With an initial capacity of 24 megawatts, the innovative data center uses seawater as a natural cooling system. China is submerging data centers into the ocean to keep them cool.Photograph: Shanghai Hailanyun Technology China has become the first country in the world to operate an underwater data center, or UDC, powered by wind. Located off the coast of Shanghai, the complex represents a significant advance in the country's strategy to secure energy supplies in the face of the accelerated growth of artificial intelligence, reduce dependence on fossil fuels, and reduce the environmental impact of its technology infrastructure. The initiative is the result of a collaboration between private company HiCloud Technology and state-owned China Communications Construction, which involved an investment of 1.6 billion yuan, equivalent to about $236 million. With an initial capacity of 24 megawatts, the facility is submerged at a depth of 10 meters in the Lin-gang Special Zone, within the China Pilot Free Trade Zone in Shanghai.

Hosted by Eleanor Drage and Kerry McInerney, The Good Robot is a podcast which explores the many complex intersections between gender, feminism and technology. How can communities take back control of the digital infrastructure that powers everyday life? In this episode, Eleanor Drage speaks with Tara Merk about how community-owned data centers could transform digital ownership and challenge the dominance of Big Tech. The conversation explores alternative models of internet infrastructure that prioritize local empowerment, sustainability, and cooperative governance over corporate control. Drawing on examples from Germany's renewable energy sector and community-led initiatives, Merk reflects on how decentralized ownership models can create fairer and more environmentally responsible technological systems.

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He is about to take SpaceX public--pushing other AI companies to do the same. Elon Musk is about to set in motion a chain of events that will reshape the global financial order. For starters, when SpaceX formally goes public next week, he is all but guaranteed to become the world's first trillionaire. His rocket company is targeting a valuation of $1.77 trillion, which would make it one of the 10 biggest companies in the world--bigger than Meta, Walmart, and, for that matter, Tesla. All of this activity is less about colonizing Mars and more about providing the infrastructure for the AI boom: Musk wants to use his rockets to launch data centers into space, where there is abundant solar power to harvest.

There is plenty of intriguing sci-fi on offer this month, whether it's solar-powered cities from Adrian Tchaikovsky or a strange future from M. John Harrison A father mysteriously slips through time in Joseph Eckert's Writing this as the UK swelters under an unprecedented May heatwave, perhaps it's small wonder that so many science-fiction authors are currently imagining miserable versions of an overheated future in which their characters are struggling to survive. I'm intrigued by the sound of sci-fi legend M. John Harrison's upcoming take on a dystopian future, but if post-apocalyptic hellscapes aren't your thing, I'm also happy to report that there are other options for sci-fi fans this month. Next, I'm going to explore Isabel J. Kim's sci-fi spin on immigration,, as soon as I can get my hands on it. I am excited about this book: M. John Harrison is a really classy writer, winner of all sorts of awards, and his latest novel sounds right up my street. It's set in a future years after an obscure "crisis" changed everything, in a world where the seas are full of new creatures.

Conformal prediction (CP) and its extension, conformal risk control (CRC), are established frameworks for quantifying uncertainty in supervised machine learning through formal guarantees. However, recent breakthroughs in artificial intelligence (AI) have been driven by unsupervised generative models, such as large language models (LLMs) and image generators, which are not directly compatible with CP or CRC. In this work we introduce conformal generation (Conf-Gen), a general framework adapting CRC to generative tasks while relaxing its theoretical assumptions. Conf-Gen unifies and generalizes previous attempts to apply CP to LLMs, and extends conformal methodology to entirely new domains. We demonstrate the flexibility of Conf-Gen through some novel applications, including obtaining conformal guarantees on: image generators producing non-memorized images, conversational AI systems having asked enough clarifying questions, and the output of AI agents being correct.

While parameter-efficient fine-tuning methods like low-rank adaptation (LoRA) are standard for large language models, principled estimation of epistemic uncertainty remains challenging. Recent results in the LoRA regime suggest that discrete multi-mode approaches such as deep ensembles offer little benefit over single-mode methods. This contradicts broader observations in deep learning, where ensembling independent optima typically improves generalization, and linking these modes through continuous low-loss valleys further enhances Bayesian model averaging (BMA). Whether such structure exists in the LoRA space and whether it yields functional diversity missed by local or discrete methods has not been studied. We introduce LoRA-Curve, a segmented Bézier curve parameterization in the LoRA space, with two variants: a free configuration that jointly optimizes all control points, and an anchored configuration that connects independently fine-tuned LoRA optima. We prove pathwise continuity and Lipschitz regularity of the loss along the curve and empirically show, across reasoning and classification benchmarks with Qwen2.5 7B, that linear interpolation encounters loss barriers, while our anchored multi-segment curves connect independent optima through continuous low-loss valleys. Combined with flat-minima perturbations and a Jensen-Shannon divergence regularizer, LoRA-Curve yields measurably higher mutual information of the predictive distribution without sacrificing performance, and links continuous parameter-space traversal to functional diversity.

Plus: Illinois just passed what could become America's strongest AI safety law. Climate tech companies are going public. Solar and battery company Solv Energy went public in February, hitting a $6 billion valuation. X-energy, which builds small modular nuclear reactors, followed at $11.5 billion. Then came geothermal company Fervo Energy, reaching a market cap of about $12.4 billion. All three have been IPO success stories.

In the race against China for AI supremacy, the United States dominates when it comes to access to the most cutting-edge semiconductors. But when it comes to powering the huge data centres that run on AI chips, China holds the clear advantage. A typical data centre can consume as much electricity as 100,000 households, while next-generation "hyperscale" facilities can gobble up as much power as two million homes, according to the International Energy Agency (IEA). China's access to an abundant supply of cheap electricity places it in the ideal position to meet such colossal energy demands. China already generates more than twice as much electricity as the US, a lead that is expected to widen amid an aggressive state-led investment in the country's energy grid.