Large Language Models (LLMs) are transforming search engines into Conversational Search Engines (CSE). Consequently, Search Engine Optimization (SEO) is being shifted into Conversational Search Engine Optimization (C-SEO). We are beginning to see dedicated C-SEO methods for modifying web documents to increase their visibility in CSE responses. However, they are often tested only for a limited breadth of application domains; we do not know whether certain C-SEO methods would be effective for a broad range of domains. Moreover, existing evaluations consider only a single-actor scenario where only one web document adopts a C-SEO method; in reality, multiple players are likely to competitively adopt the cutting-edge C-SEO techniques, drawing an analogy from the dynamics we have seen in SEO.

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.

Clustering-based nearest neighbor search algorithms partition points into shards to form an index, and search only a subset of shards to process a query. Even though search efficacy is heavily influenced by the algorithm that identifies the shards to probe, it has received little attention in the literature. We study routing in clustering-based maximum inner product search, which includes cosine similarity search. We unpack existing routers and notice the surprising role of optimism. We then take a page from the sequential decision making literature and formalize that insight following the principle of ``optimism in the face of uncertainty.'' In particular, we present a framework that incorporates the moments of the distribution of inner products within each shard to estimate the maximum inner product. We then develop a practical instance of our algorithm that uses only the first two moments to reach the same accuracy as state-of-the-art routers by probing up to $50\%$ fewer points on benchmark datasets without compromising efficiency. Our algorithm is also space-efficient: we design a sketch of the second moment whose size is independent of the number of points and requires $\mathcal{O}(1)$ vectors per shard.

How Much of Data-Center Activism Is Really AI Slop? Anti-AI sentiment is genuine, but its online expression looks stranger and stranger. Americans are wary of AI in general, and they are especially suspicious of the AI data centers that are popping up across the country like enormous mushrooms. A majority do not want a new data center built in their town. Across the country, community groups have organized to protest individual projects, and activists have successfully lobbied local and state politicians to place moratoriums on the facilities' construction.

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Datacentre industry representatives disputed the findings and said the sector boosted the economy. Datacentre industry representatives disputed the findings and said the sector boosted the economy. 'Hidden datacentre tax' costing Irish households millions, report says Datacentres used 22% of country's electricity last year, pushing up household bills, study suggests Thu 28 May 2026 09.01 EDTLast modified on Thu 28 May 2026 09.32 EDT Energy demand by datacentres in Ireland has added hundreds of euros to household electricity bills in a pattern that could be replicated across Europe, according to a report. Ireland's growing number of datacentres last year used 22% of the country's electricity, more than all urban homes combined, according to the Central Statistics Office. The equivalent figure in the US and UK is 6%.

The tech giant says a breakthrough in data-center networking has dramatically accelerated the flow of information through its massive cloud infrastructure. Amazon says it recently achieved a major breakthrough in networking design--and has been quietly deploying the new technology in its data centers since late last year. The company claims it has significantly increased data speeds while reducing energy use, potentially giving the tech giant an edge as companies race to build ever-faster systems in the cloud. The new technology hinges on a "quasi-random" design that combines elements of traditional, structured data networks with the performance advantages of more random architectures. Researchers have explored random networks for decades, but the technology has never been successfully scaled.

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.

Selecting a finite dictionary of observables whose span is Koopman-invariant is a central challenge in data-driven Koopman operator approximation. We address this problem by exploiting zero-block structure in Extended Dynamic Mode Decomposition (EDMD) matrices. We show that any sub-dictionary whose span is Koopman-invariant induces an exact zero block in the EDMD matrix, even for finite data. We then show that such blocks can be detected by applying PageRank to a row-normalized EDMD matrix constructed from a large initial dictionary. The theory extends to approximately invariant subspaces and yields stronger guarantees for personalized PageRank (PPR) when the seed observables lie inside the target block and reach all observables in that block. Combining EDMD concentration bounds with PageRank perturbation theory gives end-to-end detection guarantees with $O(1/\sqrt{M})$ finite-sample scaling and explicit constants. More generally, without assuming an invariant subspace exists, high PPR mass on a sub-dictionary controls discounted multi-step leakage from the seed observables. Numerical experiments on the Duffing oscillator, Van der Pol oscillator, Lorenz system, and a three-well Ramachandran potential suggest that the method identifies compact, interpretable dictionaries with accurate predictions.

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