Is Trump losing patience with Putin? Will sanctions against Russian oil giants hurt Putin? How much of Europe's oil still comes from Russia? Europe lacks coordination as Russia'prepares for war with NATO': Experts Europe is unprepared to counteract a new chapter of Russian military and intelligence activities in the Baltic and North Seas, experts have told Al Jazeera. At the same time, they said, a growing rift between European and United States intelligence services is leaving the continent unsupported.

The great wildebeest migration, seen from space: satellites and AI are helping count Africa's wildlife The Great Wildebeest Migration is one of the most remarkable natural spectacles on Earth. Each year, immense herds of wildebeest, joined by zebras and gazelles, travel 800-1,000km between Tanzania and Kenya in search of fresh grazing after the rains . This vast, circular journey is the engine of the Serengeti-Mara ecosystem. The migration feeds predators such as lions and crocodiles, fertilises the land and sustains the grasslands. Countless other species, and human livelihoods tied to rangelands and tourism, depend on it.

A data center, which can use as much electricity as Philadelphia, is the new American factory, creating the future and propping up the economy. "I do guess that a lot of the world gets covered in data centers," Sam Altman, the C.E.O. of OpenAI, has said. Drive in almost any direction from almost any American city, and soon enough you'll arrive at a data center--a giant white box rising from graded earth, flanked by generators and fenced like a prison yard. Data centers for artificial intelligence are the new American factory. Packed with computing equipment, they absorb information and emit A.I. Since the launch of ChatGPT, in 2022, they have begun to multiply at an astonishing rate. "I do guess that a lot of the world gets covered in data centers over time," Sam Altman, the C.E.O. of OpenAI, recently said. The leading independent operator of A.I. data centers in the United States is CoreWeave, which was founded eight years ago, as a casual experiment. In 2017, traders at a middling New York hedge fund decided to begin mining cryptocurrency, which they used as the entry fee for their fantasy-football league. To mine the crypto, they bought a graphics-processing unit, a powerful microchip made by the company Nvidia. The G.P.U. was marketed to video gamers, but Nvidia offered software that turned it into a low-budget supercomputer. "It was so successful, from a return-of-capital perspective, that we started scaling it," Brian Venturo, one of CoreWeave's co-founders, told me. "If you make your money back in, like, five days, you want to do that a lot." Within a year, the traders had quit the hedge-fund business and bought several thousand G.P.U.s, which they ran from Venturo's grandfather's garage, in New Jersey.

Ebeyer published posts about famous people who had realized that they were aphantasic: Glen Keane, one of the leading Disney animators on "The Little Mermaid" and "Beauty and the Beast"; John Green, the author of "The Fault in Our Stars," whose books had sold more than fifty million copies; J. Craig Venter, the biologist who led the first team to sequence the human genome; Blake Ross, who co-created the Mozilla-Firefox web browser when he was nineteen. Ebeyer also wanted the Aphantasia Network to be a place where aphantasics could find recent scientific research. For instance, estimating the strength of a person's imagery had been thoroughly subjective until Joel Pearson, a cognitive neuroscientist at the University of New South Wales, in Australia, devised tests to measure it more precisely. In a paper from 2022, Pearson reported that when people with imagery visualized a bright object their pupils contracted, as though they were seeing a bright object in real life, but the pupils of aphantasics imagining a bright object stayed the same. Another study of his had shown that, although aphantasics had the same fear response (sweating) as typical imagers to a frightening image shown on a screen, when exposed to a frightening story they barely responded at all.

I sound Korean--because I am Korean. Can AI make me sound American? It all began, as these things often do, with an Instagram ad . "No one tells you this if you're an immigrant, but accent discrimination is a real thing," said a woman in the video. Her own accent is faintly Eastern European--so subtle it took me a few playbacks to notice.

In Russia's'blitz' of Ukraine, the question of appeasement is back Following another week of intensive and lethal Russian bombardment of Ukraine's cities, a composite image has been doing the rounds on Ukrainian social media. Underneath an old, black-and-white photo of Londoners queuing at a fruit and vegetable stall surrounded by the bombed-out rubble of the Blitz, a second image - this time in colour - creates a striking juxtaposition. Taken on Saturday, it shows shoppers thronging to similar stalls in a northern suburb of the Ukrainian capital, Kyiv, while a column of black smoke rises ominously in the background. Bombs can't stop markets, reads the caption linking the two images. The night before, as the city's sleep was interrupted once again by the now all-too-familiar booms of missile and drone strikes, two people were killed and nine others injured.

Is Trump losing patience with Putin? Will sanctions against Russian oil giants hurt Putin? How much of Europe's oil still comes from Russia? Russian drone attacks on the Ukrainian capital, Kyiv, early on Sunday killed at least three people and wounded 29 others, according to Ukrainian Minister of Internal Affairs Ihor Klymenko. The wounded included seven children, Klymenko said.

Adversarial training has emerged as a key technique to enhance model robustness against adversarial input perturbations. Many of the existing methods rely on computationally expensive min-max problems that limit their application in practice. We propose a novel formulation of adversarial training in reproducing kernel Hilbert spaces, shifting from input to feature-space perturbations. This reformu-lation enables the exact solution of inner maximization and efficient optimization. It also provides a regularized estimator that naturally adapts to the noise level and the smoothness of the underlying function. We establish conditions under which the feature-perturbed formulation is a relaxation of the original problem and propose an efficient optimization algorithm based on iterative kernel ridge regression. We provide generalization bounds that help to understand the properties of the method. We also extend the formulation to multiple kernel learning. Empirical evaluation shows good performance in both clean and adversarial settings.

To understand feature learning dynamics in neural networks, recent theoretical works have focused on gradient-based learning of Gaussian single-index models, where the label is a nonlinear function of a latent one-dimensional projection of the input. While the sample complexity of online SGD is determined by the information exponent of the link function, recent works improved this by performing multiple gradient steps on the same sample with different learning rates -- yielding a non-correlational update rule -- and instead are limited by the (potentially much smaller) generative exponent. However, this picture is only valid when these learning rates are sufficiently large. In this paper, we characterize the relationship between learning rate(s) and sample complexity for a broad class of gradient-based algorithms that encapsulates both correlational and non-correlational updates. We demonstrate that, in certain cases, there is a phase transition from an "information exponent regime" with small learning rate to a "generative exponent regime" with large learning rate. Our framework covers prior analyses of one-pass SGD and SGD with batch reuse, while also introducing a new layer-wise training algorithm that leverages a two-timescales approach (via different learning rates for each layer) to go beyond correlational queries without reusing samples or modifying the loss from squared error. Our theoretical study demonstrates that the choice of learning rate is as important as the design of the algorithm in achieving statistical and computational efficiency.

A key task in Artificial Intelligence is learning effective policies for controlling agents in unknown environments to optimize performance measures. Off-policy learning methods, like Q-learning, allow learners to make optimal decisions based on past experiences. This paper studies off-policy learning from biased data in complex and high-dimensional domains where \emph{unobserved confounding} cannot be ruled out a priori. Building on the well-celebrated Deep Q-Network (DQN), we propose a novel deep reinforcement learning algorithm robust to confounding biases in observed data. Specifically, our algorithm attempts to find a safe policy for the worst-case environment compatible with the observations. We apply our method to twelve confounded Atari games, and find that it consistently dominates the standard DQN in all games where the observed input to the behavioral and target policies mismatch and unobserved confounders exist.