Ukraine on Tuesday hit the Moscow region in series of drone strikes that killed one woman, destroyed dozens of homes and forced some 50 flights to be rerouted from the Russian capital, reporting by Reuters confirmed. The attack on Moscow was reportedly the largest drone strike levied by Kyiv at Russia since the war began more than two and half years ago. Russia, which has heavily relied on drones and missiles in its assault against Ukraine and routinely pummels Kyiv with a barrage of aerial assaults, said it destroyed at least 20 Ukrainian drones over the Moscow region along with another 124 across eight other regions. A residential building outside of Moscow after it was hit in a series of drone strikes by Ukraine on Sept. 10, 2024. Kremlin spokesperson Dmitry Peskov suggested the attacks levied at the Russian capital, which has a population of some 21 million, were not legitimate military targets.

When Elon Musk shared an image showing Kamala Harris dressed as a "communist dictator" on X last week, it was quite obviously a fake, seeing as Harris is neither a communist nor, to the best of our knowledge, a Soviet cosplayer. And, as many observers noted, the woman in the photo, presumably generated by X's Grok tool, had only a passing resemblance to the vice president. "AI still is unable to accurately depict Kamala Harris," one X user wrote. "Grok put old Eva Longoria in a snazzy outfit and called it a day," another quipped, noting the similarity of the "dictator" pictured to the Desperate Housewives star. "AI just CANNOT replicate Kamala Harris," a third posted.

As Apple launched the new iPhone 16 at its'Glowtime' event last night, it was the company's latest AI features which took centre stage once again. Now, Apple has finally revealed that its highly anticipated Apple Intelligence will begin to roll out in the US next month. As part of the iOS 18.1 update, iPhone 16 users will get access to AI features including rewriting tools, summarised notifications, and big improvements to Siri. However, UK tech fans will need to wait a little while longer as the California-based tech giant says that Apple Intelligence won't arrive there until December. So, with the rollout of Apple's first-ever AI tools just around the corner, MailOnline breaks down what is coming and when you can expect to try it out.

Google lost its final legal challenge on Tuesday against a European Union penalty for giving its own shopping recommendations an illegal advantage over rivals in search results, ending a long-running antitrust case that came with a whopping fine. The European Union's Court of Justice upheld a lower court's decision, rejecting the company's appeal against the 2.4 billion euro ( 2.7 billion) penalty from the European Commission, the 27-nation bloc's top antitrust enforcer. "By today's judgment, the Court of Justice dismisses the appeal and thus upholds the judgment of the General Court," the court said in a press release summarizing its decision. The commission's original decision in 2017 accused the Silicon Valley giant of unfairly directing visitors to its own Google Shopping service to the detriment of competitors. It was one of three multibillion-euro fines that the commission imposed on Google in the previous decade as Brussels started ramping up its crackdown on the tech industry.

This is the moment a drone attack hit a residential building near Moscow. Russian authorities say at least two high-rise apartments were damaged in a wave of overnight attacks by Ukraine on the area.

The Ukrainian Air Force said on Telegram that its air defences downed 38 out of 46 Shahed-type attack drones launched by Russia. They were shot down over a number of regions and cities including Kyiv, Odesa, Kherson, Sumy, Kharkiv and Poltova. The air force added that Russia also launched an Iskander-M ballistic missile and a Kh-31 air-to-surface missile. Ukraine and Russia regularly launch overnight drone raids on each other's territory. The latest wave of drone strikes comes as Moscow claims gains in eastern Ukraine.

What jumps to mind when you think about the impending AI apocalypse? If you're partial to sci-fi movie cliches, you may envisage killer robots (with or without thick Austrian accents) rising up to terminate their hubristic creators. Or perhaps, a la The Matrix, you'll go for scary machines sucking energy out of our bodies as they distract us with a simulated reality. For Yuval Noah Harari, who has spent a lot of time worrying about AI over the past decade, the threat is less fantastical and more insidious. "In order to manipulate humans, there is no need to physically hook brains to computers," he writes in his engrossing new book Nexus.

One person has been killed in Moscow after the remnants of a downed Ukrainian drone hit the apartment block where he was living and started a fire, according to Russian officials. Moscow regional Governor Andrei Vorobyov said debris from the drone damaged at least two high-rise apartment buildings in the Ramenskoye district in the early hours of Tuesday, setting several flats on fire. City mayor Sergei Sobyanin said emergency teams had been sent to a number of locations across the region as well as to the area near the Zhukovo airport and around the Domodedovo district – the site of one of Moscow's largest airports. More than 30 flights were suspended. Russia said its air defences shot down more than 70 Ukrainian drones during the night with at least 15 intercepted in and around Moscow.

Correct reasoning steps are important for language models to achieve high performance on many tasks, such as commonsense reasoning, question answering, and mathematical problem-solving [Wei et al., 2022, Kojima et al., 2022, Suzgun et al., 2022]. One way to elicit reasoning is through the chain-of-thought (CoT) method Wei et al. [2022], Kojima et al. [2022], which asks the model to provide step-by-step reasoning. Another approach encourages the model to provide similar problems Yasunaga et al. [2024] as the query, indirectly compelling the model to first understand the original query. Similarly, repeating and rephrasing the query Deng et al. [2023], Mekala et al. [2023] requires the model to first understand the problem and then modify the query into its own words. This rephrasing might help simplify the problem for the model. Additionally, reasoning can be generated by indirectly providing reasoning examples in demonstrations, referred to as in-context learning (ICL) Brown et al. [2020], Min et al. [2022], Xie et al. [2021]. While these methods have demonstrated significant performance improvements, language models are still prone to errors due to incorrect context understanding or analytical steps. Furthermore, they are subject to instability when requests are paraphrased. This instability is particularly concerning in the context of adversarial prompts, where recent research [Zou et al., 2023, Zeng et al., 2024] has shown that adversaries can intentionally rewrite prompts to coax safety-aligned language models into generating objectionable content that they would not generate otherwise.

Seasonal climate forecasts are commonly based on model runs from fully coupled forecasting systems that use Earth system models to represent interactions between the atmosphere, ocean, land and other Earth-system components. Recently, machine learning (ML) methods are increasingly being investigated for this task where large-scale climate variability is linked to local or regional temperature or precipitation in a linear or non-linear fashion. This paper investigates the use of interpretable ML methods to predict seasonal precipitation for East Africa in an operational setting. Dimension reduction is performed by decomposing the precipitation fields via empirical orthogonal functions (EOFs), such that only the respective factor loadings need to the predicted. Indices of large-scale climate variability--including the rate of change in individual indices as well as interactions between different indices--are then used as potential features to obtain tercile forecasts from an interpretable ML algorithm. Several research questions regarding the use of data and the effect of model complexity are studied. The results are compared against the ECMWF seasonal forecasting system (SEAS5) for three seasons--MAM, JJAS and OND--over the period 1993-2020. Compared to climatology for the same period, the ECMWF forecasts have negative skill in MAM and JJAS and significant positive skill in OND. The ML approach is on par with climatology in MAM and JJAS and a significantly positive skill in OND, if not quite at the level of the OND ECMWF forecast.