Elon Musk has suggested he could buy Ryanair and called for its chief executive to be fired amid a deepening spat between the pair. The budget airline on Tuesday branded the Tesla chief executive an idiot, and used the extraordinary row to promote its January sale. Musk and Ryanair boss Michael O'Leary have been trading insults over the past week after O'Leary rejected the idea of using Musk's Starlink technology to provide wi-fi on flights. The two are among the world's most outspoken business chiefs, with Musk the world's richest man with an estimated net worth of $769bn (£573bn), and O'Leary running Europe's busiest airline. A statement on Ryanair's X account on Tuesday evening said: Perhaps Musk needs a break?? Ryanair is launching a Great Idiots seat sale especially for Elon and any other idiots on'X'.

Every fitness company has jumped on the AI-powered fitness subscription bandwagon. All products featured on WIRED are independently selected by our editors. However, we may receive compensation from retailers and/or from purchases of products through these links. You've just spent hundreds of dollars on a new Garmin Fenix 8 or Forerunner 970, only to find out you might have to hand over even more money to get the full Garmin experience . Earlier this year, Garmin introduced Connect+, a subscription element to its Connect companion app earlier.

Concept-based models are an emerging paradigm in deep learning that constrains the inference process to operate through human-interpretable concepts, facilitating explainability and human interaction. However, these architectures, on par with popular opaque neural models, fail to account for the true causal mechanisms underlying the target phenomena represented in the data. This hampers their ability to support causal reasoning tasks, limits out-of-distribution generalization, and hinders the implementation of fairness constraints. To overcome these issues, we propose \emph{Causally reliable Concept Bottleneck Models} (C$^2$BMs), a class of concept-based architectures that enforce reasoning through a bottleneck of concepts structured according to a model of the real-world causal mechanisms. We also introduce a pipeline to automatically learn this structure from observational data and \emph{unstructured} background knowledge (e.g., scientific literature). Experimental evidence suggest that C$^2$BM are more interpretable, causally reliable, and improve responsiveness to interventions w.r.t. standard opaque and concept-based models, while maintaining their accuracy.

Understanding the effects of economic shocks on firms is critical for analyzing economic growth and resilience. We introduce a Web-Based Affectedness Indicator (W AI), a general-purpose tool for real-time monitoring of economic disruptions across diverse contexts. By leveraging Large Language Model (LLM) assisted classification and information extraction on texts from over five million company websites, W AI quantifies the degree and nature of firms' responses to external shocks. Using the COVID-19 pandemic as a specific application, we show that W AI is highly correlated with pandemic containment measures and reliably predicts firm performance. Unlike traditional data sources, W AI provides timely firm-level information across industries and geographies worldwide that would otherwise be unavailable due to institutional and data availability constraints. This methodology offers significant potential for monitoring and mitigating the impact of technological, political, financial, health or environmental crises, and represents a transformative tool for adaptive policy-making and economic resilience. Economic shocks, whether driven by public health crises, technological disruptions, geopolitical conflicts, or climate events, pose significant challenges to businesses and policymakers alike. Timely and accurate monitoring of these shocks is critical for crafting effective responses and enhancing economic resilience. However, traditional methods for measuring the impacts of such disruptions - such as surveys and administrative data - are often limited by costs, time lags, and coverage. In this study, we introduce the Web-Based Affectedness Indicator (W AI), a scalable and cost-effective tool for real-time monitoring of economic disruptions at the firm level. By analyzing textual data from millions of company websites, W AI provides granular insights into how firms experience and respond to external shocks. This 1 methodology overcomes traditional limitations by leveraging ubiquitous online content and state-of-the-art natural language processing (NLP) models to generate a dynamic and comprehensive view of economic affectedness. W AI can provide information on a wide range of challenges, including supply chain disruptions, financial crises, and climate-related shocks.