America's Biggest Bitcoin Miners Are Pivoting to AI In the face of a profitability crisis, industrial-scale bitcoin miners are transforming their data centers into AI factories. One afternoon in June 2024, I stood up against the fence of a sprawling industrial facility a few miles outside of Corsicana, Texas. Over a metal gate, I watched a bright yellow excavator claw at the dirt and flatbed trucks shuttle to and fro. A hangar-like structure with a gleaming white roof stretched hundreds of meters along the opposite perimeter. The company that owned the plot, Riot Platforms, was busily constructing the world's largest bitcoin mine. A year and a half later, a projected two-thirds of the facility is being repurposed to accommodate AI and high-performance computing (HPC) tasks.

Ben & Jerry's brand could be destroyed, says co-founder Ben & Jerry's will be destroyed as a brand if it remains with parent company Magnum, the company's co-founder Ben Cohen has told the BBC. His remarks are the latest in a long-running spat between the ice cream brand and its parent company over its ability to express its social activism and the continued independence of its board. The comments came on the day that the Magnum Ice Cream Company (TMICC) started trading on the European stock market - spinning off from owner Unilever. A spokesperson for Magnum said the firm wanted to build and strengthen Ben & Jerry's powerful, non-partisan values-based position in the world. Ben & Jerry's was sold to Unilever in 2000 in a deal which allowed it to retain an independent board and the right to make decisions about its social mission.

Rachael Dunfell knew two things about her husband's 21-year-old cousin: that he liked specialised racing bikes and that he was interested in the Vikings. But those pieces of information yielded few ideas for a suitable Christmas gift. So Rachael, 33, from Manchester, turned to artificial intelligence. She inputted his age, his hobby and his interest into Copilot, the Microsoft-owned chatbot, which led her to the website of a niche retailer that sells Viking-themed metal bike parts. It's just something that I really would never have known existed, she said, but it was perfect.

Ukraine is preparing to present a revised peace plan to the White House, as it seeks to avoid making territorial concessions to Russia. Kyiv is set propose alternatives to the US after President Volodymyr Zelensky again ruled out surrendering land, saying he had no right to do so under Ukrainian or international law. He made the comments as he met European and Nato leaders on Monday, part of a collective push to deter the US from backing a peace deal which includes major concessions for Ukraine, and which allies fear would leave it vulnerable to a future invasion. Meanwhile, the city of Sumy in north-western Ukraine was left without power overnight after a Russian drone attack. The region's governor said more than a dozen drones had hit power infrastructure, the latest in Russia's nightly attacks.

What are the implications of Trump's Somali'garbage' comments? What happens if the US attacks Venezuela? Does'America First' make the US weaker? What we know about the DC pipe bomb suspect Brian Cole Jr. US President Donald Trump has cleared the way for tech giant Nvidia to sell its advanced H200 chip to China, in a significant easing of Washington's export controls targeting Chinese tech. Trump said on Monday that he had informed Chinese President Xi Jinping of the decision to allow the export of the chip under an arrangement that will see 25 percent of sales paid to the US government.

US President Donald Trump has announced that he will allow AI chip giant Nvidia to sell its advanced H200 chips to approved customers in China. We will protect National Security, create American Jobs, and keep America's lead in AI, Trump said on social media on Monday. The decision will apply to other US chip companies like AMD and comes after extensive lobbying by Nvidia boss Jensen Huang, who visited Washington last week to drum up support. Nvidia - both the world's leading chip firm and most valuable company - has found itself at the centre of a geopolitical tug-of-war between the US and China in recent months, and had been banned from selling its most advanced chips to Beijing. Trump reversed the chip-selling ban in July, but demanded that Nvidia pay 15% of its Chinese revenues to the US government. Beijing then reportedly ordered its tech companies to stop buying Nvidia chips manufactured for use in the Chinese market.

Sudan's air force has carried out bombings in which at least 1,700 civilians have died in attacks on residential neighbourhoods, markets, schools and camps for displaced people, according to an investigation into air raids in the country's civil war. The Sudan Witness Project says it has compiled the largest known dataset of military airstrikes in the conflict, which began in April 2023. Its analysis indicates that the air force has used unguided bombs in populated areas. The data focuses on attacks by warplanes, which only the Sudanese Armed Forces (SAF) is capable of operating. Its rival, the paramilitary Rapid Support Forces (RSF) does not have aircraft.

Decision making often occurs in the presence of incomplete information, leading to the under- or overestimation of risk. Leveraging the observable information to learn the complete information is called nowcasting. In practice, incomplete information is often a consequence of reporting or observation delays. In this paper, we propose an expectation-maximisation (EM) framework for nowcasting that uses machine learning techniques to model both the occurrence as well as the reporting process of events. We allow for the inclusion of covariate information specific to the occurrence and reporting periods as well as characteristics related to the entity for which events occurred. We demonstrate how the maximisation step and the information flow between EM iterations can be tailored to leverage the predictive power of neural networks and (extreme) gradient boosting machines (XGBoost). With simulation experiments, we show that we can effectively model both the occurrence and reporting of events when dealing with high-dimensional covariate information. In the presence of non-linear effects, we show that our methodology outperforms existing EM-based nowcasting frameworks that use generalised linear models in the maximisation step. Finally, we apply the framework to the reporting of Argentinian Covid-19 cases, where the XGBoost-based approach again is most performant.

Bug localization remains a critical yet time-consuming challenge in large-scale software repositories. Traditional information retrieval-based bug localization (IRBL) methods rely on unchanged bug descriptions, which often contain noisy information, leading to poor retrieval accuracy. Recent advances in large language models (LLMs) have improved bug localization through query reformulation, yet the effect on agent performance remains unexplored. In this study, we investigate how an LLM-powered agent can improve file-level bug localization via lightweight query reformulation and summarization. We first employ an open-source, non-fine-tuned LLM to extract key information from bug reports, such as identifiers and code snippets, and reformulate queries pre-retrieval. Our agent then orchestrates BM25 retrieval using these preprocessed queries, automating localization workflow at scale. Using the best-performing query reformulation technique, our agent achieves 35% better ranking in first-file retrieval than our BM25 baseline and up to +22% file retrieval performance over SWE-agent.

Detecting the anomalies of web applications, important infrastructures for running modern companies and governments, is crucial for providing reliable web services. Many modern web applications operate on web APIs (e.g., RESTful, SOAP, and WebSockets), their exposure invites intended attacks or unintended illegal visits, causing abnormal system behaviors. However, such anomalies can share very similar logs with normal logs, missing crucial information (which could be in database) for log discrimination. Further, log instances can be also noisy, which can further mislead the state-of-the-art log learning solutions to learn spurious correlation, resulting superficial models and rules for anomaly detection. In this work, we propose MINES which infers explainable API invariants for anomaly detection from the schema level instead of detailed raw log instances, which can (1) significantly discriminate noise in logs to identify precise normalities and (2) detect abnormal behaviors beyond the instrumented logs. Technically, MINES (1) converts API signatures into table schema to enhance the original database shema; and (2) infers the potential database constraints on the enhanced database schema to capture the potential relationships between APIs and database tables. MINES uses LLM for extracting potential relationship based on two given table structures; and use normal log instances to reject and accept LLM-generated invariants. Finally, MINES translates the inferred constraints into invariants to generate Python code for verifying the runtime logs. We extensively evaluate MINES on web-tamper attacks on the benchmarks of TrainTicket, NiceFish, Gitea, Mastodon, and NextCloud against baselines such as LogRobust, LogFormer, and WebNorm. The results show that MINES achieves high recall for the anomalies while introducing almost zero false positives, indicating a new state-of-the-art.