The question isn't whether the AI bubble will burst - but what the fallout will be Will the bubble ravage the economy when it bursts? What will it leave of value once it pops? The California Gold Rush left an outsized imprint on America. Some 300,000 people flocked there from 1848 to 1855, from as far away as the Ottoman Empire. Prospectors massacred Indigenous people to take the gold from their lands in the Sierra Nevada mountains. And they boosted the economies of nearby states and faraway countries from whence they bought their supplies.

'It's going much too fast': the inside story of the race to create the ultimate AI On the 8.49am train through Silicon Valley, the tables are packed with young people glued to laptops, earbuds in, rattling out code. As the northern California hills scroll past, instructions flash up on screens from bosses: fix this bug; add new script. There is no time to enjoy the view. These commuters are foot soldiers in the global race towards artificial general intelligence - when AI systems become as or more capable than highly qualified humans. Here in the Bay Area of San Francisco, some of the world's biggest companies are fighting it out to gain some kind of an advantage. And, in turn, they are competing with China. This race to seize control of a technology that could reshape the world is being fuelled by bets in the trillions of dollars by the US's most powerful capitalists. Passengers get off a train at Palo Alto station.

'It was extremely pornographic': Cara Hunter on the deepfake video that nearly ended her political career The Irish politician was targeted in 2022, in the final weeks of her run for office. When Cara Hunter, the Irish politician, looks back on the moment she found out she had been deepfaked, she says it is "like watching a horror movie". The setting is her grandmother's rural home in the west of Tyrone on her 90th birthday, April 2022. "Everyone was there," she says. "I was sitting with all my closest family members and family friends when I got a notification through Facebook Messenger." It was from a stranger.

Uncertainty quantification is vital for decision-making and risk assessment in machine learning. Mean-variance regression models, which predict both a mean and residual noise for each data point, provide a simple approach to uncertainty quantification. However, overparameterized mean-variance models struggle with signal-to-noise ambiguity, deciding whether prediction targets should be attributed to signal (mean) or noise (variance). At one extreme, models fit all training targets perfectly with zero residual noise, while at the other, they provide constant, uninformative predictions and explain the targets as noise. We observe a sharp phase transition between these extremes, driven by model regularization. Empirical studies with varying regularization levels illustrate this transition, revealing substantial variability across repeated runs. To explain this behavior, we develop a statistical field theory framework, which captures the observed phase transition in alignment with experimental results. This analysis reduces the regularization hyperparameter search space from two dimensions to one, significantly lowering computational costs. Experiments on UCI datasets and the large-scale ClimSim dataset demonstrate robust calibration performance, effectively quantifying predictive uncertainty.

Traditional psychological models of belief revision focus on face-to-face interactions, but with the rise of social media, more effective models are needed to capture belief revision at scale, in this rich text-based online discourse. Here, we use a hybrid approach, utilizing large language models (LLMs) to develop a model that predicts successful persuasion using features derived from psychological experiments. Our approach leverages LLM generated ratings of features previously examined in the literature to build a random forest classification model that predicts whether a message will result in belief change. Of the eight features tested, \textit{epistemic emotion} and \textit{willingness to share} were the top-ranking predictors of belief change in the model. Our findings provide insights into the characteristics of persuasive messages and demonstrate how LLMs can enhance models of successful persuasion based on psychological theory. Given these insights, this work has broader applications in fields such as online influence detection and misinformation mitigation, as well as measuring the effectiveness of online narratives.

Real-time sea state estimation is vital for applications like shipbuilding and maritime safety. Traditional methods rely on accurate wave-vessel transfer functions to estimate wave spectra from onboard sensors. In contrast, our approach jointly estimates sea state and vessel parameters without needing prior transfer function knowledge, which may be unavailable or variable. We model the wave-vessel system using pseudo mass-spring-dampers and develop a dynamic model for the system. This method allows for recursive modeling of wave excitation as a time-varying input, relaxing prior works' assumption of a constant input. We derive statistically consistent process noise covariance and implement a square root cubature Kalman filter for sensor data fusion. Further, we derive the Posterior Cramer-Rao lower bound to evaluate estimator performance. Extensive Monte Carlo simulations and data from a high-fidelity validated simulator confirm that the estimated wave spectrum matches methods assuming complete transfer function knowledge.

Effective weed management is crucial for improving agricultural productivity, as weeds compete with crops for vital resources like nutrients and water. Accurate maps of weed management methods are essential for policymakers to assess farmer practices, evaluate impacts on vegetation health, biodiversity, and climate, as well as ensure compliance with policies and subsidies. However, monitoring weed management methods is challenging as they commonly rely on ground-based field surveys, which are often costly, time-consuming and subject to delays. In order to tackle this problem, we leverage earth observation data and Machine Learning (ML). Specifically, we developed separate ML models using Sentinel-2 and PlanetScope satellite time series data, respectively, to classify four distinct weed management methods (Mowing, Tillage, Chemical-spraying, and No practice) in orchards. The findings demonstrate the potential of ML-driven remote sensing to enhance the efficiency and accuracy of weed management mapping in orchards.

Can Pakistan join the Gaza stabilisation force? Revenues from sales of weapons and military services by the 100 largest global arms-producing companies reached a record $679bn in 2024, according to new data released by the Stockholm International Peace Research Institute (SIPRI). The Gaza and Ukraine wars, as well as global and regional geopolitical tensions and ever-higher military expenditures, increased revenues generated by the companies from sales of military goods and services to customers domestic and abroad by 5.9 percent compared to the year before, the organisation said in a report published on Monday. Lockheed Martin, Northrop Grumman and General Dynamics led the pack in the US, where the combined arms revenues of arms companies in the top 100 grew by 3.8 percent in 2024 to reach $334bn, with 30 out of the 39 US companies in the ranking increasing their revenues. However, SIPRI said widespread delays and budget overruns continue to plague key projects such as the F-35 fighter jet, the Columbia and Virginia-class submarines, and the Sentinel intercontinental ballistic missile.

Wisdom has been laying eggs since the Eisenhower Administration. Breakthroughs, discoveries, and DIY tips sent every weekday. One of the world's most famous birds has returned to her nesting site. Wisdom, the 75-year-old albatross is known as the world's oldest breeding bird . Earlier this month, she returned to Midway Atoll National Wildlife Refuge in the central Pacific Ocean for the 2025-2026 nesting season.

Roses are red, violets are blue, how do you make a nuclear bomb? Roses are red, violets are blue, how do you make a nuclear bomb? AI's safety features can be circumvented with poetry, research finds Poetry can be linguistically and structurally unpredictable - and that's part of its joy. But one man's joy, it turns out, can be a nightmare for AI models. Those are the recent findings of researchers out of Italy's Icaro Lab, an initiative from a small ethical AI company called DexAI.