Pearson's Workera deal targets a big workplace problem: No time to AI upskill Pearson has agreed to acquire Workera to help businesses assess employees' AI skills and target training where it's needed most. Kayla Solino is a ZDNET Editor based in New York City and New Jersey. Pearson will acquire the AI skills platform Workera in H2 2026. A recent Workera report in ZDNET indicated gaps in AI upskilling. Together, the firms hope to develop the future of AI skills training.

Pentagon rolls out new Joint Warfighting Evaluation requiring senior officers to pass wargames and combat assessments before general officer promotion boards.

Public disagreement about whether political violence is rising and who is responsible for it stems not from ideology alone but from competing definitions about what should be counted. It could have been a scene from: a quiet evening, an ordinary home, a sudden intrusion by a stranger convinced that the law has betrayed him. In early July 2022, Democratic representative Pramila Jayapal of Washington and her husband were watching television at their Seattle home when men drove past their house screaming threats: vile, racialized, and unmistakably political. Thirty minutes later, one returned, blasting music and approaching the porch. When officers arrived, they found him armed with a loaded handgun, a round already chambered.

Wed 16 Sep 2026 07.55 EDTLast modified on Wed 16 Sep 2026 07.56 EDT Large-scale datacentres will face mandatory environmental assessments before they can go ahead, the Scottish government has announced, amid growing public concern about the impact of the AI boom. Ahead of a vote on a moratorium on new hyperscale datacentres in Holyrood on Wednesday, the Scottish government has tightened rules for new projects by requiring any above 50MW to submit environmental impact assessments, but stopped short of agreeing to an outright halt. Scotland has seen a boom in planning applications for large datacentres as global investment in AI infrastructure has exploded. Datacentres are critical for storing and processing data used by AI. One scheme in Auchtertool, Fife, has been billed as the second-largest in the world, and is among more than 20 large datacentres proposed so far.

The Falcon 9 upper stage created a 60-foot-wide crater on the moon on August 5. 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 crater image is enlarged three times from the original, with north facing up, and it covers an area about a quarter of a mile wide. Breakthroughs, discoveries, and DIY tips sent six days a week. By signing up, you confirm you are 16+, will receive newsletters and promotional content and agree to our Terms of Use and acknowledge the data practices in our Privacy Policy .

Scientists are expected to read newly published papers in their field to stay current and keep their work relevant. However, when faced with the massive number of publications, it may seem an overwhelming task to read all these papers, even if one were to reduce this to only a fraction related to one's own area of research. As an example, in 2024 alone, IEEE published no less than 46,968 papers on "robotics" or "automation", and IEEE publications represent only a fraction of the total research available online To assess the magnitude of this challenge, as well as to evaluate how much genuine progress is reported in today's publications, we undertook exactly this effort. For the task to be reasonable, we reduced our search to one particular subarea, learning from demonstration (LfD), that is methods whereby robots are taught by human experts. We monitor progress through both quantitative and qualitative metrics, offering a review on current trends and notable contributions.

The maps and images show the extent of destruction and give rescue operations a tool to find any remaining survivors. A satellite image from Vantor shows collapsed apartment buildings and widespread damage caused by the earthquake in the Playa Grande neighborhood of La Guaira. Satellite Technology Is being used to streamline rescue efforts in Venezuela following the two earthquakes that struck on June 24. Space agencies have shared images with emergency authorities and the Venezuelan government that not only reveal the magnitude of the disaster but also allow response teams to identify where to focus their efforts--and the challenges on the ground. Following the twin earthquakes in Venezuela, the Copernicus satellite system activated its emergency mapping mode at the request of the European Commission's Directorate-General for Civil Protection and Humanitarian Aid Operations.

Although numerous Artificial Intelligence Generated Image (AIGI) detectors have been proposed, often reporting high accuracy, their effectiveness in real-world scenarios remains questionable. To bridge this gap, we introduce AIGIBench, a comprehensive benchmark designed to rigorously evaluate the robustness and generalization capabilities of state-of-the-art AIGI detectors. AIGIBench simulates real-world challenges through four core tasks: multi-source generalization, robustness to image degradation, sensitivity to data augmentation, and impact of test-time preprocessing. It includes 23 diverse fake image subsets that span both advanced and widely adopted image generation techniques, along with real-world samples collected from social media and AI art platforms. Extensive experiments on 11 advanced detectors demonstrate that, despite their high reported accuracy in controlled settings, these detectors suffer significant performance drops on real-world data, limited benefits from common augmentations, and nuanced effects of preprocessing, highlighting the need for more robust detection strategies. By providing a unified and realistic evaluation framework, AIGIBench offers valuable insights to guide future research toward dependable and generalizable AIGI detection2.

Skill assessment from video entails rating the quality of a person's physical performance and explaining what could be done better. Today's models specialize for an individual sport, and suffer from the high cost and scarcity of expert-level supervision across the long tail of sports. Towards closing that gap, we explore transferable video representations for skill assessment. Our CROSSTRAINER approach discovers skill-attributes--such as balance, control, and hand positioning--whose meaning transcends the boundaries of any given sport, then trains a multimodal language model to generate actionable feedback for a novel video, e.g., "lift hands more to generate more power" as well as its proficiency level, e.g., early expert. We validate the new model on multiple datasets for both cross-sport (transfer) and intra-sport (in-domain) settings, where it achieves gains up to 60% relative to the state of the art. By abstracting out the shared behaviors indicative of human skill, the proposed video representation generalizes substantially better than an array of existing techniques, enriching today's multimodal large language models.

Diverse decoding of large language models is crucial for applications requiring multiple semantically distinct responses, yet existing methods primarily achieve lexical rather than semantic diversity. This limitation significantly constrains Bestof-N strategies, group-based reinforcement learning, and data synthesis. While temperature sampling and diverse beam search modify token distributions or apply n-gram penalties, they fail to ensure meaningful semantic differentiation. We introduce Semantic-guided Diverse Decoding (SemDiD), operating directly in embedding space that balances quality with diversity through three complementary mechanisms: orthogonal directional guidance, dynamic inter-group repulsion, and position-debiased probability assessment.