Is the war entering a new phase? The overpriced gas Anatoly has been buying in recent weeks in Moscow will ruin his white Kia's engine. "It's low-quality," the taxi driver told Al Jazeera withholding his last name for security purposes. "The engine already sounds like a sick heart The government allows a'temporary decrease in quality,' but what am I to do when I need new spare parts" that are barely available because of Western sanctions, he asked rhetorically. They don't knock, they kick the door," said the 49-year-old with a three-day stubble and bloodshot eyes. Russia's top military brass has not commented on Ukraine's assaults. But even the Kremlin's most outspoken supporters have changed their once-triumphant tune. "We have to get ready for hardships and self-sacrifice," Vladimir Solovyov, a popular talk show host on the Rossiya 1 television network, said in mid-June. Solovyov has a penchant for aggressive, loud monologues and military-style attire. He once urged the Kremlin to "erase" Ukrainian cities with nuclear strikes and said that Kyiv and its Western allies "serve the prince of darkness." Military bloggers are even more pessimistic because of their proximity to the frontline. One of them, Prizrak Novorossii (The Ghost of New Russia), wrote on Telegram in late June that the Kremlin should conduct a massive mobilisation campaign because Russians already "foresee big changes and possible cataclysms because of, to put it mildly, the unfavourable dynamics of hostilities." The reason is simple - outmanned Ukrainians use "an asymmetrical tactic of long-range drone strikes with technological solutions that Russia is only catching up on," he wrote. "So, the question isn't about whether or not to have mobilisation, but about how to conduct it," the blogger concluded, adding that recent events "inspire little optimism." 'I'm afraid my son will be drafted' "I'm afraid my son will be drafted, but we don't have money to send him abroad," Kseniya, a mother of two from the western city of Tula, told Al Jazeera. She withheld her last name and personal details for security purposes. "We've been told a thousand times that [Russian President Vladimir] Putin brought stability, and instead, we see total chaos.

Image caption, Artist's impression of the Swift observatory which was built to study the cosmos A Nasa-funded spacecraft has been sent into space to catch a falling telescope. The Swift observatory detects some of the most powerful explosions in the Universe - but is at risk of crashing back to Earth in the coming months. The small space telescope will be intercepted by the LINK craft, which will attempt to grab it with three robotic arms, and try and lift it back to a safe orbit. The rescue mission, launched on Friday, has never been attempted before, and Dr Simeon Barber, a space scientist, has said it is high risk. But Nasa obviously thinks it's worth a go.

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Is the war entering a new phase? Ukrainian officials say at least four people have been killed and 10 injured in the latest Russian attacks, a day after Moscow hit Kyiv in the deadliest series of attacks this year. In the bordering Sumy region, two women, an elderly man and a toddler were killed and three others injured after a Russian drone hit a residential apartment building, Oleh Hryhorov, head of the regional military administration, said on Friday. Two of the injured women remain in hospital, said Vilkul, adding that nine apartment blocks, a school building, a company, several shops, garages and about 10 vehicles were damaged. Two residential buildings were also cut off from the gas supply.

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Recent work has established a fundamental trilemma between Byzantine robustness, local differential privacy (LDP), and optimization error in distributed learning. We show that this trilemma does not universally extend to generalization error, but instead depends critically on the privacy regime. Specifically, in the high-noise regime (strong privacy), we prove that increasing privacy reduces the generalization error, i.e., there is no tension between robustness and privacy. In the low-noise regime (weaker privacy), however, the tension between robustness and privacy reappears and increasing privacy indeed degrades generalization. Our theory explains this surprising non-monotonic behavior of the generalization error via matching lower and upper bounds on the algorithmic stability of Byzantine-robust distributed learning under LDP constraints. We corroborate and further analyze these theoretical findings with empirical evaluations.

The aggregation with exponential weights (AEW) estimator is not fully understood in the basic setting of model selection aggregation with squared loss. In particular, whether it is minimax-rate optimal in expectation for large enough fixed temperatures and under random design has been an open problem since its introduction, which was explicitly posed by Lecué and Mendelson (2013). In this paper, we settle this problem by showing that \emph{without} requiring a Bernstein-type assumption, the AEW indeed achieves the excess risk $T \log (M) / (n+1)$ in expectation, whenever the temperature $T$ satisfies $(L^2/T)\exp(B/T)\leq μ/2$. Here, the number of dictionary elements is $M$, the estimator has observed $n$ i.i.d. samples from any distribution, and the loss is assumed to be bounded by $B$, $L$-Lipschitz continuous and $μ$-strongly convex. For squared loss, we show that $T\geq 4 b^2$ suffices when the predictions and labels are $[0,b]$-valued. Because AEW is known to be suboptimal in expectation for temperatures below some constant, this shows that AEW has a sharp phase transition when the temperature is large enough but constant, as conjectured by Lecué and Mendelson.

AI is'not smart' so what's next in artificial intelligence? We don't have robots that are nearly as good at understanding the physical world as a rat, says Yann LeCun, one of the leading figures in the world of artificial intelligence. He worked at Facebook-owner, Meta, for a decade, where he was chief AI scientist, but left in 2025 and founded Advanced Machine Intelligence Labs (AMI Labs). His goal is to move AI beyond current systems like ChatGPT, Claude and Gemini. They have their uses, he says, but will never be able to tackle complicated situations in the real world, like getting a robot to do household chores.

One of Kalshi's most prominent traders tells WIRED he's swearing off Spotify-related markets until the issue is resolved. Top Kalshi trader Caleb Davies usually speaks to the press about how prediction markets help him rake in money. The Minneapolis-based IT worker estimates he's made $1.2 million overall across different prediction platforms, with $414,000 in winnings from Kalshi's culture markets alone. He especially enjoys wagering on music charts, because he carefully analyzes Spotify data to pick winners. "Every single morning, I'm going in, downloading the data, and updating my projections," he tells WIRED.

Artificial intelligence news covers a top manufacturer saying AI creates jobs, President Donald Trump on chipmaking in Arizona, an ECG study on sudden cardiac death and more.