The video games industry is unpredictable. If you'd told us this time last year that a previously unknown French studio would claim game of the year, Battlefield 6 would knock Call of Duty off the top of the annual charts and that Saudi Arabia would buy gaming giant Electronic Arts (EA) we'd have been... sceptical. So you'd have to be very sure of yourself - or very foolish - to try and predict what's going to happen in the year ahead. Luckily, we're not in the crystal ball business here at BBC Newsbeat, but there are a few things we can be confident video game fans should keep an eye on in 2026. GTA 6: Will it actually arrive in 2026?

Artificial intelligence (AI) is rapidly transforming public - sector processes worldwide, yet standardized measures rarely address the unique drivers, governance models, and cultural nuances of the Gulf Cooperation Council (GCC) countries. This study employs a theory - driven foundation derived from an in - depth analysis of literature review and six National AI Strategies (NASs), coupled with a data - driven approach that utilizes a survey of 203 mid - and senior - level government employees and advanced statistical techniques (K - Means clustering, Principal Component Analysis, and Partial Least Squares Structural Equation Modeling). By combining policy insights with empirical evidence, the research develops and validates a novel AI Adoption Index specifically tailored to the GCC public sector. Findings indicate that robust technical infrastructure and clear policy mandates exert the strongest influence on successful AI implementations, overshadowing organizational readiness in early adoption stages. The combined model explains 70% of the variance in AI outcomes, suggesting that resource - rich environments and top - down policy directives can drive rapid but uneven technology uptake. By consolidating key dimensions (Technical Infrastructure (TI), Organizational Readiness (O R), and Governance Environment (GE)) into a single composite index, this study provides a holistic yet context - sensitive tool for benchmarking AI maturity. The index offers actionable guidance for policymakers seeking to harmonize large - scale deployments w ith ethical and regulatory standards. Beyond advancing academic discourse, these insights inform more strategic allocation of resources, cross - country cooperation, and capacity - building initiatives, thereby supporting sustained AI - driven transformation in the GCC region and beyond.

Artificial intelligence (AI) is emerging as a foundational general-purpose technology, raising new dilemmas of sovereignty in an interconnected world. While governments seek greater control over it, the very foundations of AI--global data pipelines, semiconductor supply chains, open-source ecosystems, and international standards--resist enclosure. This paper develops a conceptual and formal framework for understanding sovereign AI as a continuum rather than a binary condition, balancing autonomy with interdependence. Drawing on classical theories, historical analogies, and contemporary debates on networked autonomy, we present a planner's model that identifies two policy heuristics: equalizing marginal returns across the four sovereignty pillars and setting openness where global benefits equal exposure risks. We apply the model to India, highlighting sovereign footholds in data, compute, and norms but weaker model autonomy. The near-term challenge is integration via coupled Data x Compute investment, lifecycle governance (ModelOps), and safeguarded procurement. We then apply the model to the Middle East (Saudi Arabia and the UAE), where large public investment in Arabic-first models and sovereign cloud implies high sovereignty weights, lower effective fiscal constraints, and strong Data x Compute complementarities. An interior openness setting with guardrails emerges as optimal. Across contexts, the lesson is that sovereignty in AI needs managed interdependence, not isolation.

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Saudi crown prince to visit U.S. with defense, AI and nuclear energy on agenda In his upcoming visit to the White House, the crown prince is seeking security guarantees and wants access to artificial intelligence technology and progress toward a deal on a civilian nuclear program. RIYADH/WASHINGTON - A visit by Saudi Arabia's de facto ruler to the White House for talks on Tuesday with U.S. President Donald Trump aims to deepen decades-old cooperation on oil and security while broadening ties in commerce, technology and potentially even nuclear energy. It will be the first trip by Crown Prince Mohammed bin Salman to the U.S. since the 2018 killing of Saudi critic Jamal Khashoggi by Saudi agents in Istanbul, which caused a global uproar. U.S. intelligence concluded that the crown prince approved the capture or killing of Khashoggi, a prominent critic. The crown prince, widely known by his initials MBS, denied ordering the operation but acknowledged responsibility as the kingdom's de facto ruler.

