Predicting a complete spatially correlated field from sparse observations is a fundamental challenge in spatial statistics and environmental modelling. Classical interpolation methods such as Kriging rely on Gaussian process assumptions and variography, which can limit their effectiveness in non-stationary settings and require substantial domain expertise. In this work, we leverage an architecture based on convolutional neural networks (CNNs) for spatial interpolation that is trained and applied on a single partially observed field, without access to external data or prior fields. The model is supervised directly on the observed locations and learns to predict values at unobserved points on the user defined grid. Unlike Kriging, our method does not require explicit covariance modelling or variogram estimation, and it can flexibly capture local spatial patterns in a data-driven manner. This work demonstrates the potential of CNNs for single-instance spatial interpolation under sparse supervision, offering a practical alternative to classical geostatistical methods, and extending the use of CNNs to a new problem domain.

Reliable probability estimates are critical in many machine learning applications, yet modern classifiers are often poorly calibrated. Post-hoc calibration provides a simple and widely used solution, but the large number of proposed methods, combined with small-scale and inconsistent evaluations, makes it difficult to determine which approaches are truly effective in practice. We introduce a large-scale, standardized benchmark for post-hoc calibration, covering nearly 2000 experiments across tabular and computer vision tasks, including binary, multiclass, and large-scale classification settings. Our benchmark aggregates predictions from a diverse set of classical models, modern deep learning architectures, and foundation models, and provides unified, reproducible implementations of dozens of calibration methods within a common evaluation framework. We argue that Post-Hoc Improvement (PHI) in proper scoring rules offers a principled alternative to traditional calibration error estimators for comparing post-hoc methods, capturing both calibration quality and potential degradation to the model's predictive performance. Using this framework, we conduct the most comprehensive empirical study of post-hoc calibration to date. Our results reveal consistent patterns across domains: smooth calibration functions outperform binning-based approaches, dedicated multiclass methods are essential in high-dimensional settings, and generic machine learning models are not competitive without calibration-specific design. To facilitate future research, we release all data, code, and evaluation tools, providing a plug-and-play benchmark for developing and comparing calibration methods.

Numeric tabular datasets are the dominant data format in scientific practice, yet large language models lack native mechanisms for representing numeric datasets in a meaningful way across heterogeneous feature spaces. Existing approaches either target predictive modeling over individual datasets, which requires a shared set of variable definitions, or lack mechanisms for interpretable cross-dataset alignment. The proposed methodology characterizes numeric tabular datasets through structured exploratory data analysis descriptors, embeds those descriptors into a shared vector space using a pretrained sentence transformer, and quantifies cross-dataset similarity via Canonical Correlation Analysis (CCA). Furthermore, a penalized formulation of CCA is applied to recover sparse, interpretable variable-level correspondences between datasets, identifying which statistical descriptors or variable-level quantities drive cross-dataset alignment without requiring shared variable names or feature conventions. Differential privacy is optionally applied to the descriptor set prior to embedding, supporting deployment in sensitive data contexts without requiring access to raw observations at time of comparison. The methodology is evaluated across 15 datasets spanning general-purpose benchmarks, materials informatics, and nuclear-grade graphite characterization. Results demonstrate a total P@1 score of 0.9, with known nearest-neighbor retrieval and cluster structure remaining robust across embedding ablations and differential privacy budgets. The proposed framework provides a principled pathway for integrating heterogeneous numeric data into retrieval-augmented generation pipelines while preserving statistical context, with direct applications to data-driven algorithm selection and simulation model initialization for unknown datasets.

The AI assistant had its personality stripped in pursuit of a more consistent experience. Copilot is getting yet another visual overhaul as Microsoft reconsiders its approach to AI across Windows and its various apps. The new changes are focused on the version of Copilot accessible in Microsoft 365, and visually streamline the AI assistant to using it more consistent across apps like Word, PowerPoint and Excel. The most striking difference in Copilot's new look is how little color it has. You can still get Copilot to produce full-color outputs and it will reference other apps by their colorful app icons.

