'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.

What are Russia's gains from the Iran war? 'We are not losers; we are winners' Ukrainian drones strike Russia's Tuapse refinery for third time NewsFeed Ukrainian drones strike Russia's Tuapse refinery for third time Ukraine has targeted a major Russian oil refinery in the Black Sea port city of Tuapse for the third time in less than two weeks, setting off a fresh blaze and prompting authorities to evacuate local residents. Qatar says using Hormuz Strait as political weapon is'unacceptable' Australia's top diplomat visits China to talk energy security

The United Arab Emirates has announced it's withdrawing from OPEC and OPEC+. Al Jazeera's Michael Appel outlines the significance of the announcement and its likely impact on the energy market. 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

Uncertainty estimation plays an important role for future reliable deployment of deep segmentation models in safety-critical scenarios such as medical applications. However, existing methods for uncertainty estimation have been limited by the lack of explicit guidance for calibrating the prediction risk and model confidence. In this work, we propose a novel fine-grained reward maximization (FGRM) framework, to address uncertainty estimation by directly utilizing an uncertainty metric related reward function with a reinforcement learning based model tuning algorithm. This would benefit the model uncertainty estimation through direct optimization guidance for model calibration. Specifically, our method designs a new uncertainty estimation reward function using the calibration metric, which is maximized to fine-tune an evidential learning pre-trained segmentation model for calibrating prediction risk.

Recently there is a large amount of work devoted to the study of Markov chain stochastic gradient methods (MC-SGMs) which mainly focus on their convergence analysis for solving minimization problems. In this paper, we provide a comprehensive generalization analysis of MC-SGMs for both minimization and minimax problems through the lens of algorithmic stability in the framework of statistical learning theory. For empirical risk minimization (ERM) problems, we establish the optimal excess population risk bounds for both smooth and non-smooth cases by introducing on-average argument stability. For minimax problems, we develop a quantitative connection between on-average argument stability and generalization error which extends the existing results for uniform stability [38]. We further develop the first nearly optimal convergence rates for convex-concave problems both in expectation and with high probability, which, combined with our stability results, show that the optimal generalization bounds can be attained for both smooth and non-smooth cases. To the best of our knowledge, this is the first generalization analysis of SGMs when the gradients are sampled from a Markov process.