On the 31st March, our editorial team headed to the Royal Society for AI for Science . This day-long conference explored how AI is changing the nature of scientific discovery, and was hosted by the Fundamental Research team from the Alan Turing Institute. Nestled in a terrace of 19th century townhouses along the banks of the Thames, the Royal Society looks as grand as the names who have passed through its doors throughout the years. Prof Jason McEwen, Chief Scientist for the Turing Institute, opened the event with an insightful talk on the nature of scientific revolution, and how the bidirectional relationship between AI and science could spark the next one. Then, Prof Anna Scaife from the University of Manchester spoke on the use of foundation models for astronomical discovery.

This Indigenous Language Survived Russian Occupation. YouTube's search and recommendation algorithms are driving children to Russian-language content even when they seek out videos in Kyrgyz, creating a cultural shift that concerns some parents. When anthropology researcher Ashley McDermott was doing fieldwork in Kyrgyzstan a few years ago, she says many people voiced the same concern: Children were losing touch with their indigenous language. The Central Asian country of 7 million people was under Russian control for a century until 1991, but Kyrgyz (pronounced kur-giz) survived and remains widely spoken among adults. McDermott, a doctoral student at the University of Michigan, says she also heard that some kids in rural villages where Kyrgyz dominated had spontaneously learned to speak Russian.

Trump's mass firing just dealt another blow to American science Ambitious research is on the chopping block following yet more cuts at the National Science Foundation. This past week delivered another gut punch for science in the US. This time, the target was the National Science Foundation--a federal agency that funds major research projects to the tune of around $9 billion. The foundation's efforts were overseen by a board of 22 prominent scientists. On Friday last week, they were all fired . The NSF has been without a director since April 2025, when former director Sethuraman Panchanathan stepped down in the wake of DOGE-led funding cuts and mass firings.

Launching next week on T-Mobile's network, the cell plan takes a nuclear approach to online safety. A new US-wide cell phone network marketed to Christians is set to launch next week. It blocks porn, which experts in network security say marks the first time a US cell plan has used network-level blocking for such content that can't be turned off even by adult account owners. It's also rolling out a filter on sexual content aimed at blocking material related to gender and trans issues, which will be optional but turned on by default across all plans. The network, which is currently being tested ahead of its May 5 launch date, will be run by Radiant Mobile, a newly launched mobile virtual network operator (MVNO). These operators don't own cell towers but buy bandwidth from the big providers (in this case, T-Mobile) and sell to specific demographics (President Trump announced his own MVNO last year called Trump Mobile; CREDOMobile sends donations to progressive causes).

What are Russia's gains from the Iran war? 'We are not losers; we are winners' Russia has continued heavy attacks on Ukraine for the past 24 hours, with several coming overnight on Thursday and in the early hours of Friday. At least one person has been killed and several have been injured. A Russian drone attack overnight damaged port infrastructure in Ukraine's southern Odesa region and wounded two people in the Black Sea port city of Odesa, regional Governor Oleh Kiper said on Friday morning. Two high-rise residential buildings were damaged in the attack, which destroyed apartments and caused fires, Kiper wrote on the Telegram messaging app. "This night, Russia again massively attacked the civilian infrastructure of the Odesa region: two people were injured," he said.

This paper presents a novel method to enhance the reliability of image classification models during deployment in the face of transient hardware errors. By utilizing enriched text embeddings derived from GPT-3 with question prompts per class and CLIP pretrained text encoder, we investigate their impact as an initialization for the classification layer. Our approach achieves a remarkable 5.5 average increase in hardware reliability (and up to 14) across various architectures in the most critical layer, with minimal accuracy drop (0.3% on average) compared to baseline PyTorch models. Furthermore, our method seamlessly integrates with any image classification backbone, showcases results across various network architectures, decreases parameter and FLOPs overhead, and follows a consistent training recipe. This research offers a practical and efficient solution to bolster the robustness of image classification models against hardware failures, with potential implications for future studies in this domain.

What are Russia's gains from the Iran war? 'We are not losers; we are winners' What do Ukraine's robot soldiers mean for the future of warfare? In a scene reminiscent of a computer war game, three battle-fatigued soldiers, dressed in white snow camouflage, emerge from a war-torn alley with their hands raised above their heads. They crouch down, following the orders being blasted at them, fear and shock etched across their faces as they stare down the barrel of a machinegun mounted on a so-called ground robot. In April, Ukrainian President Volodymyr Zelenskyy said that, for the "first time in the history of this war, an enemy position was taken exclusively by unmanned platforms - ground systems and drones". "Ground robotic systems have already carried out more than 22,000 missions on the front in just three months," he wrote in a post on X, alongside images of green machines with tank tracks and weapons mounted on top.

Pruning the weights of randomly initialized neural networks plays an important role in the context of lottery ticket hypothesis. Ramanujan et al. [23] empirically showed that only pruning the weights can achieve remarkable performance instead of optimizing the weight values. However, to achieve the same level of performance as the weight optimization, the pruning approach requires more parameters in the networks before pruning and thus more memory space. To overcome this parameter inefficiency, we introduce a novel framework to prune randomly initialized neural networks with iteratively randomizing weight values (IteRand). Theoretically, we prove an approximation theorem in our framework, which indicates that the randomizing operations are provably effective to reduce the required number of the parameters. We also empirically demonstrate the parameter efficiency in multiple experiments on CIFAR-10 and ImageNet.

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