W e have all been there: packed shoulder to shoulder on the train while the person beside us coughs and sneezes. A protective flu jab offers some reassurance, but so does the thought that there will be a hospital bed for you, should you need it. Yet, in England, about 95% of adult hospital beds were occupied at the height of flu season last year. If hospitals were hotels, that level of occupancy would be good business, and we would commend them for their efficiency. But do we really want a hospital to have so few beds in reserve that it's just one bad flu week away from having to treat patients in corridors?

Tado is the biggest manufacturer of smart heating controls in Europe, but there are alternatives with similar per-radiator control. Tado is the biggest manufacturer of smart heating controls in Europe, but there are alternatives with similar per-radiator control. We earn a commission if you buy something through an affiliate link. Can AI really save you money on your heating bill? Samuel Gibbs doubted he could cut costs and still keep warm by putting tech in control. C an smart thermostats and AI reduce your heating bills?

With AI inference now driving enterprise workloads, organizations must rethink infrastructure for speed, efficiency, scalability, and performance per watt to unlock AI's real-world potential. The era of AI inference has arrived. Imagine a healthcare system analyzing millions of data points in real time to accelerate life-saving medical research, or an intelligent assistant instantly resolving thousands of complex customer needs at once. These real-world breakthroughs rely on advanced infrastructure acting as the engine of continuous intelligence, powering real-time services while also supporting an increasingly intelligent edge of IoT and consumer devices. However, in this inference-driven landscape, every delay, bottleneck, or wasted watt directly affects human outcomes and operating costs. This shift changes what infrastructure must deliver.

While Dyson isn't officially at IFA 2026, Europe's biggest tech show, it's conveniently hosting its Dyson Unveiled event just down the road in Berlin. After revealing its new toothbrush in Paris, it also had no fewer than 11 more products, covering new air purifiers, robovacs, floor cleaners and haircare. The company's 2026 theme, according to chief engineer Jake Dyson, is more intelligence, whether that's computer vision, AI or machine learning. A good example is the CameraJet's sensors detecting your gumline and aiming waterjets in the right place, or Dyson's new Airsmooth hot brush that measures temperature 100 times a second to reduce hair damage. The new product range is literally dominated by the company's new HushJet Big Quiet Cool Pure air purifier.

Apple just launched the new M5 Ultra for the latest Mac Studio and as you'd expect, it's the fastest consumer processor the company has ever built. But how much speedier is it than its predecessor, the M3 Ultra? Let's dive in and see how Apple's latest super silicon measures up. If you've followed Apple's Ultra chip family so far, you've probably noticed that it has skipped the M2, M4 and M6 generations and only come out as M1 Ultra, M3 Ultra and now M5 Ultra versions. The reason for that (and the delay compared to Apple's other chips) is because of the complexity -- they're effectively two processors mated together using Apple's "Ultrafusion" bridge technology.

Improving energy performance can effectively extend the time a robot can operate and reduce battery load, enabling lighter, more flexible, and more durable robotic systems. Nature has evolved optimal energy-saving locomotion strategies through billions of years of natural selection, providing unparalleled blueprints for robotic optimization. Among diverse modes of aquatic locomotion, intermittent swimming, also called bout-and-glide swimming, is a widespread adaptive behavior in aquatic organisms of a wide range of sizes, including larval zebrafish, red-nose tetra, koi carp, and even whales. This natural bout-and-glide gait features alternating motion phases: short periods of active body and tail undulation for propulsion, followed by passive gliding with a streamlined, straight body posture. It is widely recognized that this intermittent swimming gait is closely associated with optimizing biological energy, making it of great research value to transplant and explore such natural motion mechanisms into robotic control systems.

Large language models (LLMs) have demonstrated remarkable progress in generating functional code, leading to numerous AI-based coding program tools. However, their reliance on the perplexity objective during both training and inference primarily emphasizes functionality, often at the expense of efficiency--an essential consideration for real-world coding tasks. Perhaps interestingly, we observed that well-trained LLMs inherently possess knowledge about code efficiency, but this potential remains underutilized with standard decoding approaches. To address this, we design strategic prompts to activate the model's embedded efficiency understanding, effectively using LLMs as \textit{efficiency critiques} to guide code generation toward higher efficiency without sacrificing--and sometimes even improving--functionality, all without the need for costly real code execution. Extensive experiments on benchmark datasets (EffiBench, HumanEval+) across multiple representative code models demonstrate up to a 70.6\% reduction in average execution time and a 13.6\% decrease in maximum memory usage, highlighting the computational efficiency and practicality of our approach compared to existing alternatives.

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Learning effective visuomotor policies for robotic manipulation is challenging, as it requires generating precise actions while maintaining computational efficiency. Existing methods remain unsatisfactory due to inherent limitations in the essential action representation and the basic network architectures. We observe that representing actions in the frequency domain captures the structured nature of motion more effectively: low-frequency components reflect global movement patterns, while high-frequency components encode fine local details.

Randomized controlled trials (RCTs) often exhibit limited inferential efficiency in estimating treatment effects due to small sample sizes. In recent years, the combination of external controls has gained increasing attention as a means of improving the efficiency of RCTs. However, external controls are not always comparable to RCTs, and direct borrowing without careful evaluation can introduce substantial bias and reduce the efficiency of treatment effect estimation. In this paper, we propose a novel influence-based adaptive sample borrowing approach that effectively quantifies the "comparability" of each sample in the external controls using influence function theory. Given a selected set of borrowed external controls, we further derive a semiparametric efficient estimator under an exchangeability assumption. Recognizing that the exchangeability assumption may not hold for all possible borrowing sets, we conduct a detailed analysis of the asymptotic bias and variance of the proposed estimator under violations of exchangeability. Building on this bias-variance trade-off, we further develop a data-driven approach to select the optimal subset of external controls for borrowing. Extensive simulations and realworld applications demonstrate that the proposed approach significantly enhances treatment effect estimation efficiency in RCTs, outperforming existing approaches.