Could AI Data Centers Be Moved to Outer Space? Massive data centers for generative AI are bad for the Earth. Data centers are being built at a frantic pace all over the world, driven by the AI boom. These facilities consume staggering amounts of electricity. By 2028, AI servers alone may use as much energy as 22 percent of US households.

Vision-Language Models (VLMs) such as CLIP have yielded unprecedented performances for zero-shot image classification, yet their generalization capability may still be seriously challenged when confronted to domain shifts.

Large language model (LLM) queries are predominantly processed by frontier models in centralized cloud infrastructure. Rapidly growing demand strains this paradigm, and cloud providers struggle to scale infrastructure at pace. Two advances enable us to rethink this paradigm: small LMs (<=20B active parameters) now achieve competitive performance to frontier models on many tasks, and local accelerators (e.g., Apple M4 Max) run these models at interactive latencies. This raises the question: can local inference viably redistribute demand from centralized infrastructure? Answering this requires measuring whether local LMs can accurately answer real-world queries and whether they can do so efficiently enough to be practical on power-constrained devices (i.e., laptops). We propose intelligence per watt (IPW), task accuracy divided by unit of power, as a metric for assessing capability and efficiency of local inference across model-accelerator pairs. We conduct a large-scale empirical study across 20+ state-of-the-art local LMs, 8 accelerators, and a representative subset of LLM traffic: 1M real-world single-turn chat and reasoning queries. For each query, we measure accuracy, energy, latency, and power. Our analysis reveals $3$ findings. First, local LMs can accurately answer 88.7% of single-turn chat and reasoning queries with accuracy varying by domain. Second, from 2023-2025, IPW improved 5.3x and local query coverage rose from 23.2% to 71.3%. Third, local accelerators achieve at least 1.4x lower IPW than cloud accelerators running identical models, revealing significant headroom for optimization. These findings demonstrate that local inference can meaningfully redistribute demand from centralized infrastructure, with IPW serving as the critical metric for tracking this transition. We release our IPW profiling harness for systematic intelligence-per-watt benchmarking.

These innovative pavings convert the kinetic energy of footsteps into clean electric energy. We walk here, we walk there, we walk everywhere. Maybe you're headed to work or to lunch in a busy city. You're expending energy, and the exercise is good for you. But what if, on top of that, we could recapture all that freely supplied energy and convert it to usable electricity?

Amazon Prime Day 2025 is fast approaching, and the sale is already underway on some items. To help you find the best early Prime Day deals, we've scoured Amazon for deals on the tech we love. As always, every deal we recommend here is on a product our reviewers have personally tested and approved--you won't find any shoddy dupes or mystery brands here. This year Prime Day runs for four days, July 8-11, rather than the usual two. That means there's twice as long to suffer save. Be sure to read our explainer on all the Amazon Prime perks you should be taking advantage of.

Amazon Prime Day 2025 is fast approaching, and the sale is already underway on some items. To help you find the best early Prime Day deals, we've scoured Amazon for deals on the tech we love. As always, every deal we recommend here is on a product our reviewers have personally tested and approved--you won't find any shoddy dupes or mystery brands here. This year Prime Day runs for four days, July 8-11, rather than the usual two. That means there's twice as long to suffer save.

Vision-Language Models (VLMs) such as CLIP have yielded unprecedented performances for zero-shot image classification, yet their generalization capability may still be seriously challenged when confronted to domain shifts. Our method employs a diverse set of templates for text prompts, augmenting the existing framework of CLIP. Predictions are utilized as pseudo labels for model updates, followed by weight averaging to consolidate the learned information globally. Furthermore, we introduce a text ensemble strategy, enhancing the overall test performance by aggregating diverse textual cues.Our findings underscore the effectiveness of WATT across diverse datasets, including CIFAR-10-C, CIFAR-10.1, Notably, these enhancements are achieved without the need for additional model transformations or trainable modules.

The widespread adoption of IoT has driven the development of cyber-physical systems (CPS) in industrial environments, leveraging Industrial IoTs (IIoTs) to automate manufacturing processes and enhance productivity. The transition to autonomous systems introduces significant operational costs, particularly in terms of energy consumption. Accurate modeling and prediction of IIoT energy requirements are critical, but traditional physics- and engineering-based approaches often fall short in addressing these challenges comprehensively. In this paper, we propose a novel methodology for benchmarking and analyzing IIoT devices and applications to uncover insights into their power demands, energy consumption, and performance. To demonstrate this methodology, we develop a comprehensive framework and apply it to study an industrial CPS comprising an educational robotic arm, a conveyor belt, a smart camera, and a compute node. By creating micro-benchmarks and an end-to-end application within this framework, we create an extensive performance and power consumption dataset, which we use to train and analyze ML models for predicting energy usage from features of the application and the CPS system. The proposed methodology and framework provide valuable insights into the energy dynamics of industrial CPS, offering practical implications for researchers and practitioners aiming to enhance the efficiency and sustainability of IIoT-driven automation.

Black Friday is the big day for retailers slashing prices to kick off the holiday shopping season and clear out their 2024 stock. There's still a week until the big day but you don't have to wait to carve the Thanksgiving turkey and watch the Cowboys lose to snag discounts, because the best early Black Friday deals are live already. The WIRED team boasts decades of experience in product testing and a nose for sniffing out the best deals using a suite of price-tracking tools. For Black Friday, we cross-reference our buying guide recommendations with the latest sale prices to find the best early Black Friday deals on gadgets and gizmos worth owning. Someone from the WIRED Reviews team has tested every product we include in our deals coverage, so you can rest easy knowing we don't highlight low prices on low-quality goods. We strive to find deals at their best price ever, or very close to it (some match previous discounts, but we have never seen them lower unless stated). Updated November 22, 2024: We added deals on devices from PlayShifu, Shargeek, Anker, Mill, CrunchCup, Eve, Govee, Coway, Bosch, Arlo, Eufy, Google, TP-Link, Asus, Amazon, Tymo, Bellissima, CHI, R Co, Paul Mitchell, Arcade1Up, 8BitDo, Rayban, State Bags, reorganized the categories, removed discontinued deals, and updated prices. Get best-in-class reporting that's too important to ignore for just 2.50 1 per month for 1 year. Includes unlimited digital access and exclusive subscriber-only content. Samsung's second-tier OLED (9/10, WIRED Recommends) is one of WIRED reviewer Ryan Waniata's favorite TVs of the year. This QD-OLED panel boasts fabulously rich colors, can get slightly brighter than the LG C4, and has the perfect black levels and excellent picture quality from any angle that make OLED the dominant TV tech today. This TV has HDMI 2.1 support across all four inputs, and Samsung's Game Hub lets you stream from loads of services, including Xbox. The only obvious downside is the lack of Dolby Vision.