High-definition 3D city maps underpin smart transportation, digital twins, and autonomous driving, where object level change detection across bi temporal LiDAR enables HD map maintenance, construction monitoring, and reliable localization. Classical DSM differencing and image based methods are sensitive to small vertical bias, ground slope, and viewpoint mismatch and yield cellwise outputs without object identity. Point based neural models and voxel encodings demand large memory, assume near perfect pre alignment, degrade thin structures, and seldom enforce class consistent association, which leaves split or merge cases unresolved and ignores uncertainty. We propose an object centric, uncertainty aware pipeline for city scale LiDAR that aligns epochs with multi resolution NDT followed by point to plane ICP, normalizes height, and derives a per location level of detection from registration covariance and surface roughness to calibrate decisions and suppress spurious changes. Geometry only proxies seed cross epoch associations that are refined by semantic and instance segmentation and a class constrained bipartite assignment with augmented dummies to handle splits and merges while preserving per class counts. Tiled processing bounds memory without eroding narrow ground changes, and instance level decisions combine 3D overlap, normal direction displacement, and height and volume differences with a histogram distance, all gated by the local level of detection to remain stable under partial overlap and sampling variation. On 15 representative Subiaco blocks the method attains 95.2% accuracy, 90.4% mF1, and 82.6% mIoU, exceeding Triplet KPConv by 0.2 percentage points in accuracy, 0.2 in mF1, and 0.8 in mIoU, with the largest gain on Decreased where IoU reaches 74.8% and improves by 7.6 points.

Waste management is a critical global issue with significant environmental and public health implications. It has become more destructive during large-scale events such as the annual pilgrimage to Makkah, Saudi Arabia, one of the world's largest religious gatherings. This event's popularity has attracted millions worldwide, leading to significant and un-predictable accumulation of waste. Such a tremendous number of visitors leads to in-creased waste management issues at the Grand Mosque and other holy sites, highlighting the need for an effective solution other than traditional methods based on rigid collection schedules. To address this challenge, this research proposed an innovative solution that is context-specific and tailored to the unique requirements of pilgrimage season: a Smart Waste Management System, called TUHR, that utilizes the Internet of Things and Artificial Intelligence. This system encompasses ultrasonic sensors that monitor waste levels in each container at the performance sites. Once the container reaches full capacity, the sensor communicates with the microcontroller, which alerts the relevant authorities. Moreover, our system can detect harmful substances such as gas from the gas detector sensor. Such a proactive and dynamic approach promises to mitigate the environmental and health risks associated with waste accumulation and enhance the cleanliness of these sites. It also delivers economic benefits by reducing unnecessary gasoline consumption and optimizing waste management resources. Importantly, this research aligns with the principles of smart cities and exemplifies the innovative, sustainable, and health-conscious approach that Saudi Arabia is implementing as part of its Vision 2030 initiative.

The Esports World Cup 2025 at Boulevard City Arena in Riyadh on Aug. 2. Saudi Arabia is focusing on gaming as part of a national strategy to create tens of thousands of new jobs and diversify the kingdom's economy away from oil. Saudi Arabia is accelerating plans to transform itself into a hub for gamers with its blockbuster deal to take Electronic Arts private. In addition to an existing $5 billion equity stake it is rolling over into the new entity, the kingdom's Public Investment Fund is providing more fresh capital than partners Silver Lake Management and Jared Kushner's Affinity Partners to buy out the other public investors, according to people familiar with the matter. That's made it the largest contributor to the $36 billion in equity being put in to finance the deal, the people said, asking not to be identified discussing non-public information. In a time of both misinformation and too much information, quality journalism is more crucial than ever.

Electronic Arts (EA), one of the biggest gaming companies in the world, has agreed a deal to sell the company for $55bn (£41bn). The consortium of buyers include Saudi Arabia's Public Investment Fund (PIF), Silver Lake and Jared Kushner's Affinity Partners. EA is known for making and publishing best-selling games such as EA FC, formerly known as Fifa, The Sims and Mass Effect. It is understood to be the largest leveraged buyout in history - where a significant amount of the purchase is financed by borrowing money. The deal will take EA private - meaning all of its public shares will be purchased and it will no longer be traded on a stock exchange.

ArabJobs is a publicly available corpus of Arabic job advertisements collected from Egypt, Jordan, Saudi Arabia, and the United Arab Emirates. Comprising over 8,500 postings and more than 550,000 words, the dataset captures linguistic, regional, and socio-economic variation in the Arab labour market. We present analyses of gender representation and occupational structure, and highlight dialectal variation across ads, which offers opportunities for future research. We also demonstrate applications such as salary estimation and job category normalisation using large language models, alongside benchmark tasks for gender bias detection and profession classification. The findings show the utility of ArabJobs for fairness-aware Arabic NLP and labour market research. The dataset is publicly available on GitHub: https://github.com/drelhaj/ArabJobs.