Reactor no 3 lost off-site power for about 24 hours after the attack. Reactor no 3 lost off-site power for about 24 hours after the attack. A drone strike that cut off external power to a nuclear reactor in the United Arab Emirates this week has revived concerns over the safety of nuclear plants during wartime. Reactor no 3 at the Barakah nuclear plant lost vital off-site power for about 24 hours after the attack on Sunday, forcing it to rely on emergency diesel generators. The UAE's defence ministry said on Tuesday that three drones targeting the plant had originated from Iraqi territory, suggesting a pro-Iranian proxy group was most likely to have been behind the strike.

Will Gulf states join war? What is the UAE's Barakah nuclear plant, nearly hit by a drone? A drone attack that caused a fire close to the Barakah Nuclear Energy Plant in the United Arab Emirates has raised further concerns about nuclear security and military escalation in the Gulf as discussions of peace between Iran and the United States hang in the balance. Barakah was the first nuclear power station to be built on the Arabian Peninsula. What is the Barakah Nuclear Energy Plant? Barakah is a nuclear energy plant located in Al Dhafra, the largest municipal region of the emirate of Abu Dhabi.

Plus: social media firms have agreed to be assessed on how effectively they protect teens' mental health A "QuitGPT" campaign is urging people to cancel their ChatGPT subscriptions In September, Alfred Stephen, a freelance software developer in Singapore, purchased a ChatGPT Plus subscription, which costs $20 a month and offers more access to advanced models, to speed up his work. But he grew frustrated with the chatbot's coding abilities and its gushing, meandering replies. Then he came across a post on Reddit about a campaign called QuitGPT. QuitGPT is one of the latest salvos in a growing movement by activists and disaffected users to cancel their subscriptions. In just the past few weeks, users have flooded Reddit with stories about quitting the chatbot. And while it's unclear how many users have joined the boycott, there's no denying QuitGPT is getting attention.

AI is driving unprecedented investment for massive data centers and an energy supply that can support its huge computational appetite. One potential source of electricity for these facilities is next-generation nuclear power plants, which could be cheaper to construct and safer to operate than their predecessors. We recently held a subscriber-exclusive Roundtables discussion on hyperscale AI data centers and next-gen nuclear --two featured technologies on the MIT Technology Review 10 Breakthrough Technologies of 2026 list . You can watch the conversation back here, and don't forget to subscribe to make sure you catch future discussions as they happen. Demis Hassabis, CEO of Google DeepMind, summed it up in three words: "This is embarrassing." Hassabis was replying on X to an overexcited post by Sébastien Bubeck, a research scientist at the rival firm OpenAI, announcing that two mathematicians had used OpenAI's latest large language model, GPT-5, to find solutions to 10 unsolved problems in mathematics.

Could Ukraine hold a presidential election right now? Will Europe use frozen Russian assets to fund war? How can Ukraine rebuild China ties? 'Ukraine is running out of men, money and time' Russia targets Ukraine's energy as trilateral talks loom As the presidents of Ukraine, Russia and the United States prepare to hold their first trilateral meeting to end Russia's war in Ukraine this weekend, almost half of Ukraine is without electricity and heat in sub-zero temperatures, following repeated Russian drone strikes targeting energy infrastructure. The strikes appeared designed to break Ukrainian resistance at the negotiating table on territorial concessions to Russia - the one issue Ukraine and the US said remained unresolved at the end of talks in Davos, Switzerland, between Ukraine's Volodymyr Zelenskyy and US President Donald Trump this week.

If a major disaster like Fukushima or Chernobyl ever happens again, the world would know almost straight away, thanks to an array of government and DIY radiation-monitoring programs running globally.

Ukraine is preparing to present a revised peace plan to the White House, as it seeks to avoid making territorial concessions to Russia. Kyiv is set propose alternatives to the US after President Volodymyr Zelensky again ruled out surrendering land, saying he had no right to do so under Ukrainian or international law. He made the comments as he met European and Nato leaders on Monday, part of a collective push to deter the US from backing a peace deal which includes major concessions for Ukraine, and which allies fear would leave it vulnerable to a future invasion. Meanwhile, the city of Sumy in north-western Ukraine was left without power overnight after a Russian drone attack. The region's governor said more than a dozen drones had hit power infrastructure, the latest in Russia's nightly attacks.

The integration of advanced robotics in nuclear power plants (NPPs) presents a transformative opportunity to enhance safety, efficiency, and environmental monitoring in high-stakes environments. Our paper introduces the Optimus-Q robot, a sophisticated system designed to autonomously monitor air quality and detect contamination while leveraging adaptive learning techniques and secure quantum communication. Equipped with advanced infrared sensors, the Optimus-Q robot continuously streams real-time environmental data to predict hazardous gas emissions, including carbon dioxide (CO$_2$), carbon monoxide (CO), and methane (CH$_4$). Utilizing a federated learning approach, the robot collaborates with other systems across various NPPs to improve its predictive capabilities without compromising data privacy. Additionally, the implementation of Quantum Key Distribution (QKD) ensures secure data transmission, safeguarding sensitive operational information. Our methodology combines systematic navigation patterns with machine learning algorithms to facilitate efficient coverage of designated areas, thereby optimizing contamination monitoring processes. Through simulations and real-world experiments, we demonstrate the effectiveness of the Optimus-Q robot in enhancing operational safety and responsiveness in nuclear facilities. This research underscores the potential of integrating robotics, machine learning, and quantum technologies to revolutionize monitoring systems in hazardous environments.

Google is still aiming for its "moonshot" 2030 energy goals The company's electricity demand has doubled since 2020, making its end-of-decade target more of a challenge. Last week, we hosted EmTech MIT, MIT Technology Review's annual flagship conference in Cambridge, Massachusetts. Over the course of three days of main-stage sessions, I learned about innovations in AI, biotech, and robotics. But as you might imagine, some of this climate reporter's favorite moments came in the climate sessions. I was listening especially closely to my colleague James Temple's discussion with Lucia Tian, head of advanced energy technologies at Google. They spoke about the tech giant's growing energy demand and what sort of technologies the company is looking to to help meet it.

The nuclear industry is advancing toward more new reactor designs, with next-generation reactors expected to be smaller in scale and power output. These systems have the potential to produce large volumes of information in the form of multivariate time-series data, which could be used for enhanced real-time monitoring and control. In this context, the development of remote autonomous or semi-autonomous control systems for reactor operation has gained significant interest. A critical first step toward such systems is an accurate diagnostics module capable of detecting and localizing anomalies within the reactor system. Recent studies have proposed various ML and DL approaches for anomaly detection in the nuclear domain. Despite promising results, key challenges remain, including limited to no explainability, lack of access to real-world data, and scarcity of abnormal events, which impedes benchmarking and characterization. Most existing studies treat these methods as black boxes, while recent work highlights the need for greater interpretability of ML/DL outputs in safety-critical domains. Here, we propose an unsupervised methodology based on an LSTM autoencoder with a dual attention mechanism for characterization of abnormal events in a real-world reactor radiation area monitoring system. The framework includes not only detection but also localization of the event and was evaluated using real-world datasets of increasing complexity from the PUR-1 research reactor. The attention mechanisms operate in both the feature and temporal dimensions, where the feature attention assigns weights to radiation sensors exhibiting abnormal patterns, while time attention highlights the specific timesteps where irregularities occur, thus enabling localization. By combining the results, the framework can identify both the affected sensors and the duration of each anomaly within a single unified network.