This story appears in the June 2026 print edition. While some stories from this issue are not yet available to read online, you can explore more from the magazine . Get our editors' guide to what matters in the world, delivered to your inbox every weekday. The courtship between Silicon Valley and MAGA was consummated on June 6, 2024, in San Francisco's Pacific Heights neighborhood, on a street known as "Billionaires' Row," at the 22,000-square-foot, $45 million French-limestone mansion of a venture capitalist named David Sacks. Along with Chamath Palihapitiya, a fellow venture capitalist and a colleague on the podcast, Sacks hosted a fundraiser for Donald Trump. He knew that other technology titans were coming around to the ex-president but remained in the closet. "And I think that this event is going to break the ice on that," Sacks said on the podcast the week before the fundraiser. "And maybe it'll create a preference cascade, where all of a sudden it becomes acceptable to acknowledge the truth." Check out more from this issue and find your next story to read. A few years earlier, Sacks had described the January 6, 2021, riot at the U.S. Capitol as an "insurrection" and pronounced Trump "disqualified" from ever again holding national office. "What Trump did was absolutely outrageous, and I think it brought him to an ignominious end in American politics," he said on the podcast a few days after the event. "He will pay for it in the history books, if not in a court of law." Palihapitiya was more colloquial, calling Trump "a complete piece-of-shit fucking scumbag." These might seem like tricky positions to climb down from--but the path that leads from scathing denunciation through gradual accommodation to sycophantic embrace of Trump is a well-worn pilgrimage trail. The journey is less wearisome for self-mortifiers who never considered democracy (a word seldom spoken on the podcast) all that important in the first place.

A Ukrainian drone hit an upmarket residential high-rise in Moscow in the early hours of Monday, resulting in no casualties but causing visible damage to the façade of the building. It was the third night in a row that the Russian capital came under attack from drones, days before Russia holds a scaled-back 9 May parade to mark the Soviet Union's victory over Nazi Germany. An unverified video circulating on social media showed firemen entering a heavily damaged flat covered in dust and rubble and with blown-out windows, while another showed drone debris strewn across the street below. Two other drones were intercepted, Mayor Sergei Sobyanin said. Vnukovo and Domodedovo international airports suspended operations overnight.

Dating Is a Rich Person's Game Now People actually can't afford to date anymore. Ask just about anyone what's wrong with modern dating and they will likely tell you the same thing: The apps suck. They're built on a pay-to-win model. Fewer people are finding quality partners. Some studies have even suggested that increased time on them leads to higher depression and anxiety while also contributing to loneliness among men .

When you purchase through links in our articles, we may earn a small commission. Discover MSI's 2026 AI-powered laptops, including the Prestige 16 Flip AI+, Stealth 16 AI+, and Raider 16 MAX HX - designed for productivity, portability, and high-performance gaming. CES 2026 highlighted a major shift in computing: AI-powered PCs designed for everyday users. These new AI laptops and AI PCs combine dedicated AI acceleration, powerful GPUs, and modern processors to support everything from productivity to gaming. Companies like MSI are now building AI capabilities directly into their latest laptops, making advanced features like AI-assisted productivity, content creation, and gaming performance available across multiple device categories.

The new'AI-powered' healthcare system appears to penalise the poorest. The new'AI-powered' healthcare system appears to penalise the poorest. An AI system used to predict how much Kenyans can afford to pay for access to healthcare, has systemically driven up costs for the poor, an investigation has found. The healthcare system being rolled out across the country, a key electoral promise of President William Ruto, was launched in October 2024 and intended to replace Kenya's decades-old national insurance system. Billed as " accelerating digital transformation ", it aimed to expand access to care to Kenya's large informal economy: the day labourers, hawkers, farmers and non-salaried workers that make up 83% of its workforce.

A Maholo humanoid robot carries out a series of tasks at the Institute of Science Tokyo's Robotics Innovation Center, during the center's opening last month. Artificial intelligence is transforming the way we work across industries. Two recent developments in Japan show how technology could help the nation cope with a shortage of talent in the fields of science and medical research. Some researchers have launched an effort to deploy AI-powered robots to carry out complex wet-lab experiments, which could free staff from time-consuming, repetitive work. In a time of both misinformation and too much information, quality journalism is more crucial than ever.

Video game retail chain GamesStop confirmed to the BBC on Sunday that it is making a $56bn (£41bn) unsolicited takeover offer for e-commerce firm eBay. GameStop's chief executive Ryan Cohen told the Wall Street Journal that he sees potential to make eBay a much bigger rival to Amazon, worth hundreds of billions of dollars. Cohen said his company has built a stake of around 5% in eBay and that the cash and stock takeover offer would value eBay at $125 a share, around 20% higher than its closing price on Friday. The BBC has contacted eBay for comment. Cohen also said that GameStop has a commitment letter from TD Bank to provide around $20bn in debt to help finance the deal. There is nobody who is more qualified, based on my experience, to run the eBay business, added Cohen, who is also the co-founder of online pet-products retailer Chewy.

