promise to bring AI-generated content to Disney+. On Thursday, the company announced the start of a three-year licensing agreement with OpenAI to bring more than 200 of its beloved characters, including those from Star Wars and Pixar, to the Sora app and ChatGPT. With the deal in place, OpenAI users will be able to prompt ChatGPT to generate images that tap into Disney's intellectual property, with costumes, props, vehicles and environments covered. The agreement does not include voices or "talent likenesses," meaning Sora users won't be able prompt the app to make a video with Black Widow and get something with Scarlett Johansson in it. Instead, both Sora and ChatGPT will only have access to animated and illustrated versions of Marvel and Star Wars characters like Black Panther, Captain America, Han Solo, Darth Vader and others.

The heirs of an 83-year-old Connecticut mother who was killed by her son in August filed a lawsuit against OpenAI and Microsoft, claiming ChatGPT amplified his "paranoid delusions."

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A woman and her relatives look at her home, which was damaged during a night of Russian missile and drone strikes, amid Russia's attack on Ukraine, in Novi Petrivtsi, outside Kyiv, on Saturday. Microchip manufacturers Intel, Advanced Micro Devices (AMD) and Texas Instruments were accused in a series of lawsuits of failing to keep their technology out of Russian-made weapons used to kill and wound civilians in Ukraine. Those companies -- along with a company owned by Warren Buffett's Berkshire Hathaway -- demonstrated willful ignorance" as third parties resold restricted chips to Russia to power drones and missiles in violation of U.S. sanctions, according to one of the five suits, filed Wednesday in state court in Texas. The lawsuits, filed on behalf of dozens of Ukrainian civilians by Mikal Watts and prominent law firm Baker & Hostetler, cite five attacks between 2023 and 2025 that killed dozens of people. One attack allegedly involved Iranian-made drones with components associated with Intel and AMD, while the others involved Russian-made KH-101 cruise missiles and Iskander ballistic missiles.

Ghislaine Maxwell's ultimate humiliation: Epstein's sex trafficker girlfriend poses in outrageous outfits and exposes herself in dozens of photos released from the billionaire paedophile's files I was falsely accused of being the Brown University shooter... Silent Trump flees growing storm over Epstein'cover-up' as he jets off for holidays without ANY comment Truth about THIS photo of Karoline Leavitt's face... and why if she was non-binary and disabled, Vanity Fair would never have done this: KENNEDY Why Conan O'Brien'stopped party guests calling 911' on Nick Reiner: Insiders reveal disturbing new details of final hours before Rob and Michele murders After 27 years as a TV anchor I was suddenly pulled off screens. My boss's explanation was a brutal lesson in loyalty Emily in Paris cast left'aghast' and'walking on eggshells' as off-camera drama becomes overwhelming... and whispers swirl about a CURSE Doctors said my hip pain was just tendinitis from sitting all day at work.

The Weibull distribution is a commonly adopted choice for modeling the survival of systems subject to maintenance over time. When only proxy indicators and censored observations are available, it becomes necessary to express the distribution's parameters as functions of time-dependent covariates. Deep neural networks provide the flexibility needed to learn complex relationships between these covariates and operational lifetime, thereby extending the capabilities of traditional regression-based models. Motivated by the analysis of a fleet of military vehicles operating in highly variable and demanding environments, as well as by the limitations observed in existing methodologies, this paper introduces WTNN, a new neural network-based modeling framework specifically designed for Weibull survival studies. The proposed architecture is specifically designed to incorporate qualitative prior knowledge regarding the most influential covariates, in a manner consistent with the shape and structure of the Weibull distribution. Through numerical experiments, we show that this approach can be reliably trained on proxy and right-censored data, and is capable of producing robust and interpretable survival predictions that can improve existing approaches.

Abstract--Graph clustering plays a pivotal role in unsupervised learning methods like spectral clustering, yet traditional methods for graph clustering often perpetuate bias through unfair graph constructions that may underrepresent some groups. The current research introduces novel approaches for constructing fair k-nearest neighbor (kNN) and fair ϵ-neighborhood graphs that proactively enforce demographic parity during graph formation. By incorporating fairness constraints at the earliest stage of neighborhood selection steps, our approaches incorporate proportional representation of sensitive features into the local graph structure while maintaining geometric consistency. Our work addresses a critical gap in pre-processing for fair spectral clustering, demonstrating that topological fairness in graph construction is essential for achieving equitable clustering outcomes. Widely used graph construction methods like kNN and ϵ-neighborhood graphs propagate edge based disparate impact on sensitive groups, leading to biased clustering results. Providing representation of each sensitive group in the neighborhood of every node leads to fairer spectral clustering results because the topological features of the graph naturally reflect equitable group ratios. This research fills an essential shortcoming in fair unsupervised learning, by illustrating how topological fairness in graph construction inherently facilitates fairer spectral clustering results without the need for changes to the clustering algorithm itself. Thorough experiments on three synthetic datasets, seven real-world tabular datasets, and three real-world image datasets prove that our fair graph construction methods surpass the current baselines in graph clustering tasks. Machine learning algorithms are widely used for decision-making in a variety of fields, including criminal justice [1], healthcare [2], [3], and finance [4]. The reason for this is that these algorithms have been shown to be very accurate and effective at analyzing big datasets. The increasing prevalence of these algorithms has raised questions regarding their fairness and potential to reinforce societal biases [5], [6]. These biases can result in unfair treatment of certain groups of people thereby create significant societal implications. Recently, concerns have been raised about the fairness of clusters produced by popular clustering algorithms.

In the governance of emerging technologies, ethical guidance has often relied on so-called soft law instruments--codes of conduct, guidelines, or frameworks--designed to promote responsible behavior without imposing binding legal constraints. This is partly due to the difficulty of imposing harmonized regulations across the EU, especially in a global context characterized by strong reservations expressed by other international actors, e.g. the United States of America, with regard to the regulation of artificial intelligence (AI) that "unduly burdens AI innovation" (Kratsios, Sacks, and Rubio 2025) . Another reason is related to the principle, upheld in several member states such as Germany, that protects scientific freedom by constitutional law. Nevertheless, the recent trajectory of technological regulation in the European Union shows that soft law can evolve into hard law: this has been the case, notably, with the adoption of the AI Act (European Commission 2022; Terpan 2015) .

Generative audio models, based on diffusion and autoregressive architectures, have advanced rapidly in both quality and expressiveness. This progress, however, raises pressing copyright concerns, as such models are often trained on vast corpora of artistic and commercial works. A central question is whether one can reliably verify if an artist's material was included in training, thereby providing a means for copyright holders to protect their content. In this work, we investigate the feasibility of such verification through membership inference attacks (MIA) on open-source generative audio models, which attempt to determine whether a specific audio sample was part of the training set. Our empirical results show that membership inference alone is of limited effectiveness at scale, as the per-sample membership signal is weak for models trained on large and diverse datasets. However, artists and media owners typically hold collections of works rather than isolated samples. Building on prior work in text and vision domains, in this work we focus on dataset inference (DI), which aggregates diverse membership evidence across multiple samples. We find that DI is successful in the audio domain, offering a more practical mechanism for assessing whether an artist's works contributed to model training. Our results suggest DI as a promising direction for copyright protection and dataset accountability in the era of large audio generative models.