Silicon Valley's battle against AI regulation is already shaping the next US election cycle. Silicon Valley is already pouring tens of millions of dollars into the midterm elections taking place across the US in 2026, as the tech industry's war over AI regulation moves decisively into American politics. Technology executives, investors, and companies tied to the AI boom are funding a new network of AI-focused super PACS, which is poised to make AI a major issue in this year's state and federal elections races. The election spending marks a sharp escalation of the AI regulation debate that has divided Silicon Valley for years. In the absence of federal action, state lawmakers in New York, California, and Colorado have passed laws in the past year requiring large AI developers to disclose safety practices and assess risks such as algorithmic discrimination.

While AI is all over the stages, Trump is dominating the side conversations. I've been here for two days now, attending meetings, speaking on panels, and basically trying to talk to anyone I can. And as far as I can tell, the only things anyone wants to talk about are AI and Trump. Davos is physically defined by the Congress Center, where the official WEF sessions take place, and the Promenade, a street running through the center of the town lined with various "houses"--mostly retailers that are temporarily converted into meeting hubs for various corporate or national sponsors. So there is a Ukraine House, a Brazil House, Saudi House, and yes, a USA House (more on that tomorrow). There are a handful of media houses from the likes of CNBC and the .

The trial starts soon in New Mexico's case against Meta--and the company is pulling out all the stops to protect its reputation. As Meta heads to trial in the state of New Mexico for allegedly failing to protect minors from sexual exploitation, the company is making an aggressive push to have certain information excluded from the court proceedings. The company has petitioned the judge to exclude certain research studies and articles around social media and youth mental health; any mention of a recent high-profile case involving teen suicide and social media content; and any references to Meta's financial resources, the personal activities of employees, and Mark Zuckerberg's time as a student at Harvard University. Meta's requests to exclude information, known as motions in limine, are a standard part of pretrial proceedings, in which a party can ask a judge to determine in advance which evidence or arguments are permissible in court. This is to ensure the jury is presented with facts and not irrelevant or prejudicial information and that the defendant is granted a fair trial.

River of waste'visible for miles' dumped at mountain beauty spot A farmer says she is devastated by a disgusting river of fly-tipped waste dumped down the side of a mountain. Katie Davies, whose family has owned land on Bwlch Mountain in Treorchy for 90 years, said the clean up could cost thousands of pounds and could also harm her sheep which graze on the land. Travel blogger Nathan Dixon, who captured drone footage showing the scale of the discarded waste, said the mess could be seen from three to five miles away, adding that it sticks out like a sore thumb. Rhondda Cynon Taf council said it always took action to hold those responsible for fly-tipping to account, while Natural Resources Wales (NRW) said fly-tipping was a serious crime. Davies, who runs small family business Nantymoel farm which produces Welsh beef and lamb, said the mess keeps me up at night.

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Could Ukraine hold a presidential election right now? Will Europe use frozen Russian assets to fund war? How can Ukraine rebuild China ties? 'Ukraine is running out of men, money and time' At least three people have been reported killed after Russian forces struck the southeastern Ukrainian city of Zaporizhzhia, Governor Ivan Fedorov announced on the Telegram messaging app. Russian strikes also destroyed several private houses and cars, and left nearly 1,500 households without electricity, the governor said.

All UK children could be offered screening for type 1 diabetes using a simple finger-prick blood test, say researchers who have been running a large study. Currently, many young people go undiagnosed and risk developing a life-threatening complication called diabetic ketoacidosis that needs urgent hospital treatment. Identifying diabetes earlier could help avoid this and mean treatments to control problematic blood sugar levels can be given sooner. Some 17,000 children aged three to 13 have already been checked as part of the ELSA (Early Surveillance for Autoimmune diabetes) study, funded by diabetes charities. Imogen, who is 12 and from the West Midlands, is one of those found to have diabetes thanks to the screening.

Physics-Informed Neural Networks (PINNs) have emerged as a powerful tool for solving differential equations and modeling physical systems by embedding physical laws into the learning process. However, rigorously quantifying how well a PINN captures the complete dynamical behavior of the system, beyond simple trajectory prediction, remains a challenge. This paper proposes a novel experimental framework to address this by employing Fisher information for differentiable dynamical systems, denoted $g_F^C$. This Fisher information, distinct from its statistical counterpart, measures inherent uncertainties in deterministic systems, such as sensitivity to initial conditions, and is related to the phase space curvature and the net stretching action of the state space evolution. We hypothesize that if a PINN accurately learns the underlying dynamics of a physical system, then the Fisher information landscape derived from the PINN's learned equations of motion will closely match that of the original analytical model. This match would signify that the PINN has achieved comprehensive fidelity capturing not only the state evolution but also crucial geometric and stability properties. We outline an experimental methodology using the dynamical model of a car to compute and compare $g_F^C$ for both the analytical model and a trained PINN. The comparison, based on the Jacobians of the respective system dynamics, provides a quantitative measure of the PINN's fidelity in representing the system's intricate dynamical characteristics.

Outlier detection (OD) aims to identify abnormal instances, known as outliers or anomalies, by learning typical patterns of normal data, or inliers. Performing OD under an unsupervised regime-without any information about anomalous instances in the training data-is challenging. A recently observed phenomenon, known as the inlier-memorization (IM) effect, where deep generative models (DGMs) tend to memorize inlier patterns during early training, provides a promising signal for distinguishing outliers. However, existing unsupervised approaches that rely solely on the IM effect still struggle when inliers and outliers are not well-separated or when outliers form dense clusters. To address these limitations, we incorporate active learning to selectively acquire informative labels, and propose IMBoost, a novel framework that explicitly reinforces the IM effect to improve outlier detection. Our method consists of two stages: 1) a warm-up phase that induces and promotes the IM effect, and 2) a polarization phase in which actively queried samples are used to maximize the discrepancy between inlier and outlier scores. In particular, we propose a novel query strategy and tailored loss function in the polarization phase to effectively identify informative samples and fully leverage the limited labeling budget. We provide a theoretical analysis showing that the IMBoost consistently decreases inlier risk while increasing outlier risk throughout training, thereby amplifying their separation. Extensive experiments on diverse benchmark datasets demonstrate that IMBoost not only significantly outperforms state-of-the-art active OD methods but also requires substantially less computational cost.

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'.