The federal jury in Oakland, California, found Altman, OpenAI and its president, Greg Brockman, not liable for Elon Musk's claims that they unjustly enriched themselves and broke a founding contract made with Musk when founding the startup. The verdict, delivered after less than two hours of deliberation, is a stark rebuke of Musk and his lawyer's claims that Altman "stole a charity" through his leadership of OpenAI . It also provides the AI firm with a clear path ahead to pursue going public later this year at about a $1tn valuation . The jury's finding is a non-binding, advisory verdict that left Judge Yvonne Gonzalez Rogers with ultimate power to issue her own ruling in the case. Gonzalez Rogers immediately said that she would agree with the jury's decision and dismissed Musk's claims.

A California jury has tossed out Elon Musk's high-profile lawsuit against OpenAI and its boss Sam Altman. In a unanimous verdict, the case was thrown out because Musk had filed his lawsuit after a statute of limitations to bring such claims had expired. Musk had accused Altman of breaching a non-profit contract by shifting the ChatGPT-maker to a for-profit company after Musk donated $38m (£28.5m). Musk had argued Altman deceived him by accepting his money and then reneging on OpenAI's original non-profit mission to develop artificial intelligence (AI) technology for the benefit of humanity. Jurors spent three weeks viewing internal correspondence and hearing testimony, and arrived at a verdict on Monday after deliberating for roughly two hours.

A United States jury has ruled against Elon Musk in his lawsuit against OpenAI, finding the artificial intelligence (AI) company not liable to the world's richest person for having allegedly strayed from its original mission to benefit humanity. In a unanimous verdict on Monday, the jury in Oakland, California US federal court said Musk had brought his case too late. Following the verdict, Musk's lawyer said he reserved the right to appeal, but the judge suggested he may have an uphill battle because whether the statute of limitations ran out before Musk sued was a factual issue. "There's a substantial amount of evidence to support the jury's finding, which is why I was prepared to dismiss on the spot," US District Judge Yvonne Gonzalez Rogers said. Musk was a co-founder of OpenAI, the company that launched in 2015 and went on to create ChatGPT.

After three weeks of testimony and not much deliberation, a jury has ruled against Elon Musk, finding that Sam Altman and Greg Brockman were not liable in the case. The jury found that the statute of limitations had already passed when Musk sued the two executives. Musk filed his lawsuit in 2024, accusing them of stealing a charity following his departure from the AI lab in 2018. Though the jury in the case served only an advisory role, Judge Yvonne Gonzalez Rogers agreed with the jury's ruling. Musk's claims of breach of charitable trust and unjust enrichment are dismissed as untimely, she said according to Though Musk could still appeal the ruling, Rogers told his lawyer she would dismiss an appeal on the spot.

The nine-member panel took only two hours to return a verdict in favor of OpenAI on Monday, which the judge quickly adopted as her own final decision. Elon Musk suffered the worst defeat possible in his legal battle against OpenAI as a federal jury and a judge ruled he waited too long to bring his claims against the AI startup and its top executives, Sam Altman and Greg Brockman. The jury's decision was a nonbinding recommendation sent to US district judge Yvonne Gonzalez Rogers, though she immediately accepted it on Monday as her own, making it final. The nine-member panel delivered the unanimous verdict in an Oakland, California courtroom after deliberating for under two hours. They found that statutes of limitations expired well before Musk filed his lawsuit in 2024.

ChatGPT Pro users can now connect banking and investment accounts from over 12,000 financial institutions through Plaid integration for personalized financial insights. PCWorld highlights that while the feature offers read-only access and 30-day data deletion, it raises significant privacy and security concerns for users. The AI-powered dashboard provides financial summaries and answers queries, but users must weigh convenience against potential data risks. If there's one area where LLMs excel, it's plowing through reams of data and teasing out patterns, trends, and insights. So it's not surprising that OpenAI is zeroing in on personal finance, with ChatGPT now capable of delving into our banking, checking, and investment accounts .

Inside Anduril and Meta's quest to make smart glasses for warfare It's been a year since the duo entered the US Army's troubled augmented-reality contest. Here's what it looks like so far. The defense-tech company Anduril has shared new details about the augmented-reality headset for the military it's prototyping with Meta, including a vision for ordering drone strikes via eye-tracking and voice commands. Quay Barnett, who leads the efforts as a vice president at Anduril following a career in the Army's Special Operations Command, says his fundamental goal is to optimize "the human as a weapons system." The vision is undoubtedly cyborg-inspired: Barnett wants drones and soldiers to see together, share information seamlessly, and make decisions as one. Anduril actually has two such projects in the works.

Musk v. Altman week 3: Musk and Altman traded blows over each other's credibility. Now the jury will pick a side. In the final week of the Musk v. Altman trial, lawyers attacked the credibility of the two tech leaders. Sam Altman was accused of lying and self-dealing, while Elon Musk was portrayed as a power-seeker trying to control artificial general intelligence. The case unearthed new details about the two arch-rivals and OpenAI's contested nonprofit status, as well as a golden trophy of a donkey's ass awarded to an employee who challenged Musk. Michelle Kim, who's also a lawyer, has been in court throughout the Musk v. Altman trial.

This paper proposes a deep reinforcement learning (DRL)-based event-triggered controller design for networked artificial pancreas (AP) systems. Although existing DRL-based AP controllers typically assume periodic control updates, networked control systems (NCSs) require a reduction in communication frequency to achieve energy-efficient operation, which is directly tied to control updates. However, jointly learning both insulin dosing and update timing significantly increases the complexity of the learning problem. To alleviate this complexity, we develop a practical DRL-based controller design that avoids explicitly learning update timing by introducing a rule-based criterion defined by changes in blood glucose. As a result, decision-making occurs at irregular intervals, and the problem is naturally formulated as a semi-Markov decision process (SMDP), for which we extend a standard DRL algorithm. Numerical experiments demonstrate that the proposed method improves communication efficiency while maintaining control performance.

Deep generative models provide flexible frameworks for modeling complex, structured data such as images, videos, 3D objects, and texts. However, when applied to sequences of human skeletons, standard variational autoencoders (VAEs) often allocate substantial capacity to nuisance factors-such as camera orientation, subject scale, viewpoint, and execution speed-rather than the intrinsic geometry of shapes and their motion. We propose the Elastic Shape - Variational Autoencoder (ES-VAE), a geometry-aware generative model for skeletal trajectories that leverages the transported square-root velocity field (TSRVF) representation on Kendall's shape manifold. This representation inherently removes rigid translations, rotations, and global scaling of shapes, and temporal rate variability of sequences, isolating the underlying shape dynamics. The ES-VAE encoder maps skeletal sequences to a low-dimensional latent space incorporating the Riemannian logarithm map, while the decoder reconstructs sequences using the corresponding exponential map. We demonstrate the effectiveness of ES-VAE on two datasets. First, we analyze skeletal gait cycles to predict clinical mobility scores and classify subjects into healthy and post-stroke groups. Second, we evaluate action recognition on the NTU RGB+D dataset. Across both settings, ES-VAE consistently outperforms standard VAEs and a range of sequence modeling baselines, including temporal convolutional networks, transformers, and graph convolutional networks. More broadly, ES-VAE provides a principled framework for learning generative models of longitudinal data on pose shape manifolds, offering improved latent representation and downstream performance compared to existing deep learning approaches.