Musk v. Altman is a fight over how much power is too much in Silicon Valley. Get your news from a source that's not owned and controlled by oligarchs. In May 2016, Elon Musk did something out of character that he has now spent years of his life trying to undo: He made what he believed to be a charitable donation. The world's richest man is also among its stingiest. Musk's private foundation often doles out less than the minimum percentage required by law.

What are Russia's gains from the Iran war? 'We are not losers; we are winners' The world's militaries spent $2.88 trillion in 2025, an increase of 2.9 percent from the year before, according to the Stockholm International Peace Research Institute's (SIPRI) latest report. To put that number into perspective, $2.88 trillion amounts to $350 of military spending for each person on the planet. In this visual explainer, Al Jazeera unpacks the rise of global militarisation, including how much each nation spends, which countries sell the most weapons, and how military spending compares with spending on healthcare and education. In 2025, the five biggest military spenders were the United States ($954bn), China ($336bn), Russia ($190bn), Germany ($114bn) and India ($92bn), accounting for more than half (58 percent) of world military spending. The US is by far the biggest spender, as it has been every year since World War II.

Modeling the interaction between proteins and ligands and accurately predicting their binding structures is a critical yet challenging task in drug discovery. Recent advancements in deep learning have shown promise in addressing this challenge, with sampling-based and regression-based methods emerging as two prominent approaches. However, these methods have notable limitations. Sampling-based methods often suffer from low efficiency due to the need for generating multiple candidate structures for selection. On the other hand, regression-based methods offer fast predictions but may experience decreased accuracy.

From sorting chicken nuggets to screwing in light bulbs, Eka's robots are eerily lifelike. But do they have real physical smarts? It starts gingerly pawing around the table, as if searching for its glasses on the nightstand. It gently positions the bulb between its two pincers. The claw goes chasing it across the table. After a few nips, the bulb is back in its grasp. In more than a decade of writing about robots, I have never seen one move so naturally.

HCCV datasets constructed through nonconsensual web scraping lack crucial metadata for comprehensive fairness and robustness evaluations. Current remedies are post hoc, lack persuasive justification for adoption, or fail to provide proper contextualization for appropriate application. Our research focuses on proactive, domain-specific recommendations, covering purpose, privacy and consent, and diversity, for curating HCCV evaluation datasets, addressing privacy and bias concerns. We adopt an ante hoc reflective perspective, drawing from current practices, guidelines, dataset withdrawals, and audits, to inform our considerations and recommendations.

A private learner is trained on a sample of labeled points and generates1 a hypothesis that can be used for predicting the labels of newly sampled2 points while protecting the privacy of the training set [Kasiviswannathan3 et al., FOCS 2008]. Research uncovered that private learners may need to4 exhibit significantly higher sample complexity than non-private learners5 as is the case with, e.g., learning of one-dimensional threshold functions6 [Bun et al., FOCS 2015, Alon et al., STOC 2019].7 We explore prediction as an alternative to learning. Instead of putting8 forward a hypothesis, a predictor answers a stream of classification queries.9 Earlier work has considered a private prediction model with just a single10 classification query [Dwork and Feldman, COLT 2018]. We observe that11 when answering a stream of queries, a predictor must modify the hypothesis12 it uses over time, and, furthermore, that it must use the queries for this13 modification, hence introducing potential privacy risks with respect to the14 queries themselves.15 We introduce private everlasting prediction taking into account the privacy16 of both the training set and the (adaptively chosen) queries made to the17 predictor. We then present a generic construction of private everlasting18 predictors in the PAC model. The sample complexity of the initial training19 sample in our construction is quadratic (up to polylog factors) in the VC20 dimension of the concept class. Our construction allows prediction for21 all concept classes with finite VC dimension, and in particular threshold22 functions with constant size initial training sample, even when considered23 over infinite domains, whereas it is known that the sample complexity24 of privately learning threshold functions must grow as a function of the25 domain size and hence is impossible for infinite domains.26