The 2026 World Cup promises a global celebration. Many Arab fans may find themselves excluded. For the first time in World Cup history, eight Arab nations have qualified for this year's tournament, including Morocco, Tunisia, Egypt, Algeria, Saudi Arabia, Qatar, Iraq, and Jordan--double the number of teams that qualified for Qatar in 2022. Yet, the tournament is taking place at an unprecedented moment of heightened geopolitical tension. The US-Israel war with Iran, which began in February of this year, has caused ripple effects across Gulf states and neighboring countries in the Levant, including Lebanon, Palestine, and Jordan, reshaping the security around travel and mobility for fans and players hailing from the region. The US State Department has fully suspended visa issuance for nationals from countries with teams that qualified, including Iran and Haiti--despite it being the first time Haiti has qualified for a World Cup since 1974.

Some critics say the increasing use of AI could result in'asynchronous, agent-driven management'. Some critics say the increasing use of AI could result in'asynchronous, agent-driven management'. 'I didn't want to be the guinea pig': inside tech's AI-fueled manager purge As tech companies pour billions into artificial intelligence bets and slash their workforces, middle managers are squarely in the crosshairs. A trend is emerging: when tech CEOs announce that AI is making it possible to do more with fewer workers, they promise to flatten their structures by cutting away what they call unnecessary management layers and bureaucracy. Just last week, the cryptocurrency exchange Coinbase laid off 14% of its workforce while gesturing to the thrill of AI-fueled, minimal-management efficiency.

Learning goal-directed behavior in environments with sparse feedback is a major challenge for reinforcement learning algorithms. One of the key difficulties is insufficient exploration, resulting in an agent being unable to learn robust policies. Intrinsically motivated agents can explore new behavior for their own sake rather than to directly solve external goals. Such intrinsic behaviors could eventually help the agent solve tasks posed by the environment. We present hierarchicalDQN (h-DQN), a framework to integrate hierarchical action-value functions, operating at different temporal scales, with goal-driven intrinsically motivated deep reinforcement learning. A top-level q-value function learns a policy over intrinsic goals, while a lower-level function learns a policy over atomic actions to satisfy the given goals.

We introduce BioTrove, the largest publicly accessible dataset designed to advance AI applications in biodiversity. Curated from the iNaturalist platform and vetted to include only research-grade data, BioTrove contains 161.9 million images, offering unprecedented scale and diversity from three primary kingdoms: Animalia (animals), Fungi (fungi), and Plantae (plants), spanning approximately 366.6K species. Each image is annotated with scientific names, taxonomic hierarchies, and common names, providing rich metadata to support accurate AI model development across diverse species and ecosystems.We demonstrate the value of BioTrove by releasing a suite of CLIP models trained using a subset of 40 million captioned images, known as BioTrove-Train. This subset focuses on seven categories within the dataset that are underrepresented in standard image recognition models, selected for their critical role in biodiversity and agriculture: Aves (birds), Arachnida} (spiders/ticks/mites), Insecta (insects), Plantae (plants), Fungi (fungi), Mollusca (snails), and Reptilia (snakes/lizards). To support rigorous assessment, we introduce several new benchmarks and report model accuracy for zero-shot learning across life stages, rare species, confounding species, and multiple taxonomic levels.We anticipate that BioTrove will spur the development of AI models capable of supporting digital tools for pest control, crop monitoring, biodiversity assessment, and environmental conservation. These advancements are crucial for ensuring food security, preserving ecosystems, and mitigating the impacts of climate change. BioTrove is publicly available, easily accessible, and ready for immediate use.

We present the first uniform-in-time high-probability bound for SGD under the PL condition, where the gradient noise contains both Markovian and martingale difference components. This significantly broadens the scope of finite-time guarantees, as the PL condition arises in many machine learning and deep learning models while Markovian noise naturally arises in decentralized optimization and online system identification problems. We further allow the magnitude of noise to grow with the function value, enabling the analysis of many practical sampling strategies. In addition to the high-probability guarantee, we establish a matching $1/k$ decay rate for the expected suboptimality. Our proof technique relies on the Poisson equation to handle the Markovian noise and a probabilistic induction argument to address the lack of almost-sure bounds on the objective. Finally, we demonstrate the applicability of our framework by analyzing three practical optimization problems: token-based decentralized linear regression, supervised learning with subsampling for privacy amplification, and online system identification.

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An Indian university is facing backlash after one of its professors was caught falsely presenting a Chinese-made robot dog at a major artificial intelligence summit, it has reportedly since been asked to leave, as the institution's own. "You need to meet Orion. This has been developed by the Centre of Excellence at Galgotias University," Neha Singh, a professor of communications, told Indian state-run broadcaster DD News this week. The episode has drawn sharp criticism and has cast an uncomfortable spotlight on India's AI ambitions. The embarrassment was amplified by Electronics and Information Technology Minister Ashwini Vaishnaw, who shared the video clip on his official social media account before the backlash.

While performing abest-first search, compilation errors constitute 88.7% ofprogramfailures.

Each algorithm optimises its parameters with respect to an objective, such as Q-learning or policy gradient, that defines its semantics.

Gradient NormPreserving (GNP) architectures where each layer preserves the gradient norm during backpropagation. For 1-Lipschitz Convolutional Neural Networks (CNNs), this involves using orthogonal convolutions (convolution layers with an orthogonal Jacobian matrix) [Li et al., 2019b, Trockman and Kolter,