Independent sample generation is the prevailing paradigm in modern diffusion-based generative models of AI. We ask a different question: can samples coordinate through shared population statistics to transport probability mass more efficiently? We introduce Mean-Field Path-Integral Diffusion (MF-PID), a framework in which samples are promoted to interacting agents whose drift depends self-consistently on the evolving population density. We identify two analytically tractable regimes: a Linear-Quadratic-Gaussian (LQG) benchmark in which the infinite-dimensional mean-field system reduces to a finite set of Riccati and linear ODEs, and a Gaussian-mixture regime governed by a piecewise-constant protocol that preserves closed-form solvability. For a quadratic interaction potential with schedule βt and zero base drift we prove that the self-consistent MF guidance is the exact linear interpolant between initial and target global means -- a result that holds for arbitrary initial and target densities and any βt. Applied to demand-response control of energy systems, where agents aggregated into an ensemble are energy consumers (e.g. The energy saving is independent of the number of zones per building (d = 1-32 tested), confirming that the linear guidance formula broadcasts a single d-vector with O(d) communication and grows mildly in compute (sub-cubically for d 32, asymptotically O(d3) for d 1). Introduction Generative AI has been transformed by diffusion models, which frame sample generation as a stochastic process steered from noise to data [1-3]. A key structural feature of these models -- shared with other generative models, e.g. Similarly, stochastic optimal transport (SOT) and Schrödinger bridge formulations [6-8] cast distribution matching as an independent-particle path optimization, yielding tractable convolutions of Green functions but discarding inter-particle information; stochastic interpolants [9] construct flexible transport bridges between arbitrary densities via tunable continuous-time stochastic processes, recovering the Schrödinger bridge as a special limit -- again in an independent-particle framework.

Groundwater in the Densu Basin is increasingly threatened by heavy metal contamination, but conventional methods fail to capture the statistical complexity and spatial heterogeneity of pollution indicators. A key challenge is modelling the Heavy Metal Pollution Index (HPI), which is typically skewed and affected by correlated contaminants, leading to biased predictions without transformation. This study develops a predictive framework integrating response transformations with nested cross-validated ensemble machine learning. Three transformations (raw, log, and Gaussian copula) were applied to HPI and evaluated across six learners: support vector regression (SVM), $k$-nearest neighbours (k-NN), CART, Elastic Net, kernel ridge regression, and a stacked Lasso ensemble. Raw-scale models produced deceptively high fits (Elastic Net and stacked ensemble $R^2 \approx 1.0$), suggesting over-optimism. The log transformation stabilised variance (SVM: $R^2 = 0.93$, RMSE $= 0.18$; k-NN: $R^2 = 0.92$, RMSE $= 0.20$). The Gaussian copula gave the most reliable results: stacked ensemble $R^2 = 0.96$ (RMSE $= 0.19$), with other learners maintaining high accuracy. Copula-based models improved residuals and produced spatially plausible maps. DBSCAN clustering revealed Fe and Mn as primary HPI contributors, consistent with regional hydrogeochemistry. Limitations include reliance on random (not spatial) cross-validation and basin-specific scope. Future work should explore spatial validation and other geological settings. Overall, distribution-aware ensembles with clustering diagnostics offer robust, interpretable assessments of groundwater contamination.

Despite rapid recent advances in quantum machine learning, the field is in many ways stuck. Existing approaches can exhibit serious limitations, and we still lack learning frameworks that are simple, interpretable, scalable, and naturally suited to quantum data. To address this, here we introduce quantum Gaussian processes, a Bayesian framework for learning from quantum systems through priors over unknown quantum transformations. We show that, under suitable conditions, unitary quantum stochastic processes define Gaussian processes, thereby enabling regression, classification, and Bayesian optimization directly on quantum data. The key ingredient in this framework is sufficient knowledge of a quantum process's structure and symmetries to define an informative prior through its corresponding quantum kernel, effectively injecting a strong, physics-informed inductive bias into the learning model. We then prove that matchgate, or free-fermionic, evolutions give rise to provable and scalable quantum Gaussian processes, providing the first family in our framework where the unknown unitary acts non-trivially on all qubits. Finally, we demonstrate accurate long-range extrapolation, phase-diagram learning in many-body systems, and sample-efficient Bayesian optimization in a quantum sensing task. Our results identify quantum Gaussian processes as a promising route toward simpler and more structured forms of quantum learning.