Environment Animals Wildlife Endangered Species German hairy snails are disappearing from London's River Thames Londoners are scouring riverbanks to save the endangered mollusk. Breakthroughs, discoveries, and DIY tips sent every weekday. Researchers believe that its signature hairs help the strange creature live in its damp, riverside environments by enabling it to sweat off moisture. By wicking off that excess moisture, the slime gets more sticky, so the snail can hold onto the slick riverside debris and the plants it eats. However, the snail needs some extra support.

Hallucination, the generation of factually incorrect content, is a growing challenge in Large Language Models (LLMs). Existing detection and mitigation methods are often isolated and insufficient for domain-specific needs, lacking a standardized pipeline. This paper introduces THaMES (Tool for Hallucination Mitigations and EvaluationS), an integrated framework and library addressing this gap. THaMES offers an end-to-end solution for evaluating and mitigating hallucinations in LLMs, featuring automated test set generation, multifaceted benchmarking, and adaptable mitigation strategies. It automates test set creation from any corpus, ensuring high data quality, diversity, and cost-efficiency through techniques like batch processing, weighted sampling, and counterfactual validation. THaMES assesses a model's ability to detect and reduce hallucinations across various tasks, including text generation and binary classification, applying optimal mitigation strategies like In-Context Learning (ICL), Retrieval Augmented Generation (RAG), and Parameter-Efficient Fine-tuning (PEFT). Evaluations of state-of-the-art LLMs using a knowledge base of academic papers, political news, and Wikipedia reveal that commercial models like GPT-4o benefit more from RAG than ICL, while open-weight models like Llama-3.1-8B-Instruct and Mistral-Nemo gain more from ICL. Additionally, PEFT significantly enhances the performance of Llama-3.1-8B-Instruct in both evaluation tasks.

Data stream forecasts are essential inputs for decision making at digital platforms. Machine learning algorithms are appealing candidates to produce such forecasts. Yet, digital platforms require a large-scale forecast framework that can flexibly respond to sudden performance drops. Re-training ML algorithms at the same speed as new data batches enter is usually computationally too costly. On the other hand, infrequent re-training requires specifying the re-training frequency and typically comes with a severe cost of forecast deterioration. To ensure accurate and stable forecasts, we propose a simple data-driven monitoring procedure to answer the question when the ML algorithm should be re-trained. Instead of investigating instability of the data streams, we test if the incoming streaming forecast loss batch differs from a well-defined reference batch. Using a novel dataset constituting 15-min frequency data streams from an on-demand logistics platform operating in London, we apply the monitoring procedure to popular ML algorithms including random forest, XGBoost and lasso. We show that monitor-based re-training produces accurate forecasts compared to viable benchmarks while preserving computational feasibility. Moreover, the choice of monitoring procedure is more important than the choice of ML algorithm, thereby permitting practitioners to combine the proposed monitoring procedure with one's favorite forecasting algorithm.

Jack Shepherd had a polished seduction routine. He would take women out for expensive meals and thrilling rides on his speedboat. But one night his fixation on trying to impress went horribly wrong when he killed his date, Charlotte Brown. Shepherd met Charlotte - or Charli, as she was known - for the first time on a December night in 2015. Before that, they'd got to know each other online through the dating website OkCupid.

MICROSOFT HAS PATCHED a flaw in its Cortana virtual assistant that could enable hackers to bypass the lock screen on Windows 10 machines. The fix included in Microsoft's latest Patch Tuesday bug fix bundle, which comprises 12 updates intended to patch a total of 49 security vulnerabilities. This includes fixes for flaws in Windows, Office, SharePoint, and the Internet Explorer and Edge web browsers, along with a patch for the so-called'elevation of privilege vulnerability' in Microsoft's AI helper. Lane Thames, a senior security researcher at Tripwire, spoke out about the long-standing flaw with Cortana, that meant the AI helper was always listening for commands, even when a PC is locked. "The advisory states that'Cortana retrieves data from user input services without consideration for status'," said Thames.

These are just a few of the latest military and security innovations from around the world on offer at the Defence and Security Equipment International Show (DSEI) in the U.K. this week. DSEI runs from Sept. 10 through Sept 15 at the Excel Center in London. The gigantic scale of biennial DSEI is often described as unrivalled. If you are a country looking to upgrade your military might then this "one stop shop" is the place to be. Pretty much anything you would need to defend your country in war – or to launch a war for that matter - is here in London at largest show of its kind on Earth.