'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.

We introduce and study Swap Agnostic Learning. The problem can be phrased as a game between a predictor and an adversary: first, the predictor selects a hypothesis h; then, the adversary plays in response, and for each level set of the predictor {x X: h(x) = v} selects a loss-minimizing hypothesis cv C; the predictor wins if p competes with the adaptive adversary's loss. Despite the strength of the adversary, our main result demonstrates the feasibility Swap Agnostic Learning for any convex loss. Somewhat surprisingly, the result follows by proving an equivalence between Swap Agnostic Learning and swap variants of the recent notions Omniprediction [15] and Multicalibration [20]. Beyond this equivalence, we establish further connections to the literature on Outcome Indistinguishability [6, 14], revealing a unified notion of OI that captures all existing notions of omniprediction and multicalibration.

Unsupervised pre-training methods utilizing large and diverse datasets have achieved tremendous success across a range of domains. Recent work has investigated such unsupervised pre-training methods for model-based reinforcement learning (MBRL) but is limited to domain-specific or simulated data. In this paper, we study the problem of pre-training world models with abundant in-the-wild videos for efficient learning of downstream visual control tasks. However, inthe-wild videos are complicated with various contextual factors, such as intricate backgrounds and textured appearance, which precludes a world model from extracting shared world knowledge to generalize better. To tackle this issue, we introduce Contextualized World Models (ContextWM) that explicitly separate context and dynamics modeling to overcome the complexity and diversity of in-the-wild videos and facilitate knowledge transfer between distinct scenes. Specifically, a contextualized extension of the latent dynamics model is elaborately realized by incorporating a context encoder to retain contextual information and empower the image decoder, which encourages the latent dynamics model to concentrate on essential temporal variations. Our experiments show that in-the-wild video pre-training equipped with ContextWM can significantly improve the sample efficiency of MBRL in various domains, including robotic manipulation, locomotion, and autonomous driving.

Representation learning assumes that real-world data is generated by a few semantically meaningful generative factors (i.e., sources of variation) and aims to discover them in the latent space. These factors are expected to be causally disentangled, meaning that distinct factors are encoded into separate latent variables, and changes in one factor will not affect the values of the others. Compared to statistical independence, causal disentanglement allows more controllable data generation, improved robustness, and better generalization. However, most existing works assume unconfoundedness (i.e., there are no common causes to the generative factors) in the discovery process, and thus obtain only statistical independence. In this paper, we recognize the importance of modeling confounders in discovering causal generative factors.

The'Waymo of the sea' tracks sperm whale conversations More information Adding us as a Preferred Source in Google by using this link indicates that you would like to see more of our content in Google News results. The Project CETI glider can autonomously follow sperm whale vocalizations. Breakthroughs, discoveries, and DIY tips sent six days a week. Sperm whales () go deep. They can dive 1,300 to 4,000 feet-deep and also travel as much as 15,000 miles per year.

ARIA has a billion-dollar budget and big aspirations for tackling everything from epilepsy to Alzheimer's. The UK's Advanced Research and Innovation Agency (ARIA) was established in 2023 with the goal of pursuing "high-risk, high-reward" moonshots in sectors ranging from bolstering food security to new ways of ramping up human immunity . With more than £1 billion (about $1.3 billion) worth of government funding earmarked between now and 2030, one of ARIA's most ambitious programs is a £69 million initiative that aims to develop more tailored ways of modulating the human brain. The hope is to eventually address an entire range of disorders, from epilepsy to Alzheimer's. Reports have previously estimated that this suite of neurological conditions costs the UK economy tens of billions of dollars each year.

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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.

Follow this author to personalize your feed and get instant alerts. Follow Go to your personalized feed WHY FOLLOW? Smart Alerts: Get notified about major news as it happens. An eclectic crowd of leaders, artists, and innovators gathered Thursday night in New York City for the TIME100 Gala, celebrating the 100 Most Influential People of 2026 . Artists, actors, chefs, politicians, activists and business leaders brushed shoulders with each other as they shared in cocktails and conversations.