Plus: China has approved the world's first commercial brain chip. Where OpenAI's technology could show up in Iran OpenAI has controversially agreed to give the Pentagon access to its AI. But where exactly could its tech show up, and which applications will its customers and employees tolerate? There's pressure to integrate it quickly with existing military tools. One defense official revealed it could even assist in selecting strike targets. OpenAI's partnership with Anduril, which makes drones and counter-drone technologies, adds another hint at what is to come.

How men were left to starve in a South African gold mine. This image was created by Mohamed Hussein using the artificial intelligence (AI) tool Midjourney. Ayanda Ndabeni watched the faint glow from his headlamp fight the vast darkness 1,500 metres (4,920 feet) below ground. His miner's lamp had lasted for more than a week after he was lowered down into the shaft of the gold mine. But now the batteries were dying. He gently flipped the plastic switch of his lamp, turning it off, and the trapped men around him became shadows. In the stifling heat and humidity, their anxiety pressed in from all sides. Ayanda had descended into Shaft 10 of the Buffelsfontein mine in late September 2024, lowered by a team of nearly 20 men operating ropes and a pulley above ground. That day, he'd spotted police vehicles near the mine's entrance. The 36-year-old assumed it was just routine patrols around the mine system, which is 2km (1.2 miles) deep. But then the rope pulley, via which food, water, batteries and other items arrived, stopped moving. The shouting that usually indicated the rope operators were sending down a man or supplies also fell silent. When huge rocks came crashing down the shaft, they knew it was a warning. The men whispered of their growing fears that something was very wrong on the surface. Patrick Ntsokolo was also in Shaft 10. He was a few hundred metres higher up than Ayanda and had arrived in late July. Patrick was new to the mines. Tasked by the leaders of the artisanal miners with collecting the food, water and alcohol lowered down by the rope pulley, he hauled supplies along the slippery tunnels to small shops.

Microsoft's Copilot AI goes head-to-head with China's DeepSeek in Africa Microsoft is investing 5.4 billion South African rand ($330 million) to expand its cloud and AI capacity in the country by the end of next year, and it also has plans to build a geothermal-powered data center in Kenya. Microsoft is making a push for more Africans to adopt its artificial-intelligence tools as the U.S. technology giant competes with China's DeepSeek for customers from the world's youngest and fastest-growing population. The Redmond, Washington-based company plans to train 3 million Africans on its AI technology this year, in partnership with schools, universities and other institutions, with a focus on South Africa, Kenya, Nigeria and Morocco. It's also partnered with MTN Group, Africa's biggest telecommunications firm, to sell the Microsoft 365 suit of apps together with its Copilot digital assistant to its 300 million subscribers. The Microsoft Elevate training initiative aims to make sure cost is not a barrier to building AI literacy at scale," Middle East and Africa President Naim Yazbeck said in an interview. Chinese technology is active in Africa and our job is to compete."

The fungi prefer to grow in cow dung. A newly described African species in the magic mushroom family confirms its evolutionary origin. 'Psilocybe ochraceocentrata' is found growing on cattle dung in the grasslands of southern Africa and Zimbabwe. Breakthroughs, discoveries, and DIY tips sent six days a week. The discovery of a new magic mushroom species in Africa is forcing mycologists to take another look at the famous psychedelic fungi's evolutionary history.

A new book from Rebecca Solnit, promising to bring us hope in these "difficult times", is among our pick of popular science titles out this month - along with a guide on how to talk to AI, and a look at modern warfare March, in the northern hemisphere anyway, is about venturing out for some much-needed vitamin D and dodging showers. Forget that - just head for a decent café where you can delve into the marvellous science books we've got waiting for you. This month you can explore how animals shaped our world, how to spot liars from their language, what forest trees can tell us - and flowers as revolutionaries. There is some stronger stuff too, if you are in the mood: try AI in the hands of the US military, or a deep cultural look at how our world has changed beyond recognition. Whatever your choice, it's all guaranteed to enrich the inner you.

Data normalisation, a common and often necessary preprocessing step in engineering and scientific applications, can severely distort the discovery of governing equations by magnitudebased sparse regression methods. This issue is particularly acute for the Sparse Identification of Nonlinear Dynamics (SINDy) framework, where the core assumption of sparsity is undermined by the interaction between data scaling and measurement noise. The resulting discovered models can be dense, uninterpretable, and physically incorrect. To address this critical vulnerability, we introduce the Sequential Thresholding of Coefficient of Variation (STCV), a novel, computationally efficient sparse regression algorithm that is inherently robust to data scaling. STCV replaces conventional magnitude-based thresholding with a dimensionless statistical metric, the Coefficient Presence (CP), which assesses the statistical validity and consistency of candidate terms in the model library. This shift from magnitude to statistical significance makes the discovery process invariant to arbitrary data scaling. Through comprehensive benchmarking on canonical dynamical systems and practical engineering problems, including a physical mass-spring-damper experiment, we demonstrate that STCV consistently and significantly outperforms standard Sequential Thresholding Least Squares (STLSQ) and Ensemble-SINDy (E-SINDy) on normalised, noisy datasets. The results show that STCV-based methods can successfully identify the correct, sparse physical laws even when other methods fail. By mitigating the distorting effects of normalisation, STCV makes sparse system identification a more reliable and automated tool for real-world applications, thereby enhancing model interpretability and trustworthiness.