Pages from the Anthropic website and the company's logo are displayed on a computer screen in New York on 26 February 2026. Pages from the Anthropic website and the company's logo are displayed on a computer screen in New York on 26 February 2026. Anthropic reaches valuation of $965bn, beating OpenAI to become world's most valuable AI firm Claude's parent company's $65bn in latest funding round underscores vast sums of money still flowing into industry Anthropic, the AI firm behind the Claude chatbot, announced on Thursday it had raised $65bn in funding to value the company at $965bn post-money. The move makes Anthropic the world's most valuable AI startup, eclipsing its competitor OpenAI. The deal marks an exceedingly successful period of growth for Anthropic, which was once considered to be a smaller player in the global AI arms race.

LinkerBot makes dexterous robotic hands for as little as $600. It wants to become the standard for humanoids and automated factories--and eventually replace human labor altogether. If you could buy a humanoid robot for less than a smartphone, would you? Would you buy several robots to handle cooking, cleaning, babysitting, and even your job? This is the pitch being made by Zhou Yong, the 40-year-old founder and chief technology officer of LinkerBot, one of China's leading manufacturers of dexterous humanoid hands.

The complaint, filed on Thursday, said that Perplexity unlawfully copied thousands of CNN stories, videos and images to power its products and distribute "identical or substantially similar" competing content. CNN is asking for an unspecified amount of monetary damages and a court order blocking Perplexity from violating its intellectual property rights. "CNN's lawsuit stands for the proposition that Perplexity, a company valued at tens of billions of dollars, should not be able to steal from entities that create the original content Perplexity exploits," the Warner Bros-owned news company said in a statement. Anthropic was the first AI company to settle one of these cases last year, agreeing to pay $1.5bn to resolve a class action lawsuit from a group of authors. Perplexity is also facing lawsuits from The New York Times, Reddit and Dow Jones, among others.

The real image, which the police station has since shared, shows the officers in normal clothes and no female officer in the picture at all. The real image, which the police station has since shared, shows the officers in normal clothes and no female officer in the picture at all. Picture was created by administrator in charge of station's Facebook account who wanted to create'friendlier image' It was an arresting image and an irresistible story. A group of tough Thai police officers - five men and one woman - all wearing elaborate festival-style dresses, surrounding a drug dealer they had caught while undercover. The image, released by local police, was so compelling that it found its way on to the front page of the UK's Daily Star, as well as in picture stories in the Telegraph, the Sun and the New York Post. The Sun wrote: "The burly crew of five men and one woman slipped into skin tight sequins and feathers for the covert mission in Thailand ."

When most of us think of AI chatbots, we think of complex systems running on powerful hardware in massive data centers. Ask ChatGPT or Gemini a question, then watch it think as it pings some faraway server network to process, before it generates an answer. The reality is that's just one way to interact with the latest AI models, and you can run an open-weight chatbots on a recent iPhone. A local chatbot might not be as powerful as its cloud counterparts, but there are compelling reasons to ditch ChatGPT, Claude and Gemini, which I'll go over in this guide. I'll also explain how to install a local AI model on your phone. It might seem complicated, but I promise it's easier than you think.

Datacentre industry representatives disputed the findings and said the sector boosted the economy. Datacentre industry representatives disputed the findings and said the sector boosted the economy. 'Hidden datacentre tax' costing Irish households millions, report says Datacentres used 22% of country's electricity last year, pushing up household bills, study suggests Thu 28 May 2026 09.01 EDTLast modified on Thu 28 May 2026 09.32 EDT Energy demand by datacentres in Ireland has added hundreds of euros to household electricity bills in a pattern that could be replicated across Europe, according to a report. Ireland's growing number of datacentres last year used 22% of the country's electricity, more than all urban homes combined, according to the Central Statistics Office. The equivalent figure in the US and UK is 6%.