Israel and Iran have carried out a new wave of attacks on key cities, fuelling fears of an all-out sustained war, with heavy exchanges now entering a third day. Iranian missiles struck northern Israel, killing at least three people and wounding 13 others, late Saturday into Sunday, according to Israeli media. Israel targeted the Iranian defence ministry headquarters in Tehran early Sunday, according to the semi-official Tasnim news agency. Iranian officials also said the Shahran oil depot, northwest of Tehran, was struck by Israel. Tasnim News said operational and rescue forces arrived at the scene and are still working to extinguish the fire.

The site of the secret Manhattan Project in Oak Ridge, Tennessee has a new mission to help achieve an A.I. advantage over China. A new uranium enrichment facility in Oak Ridge will supply nuclear fuel to the reactors that power A.I. data centers. Over 80 years after scientists of the'Manhattan Project' harnessed the power of the atom to end World War II, the top-secret worksite has a new mission to help dominate AI before China does. The first phase of the United States' latest uranium enrichment facility opened in Oak Ridge, Tennessee in May. Uranium powers the nuclear reactors the AI data centers are turning to for reliable energy.

"Just because of this shorter distance, we will put down signals that will be approximately a hundred times stronger than the GPS signal," says Tyler Reid, chief technology officer and cofounder of Xona. "That means the reach of jammers will be much smaller against our system, but we will also be able to reach deeper into indoor locations, penetrating through multiple walls." The first GPS system went live in 1993. In the decades since, it has become one of the foundational technologies that the world depends on. The precise positioning, navigation, and timing (PNT) signals beamed by its satellites underpin much more than Google Maps in your phone. They guide drill heads at offshore oil rigs, time-stamp financial transactions, and help sync power grids all over the world.

The United Nations' digital agency says that operational carbon emissions for the world's top tech companies rose an average of 150 percent between 2020 and 2023 as investments in artificial intelligence (AI) and data centres drove up global electricity demand. Operational emissions for Amazon grew 182 percent in 2023 against 2020 levels, while emissions for Microsoft grew 155 percent, Facebook and Instagram owner Meta grew 145 percent, and Google parent company Alphabet grew 138 percent over the same period, according to the UN's International Telecommunication Union (ITU). The figures include the emissions directly created by the companies' operations as well as those from purchased energy consumption. They were included in a new report from ITU assessing the greenhouse gas emissions of the world's top 200 digital companies between 2020 and 2023. The UN agency linked the sharp uptick to recent breakthroughs in AI and the demand for digital services like cloud computing.

Hosted by Ben Byford, The Machine Ethics Podcast brings together interviews with academics, authors, business leaders, designers and engineers on the subject of autonomous algorithms, artificial intelligence, machine learning, and technology's impact on society. This is our 100th episode! A super special look at AI and the environment, we interviewed four experts for this DeepDive episode. We chatted about water stress, the energy usage of AI systems and data centres, using AI for fossil fuel discovery, the geo-political nature of AI, GenAI vs other ML algorithms for energy use, demanding transparency on energy usage for training and operating AI, more AI regulation for carbon consumption, things we can change today like picking renewable hosting solutions, publishing your data, when doing "responsible AI" you must include the environment, considering who are the controllers of the technology and what do they want, and more… Hannah Smith is Director of Operations for Green Web Foundation and co-founder of Green Tech South West. She has a background in Computer Science.

Monster Train 2 has pulled into the station, and I'm thrilled to be climbing aboard for the ride. Developed by San Francisco-based indie studio Shiny Shoe, this video game follows their incredibly popular and highly addictive roguelike deckbuilder released in 2020, which had players defend a train headed to reignite the extinguished heart of Hell. The sequel has you boarding a hellish locomotive once more, defending the Pyre on top from waves of enemies across the vehicle's three vertical levels. However, rather than barreling down to the deepest circles of Hell, Monster Train 2 has you ascending toward Heaven. And rather than battling angelic armies, you're fighting alongside them to take on a new enemy: the otherworldly Titans. I've played around 40 hours of Monster Train 2 on PC so far, and fully expect that number to increase at a socially unacceptable rate.

Meta on Tuesday said it had struck an agreement to keep one nuclear reactor of a US utility company in Illinois operating for 20 years. Meta's deal with Constellation Energy is the social networking company's first with a nuclear power plant. Other large tech companies are looking to secure electricity as US power demand rises significantly in part due to the needs of artificial intelligence and datacenters. Google has reached agreements to supply its datacenters with nuclear power via a half-dozen small reactors built by a California utility company. Microsoft's similar contract will restart the Three Mile Island nuclear plant, the site of the most serious nuclear accident and radiation leak in US history.

Two weeks ago, we launched Power Hungry, a new series shining a light on the energy demands and carbon costs of the artificial intelligence revolution. It raised some worrying issues, not least the incredible energy demands of AI video generation. But there are also reasons to be hopeful: innovations that could improve the efficiency of the software behind AI models, the computer chips those models run on, and the data centers where those chips hum around the clock. Here's what you need to know about how energy use, and therefore carbon emissions, could be cut across all three of those domains, plus an added argument for cautious optimism: the underlying business realities may ultimately bend toward more energy-efficient AI. In each issue of our print magazine, we ask a member of staff to tell us about three things they're loving at the moment. For our latest edition, which was all about creativity, we asked our China reporter Caiwei Chen to give us an insight into her life.

An important problem in sequential decision-making under uncertainty is to use limited data to compute a safe policy, which is guaranteed to outperform a given baseline strategy. In this paper, we develop and analyze a new model-based approach that computes a safe policy, given an inaccurate model of the system's dynamics and guarantees on the accuracy of this model. The new robust method uses this model to directly minimize the (negative) regret w.r.t. the baseline policy. Contrary to existing approaches, minimizing the regret allows one to improve the baseline policy in states with accurate dynamics and to seamlessly fall back to the baseline policy, otherwise. We show that our formulation is NP-hard and propose a simple approximate algorithm. Our empirical results on several domains further show that even the simple approximate algorithm can outperform standard approaches.

Markov Chain Monte Carlo (MCMC) and Belief Propagation (BP) are the most popular algorithms for computational inference in Graphical Models (GM). In principle, MCMC is an exact probabilistic method which, however, often suffers from exponentially slow mixing. In contrast, BP is a deterministic method, which is typically fast, empirically very successful, however in general lacking control of accuracy over loopy graphs. In this paper, we introduce MCMC algorithms correcting the approximation error of BP, i.e., we provide a way to compensate for BP errors via a consecutive BP-aware MCMC. Our framework is based on the Loop Calculus (LC) approach which allows to express the BP error as a sum of weighted generalized loops.