Musk testifies at OpenAI trial it's not OK to'loot a charity' Elon Musk has taken the stand at a high-stakes trial over the future of OpenAI, casting his lawsuit against the ChatGPT maker as a defence of charitable giving. The world's richest person is suing OpenAI, its cofounder and chief executive officer, Sam Altman, and its president, Greg Brockman, and said on the stand on Tuesday that they betrayed him and the public by abandoning OpenAI's mission to be a benevolent steward of AI for humanity and transforming the nonprofit into a profit-seeking juggernaut. Musk, who founded carmaker Tesla and rocket company SpaceX, also said he is committed to serving the public by working 80-to 100-hour weeks and generally not taking vacations. "I like working and solving problems that make people's lives better," he said. Before Musk began testifying, Bill Savitt, a lawyer for OpenAI and Altman, told jurors during his opening statement it was Musk who saw dollar signs as he helped finance OpenAI's early growth and pushed it to become a for-profit business, one he might eventually lead as CEO.

Global military spending is up again, according to SIPRI's latest report showing the 11th consecutive year of growth. But it's based on open data and not everything makes the list. Ukrainian drones strike Russia's Tuapse refinery for third time

In this supplementary material, we provide the proofs of convergence analysis in Section 1, 1-vs-1 transformation employed in the classification and semantic segmentation tasks in Section 2, the coordinate-wise and the preprocessing method of the LSTM teacher in Section 3, the loss functions of YOLO-v3 in Section 4, more experiments of image classification in Section 5, and the inferences of semantic segmentation in Section 6. A differentiable function e()is L-smooth with gradient Lipschitz constant C (uniformly Lipschitz continuous), if e(x) e(y) C x y, x,y. The function is called block-wise smooth with gradient Lipschitz Ci, if i e(x i,xi) ie(x i,x i) Ci xi x i, x,x (1) or with gradient Lipschitz constants { Ci}, if i e(x i,xi) ie(x i,xi) Ci x i x i, x,x (2) Further, Let Gmax max{Ci, Ci, k} C. Definition 2. For a differentiable function e(), if e(x) = 0, then x is a first-order stationary solution (SS1). For a differentiable function e(), if x is a SS1, and there exists ϵ > 0 so that for any y in the ϵ-neighborhood of x, we have e(x) e(y), then xis a local minimum. A saddle point xis an SS1 that is not a local minimum. If λmin( 2e(x)) < 0, x is a strict (non-degenerate) saddle point.

With the concept of teaching being introduced to the machine learning community, a teacher model start using dynamic loss functions to teach the training of a student model. The dynamic intends to set adaptive loss functions to different phases of student model learning. In existing works, the teacher model 1) merely determines the loss function based on the present states of the student model, i.e., disregards the experience of the teacher; 2) only utilizes the states of the student model, e.g., training iteration number and loss/accuracy from training/validation sets, while ignoring the states of the loss function. In this paper, we first formulate the loss adjustment as a temporal task by designing a teacher model with memory units, and, therefore, enables the student learning to be guided by the experience of the teacher model. Then, with a dynamic loss network, we can additionally use the states of the loss to assist the teacher learning in enhancing the interactions between the teacher and the student model. Extensive experiments demonstrate our approach can enhance student learning and improve the performance of various deep models on real-world tasks, including classification, objective detection, and semantic segmentation scenarios.

US lawmakers gave King Charles III a standing ovation upon mentioning the importance of "checks and balances" on executive power during his historic speech to Congress. Ukrainian drones strike Russia's Tuapse refinery for third time

Why is Israel still in southern Lebanon? A war to shape Lebanon's future Video captured massive explosions in southern Lebanon in what the Israeli military called strikes on a Hezbollah tunnel. Other attacks happened nearby, as Israeli Defence Minister Israel Katz vowed that southern Lebanon's fate will be like Gaza's. Ukrainian drones strike Russia's Tuapse refinery for third time Qatar says using Hormuz Strait as political weapon is'unacceptable' Australia's top diplomat visits China to talk energy security

'It's Undignified': Hundreds of Workers Training Meta's AI Could Be Laid Off More than 700 people working for a Meta contractor in Ireland are at risk of losing their jobs, documents show. Hundreds of workers in Ireland tasked with refining Meta's AI models have been told that their jobs are at risk as the company embarks on a sweeping new round of layoffs, according to documents obtained by WIRED. The affected workers are employed by the Dublin-based firm Covalen, which handles various content moderation and labeling services for Meta. The workers were informed of the layoffs over a brief video meeting on Monday afternoon and were not allowed to ask questions, according to Nick Bennett, one of the employees on the call. "We had a pretty bad feeling [before the meeting]," he says.

The war in Iran has drawn attention to arrests in the United Arab Emirates over online content, but the legal framework behind that enforcement has existed for years. When Iranian missile and drone attacks on the United Arab Emirates began earlier this year, cybercrime laws also came into focus as the conflict played out in the sky--and online. Authorities announced arrests linked to misleading videos, AI-generated clips, illegal filming, and the spread of misinformation. For many residents, the reaction was one of surprise: How could a screenshot, forwarded video, or social media post become a criminal matter? The answer lies in legal frameworks that were already in place.