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.

A new study finds algae, mussels, and starfish flock to munitions dumped in the Baltic Sea. Breakthroughs, discoveries, and DIY tips sent every weekday. As the ink dried on Germany's unconditional surrender on May 8, 1945, celebrations erupted across the world. People cheered, wept, and kissed in the streets as World War II finally came to an end in Europe. A few months later at the Potsdam Conference, Germany agreed to demilitarize and dismantle its once formidable army, leaving the nation with lots and lots of leftover munitions.

Mammotion announced a new robot mower navigation platform ahead of the IFA trade show in Berlin on Thursday: Its Tri-Fusion Positioning System combines LiDAR, Real-Time Kinematic (RTK), and vision capabilities into a single system. Combining two of these technologies is not uncommon. Older mowers have used a combination of LiDAR and RTK for much of the past half-decade, while combining one or the other with vision navigation has gained favor more recently. And while some robot mowers have all three technologies onboard, they aren't necessarily connected for navigation, and serve other mower operation purposes. Mammotion says Tri-Fusion is the culmination of nine years of development effort, and that it be made available as firmware updates for its recently released Luba Mini AWD LiDAR and the Yuka Mini Vision robot lawn mowers as well as several yet-to-be-announced models.

Maintaining a perfect lawn takes time, effort, and dedication, and mowing the grass can become a relentless, repetitive chore. Not keeping up with maintenance could mean your garden quickly starts to resemble a jungle. Two new robot lawn mowers from eufy hope to change your outlook on garden maintenance. The eufy E15 and E18, available for sale now, are a pair of connected robot lawn mowers. These clever gadgets can take this chore off your hands by bringing smart tech into your yard.

Anker's lifestyle brand Eufy has already swallowed a big chunk of the robot vacuum market and now it's got its sights on your yard. The company has been sharing details of its first two robot mowers since the start of the year, and now they're ready to start selling them. Eufy's E15 and E18 are designed to automate one of the most tedious jobs around the home -- if you're able to pay. I've been testing an E15 for the last few weeks ahead of their retail debut today and I'm fairly impressed. Early robot mowers needed a boundary wire to tell them where they were allowed to mow.

We present DocPuzzle, a rigorously constructed benchmark for evaluating long-context reasoning capabilities in large language models (LLMs). This benchmark comprises 100 expert-level QA problems requiring multi-step reasoning over long real-world documents. To ensure the task quality and complexity, we implement a human-AI collaborative annotation-validation pipeline. DocPuzzle introduces an innovative evaluation framework that mitigates guessing bias through checklist-guided process analysis, establishing new standards for assessing reasoning capacities in LLMs. Our evaluation results show that: 1)Advanced slow-thinking reasoning models like o1-preview(69.7%) and DeepSeek-R1(66.3%) significantly outperform best general instruct models like Claude 3.5 Sonnet(57.7%); 2)Distilled reasoning models like DeepSeek-R1-Distill-Qwen-32B(41.3%) falls far behind the teacher model, suggesting challenges to maintain the generalization of reasoning capabilities relying solely on distillation.

Understanding whether a generated table is of good quality is important to be able to use it in creating or editing documents using automatic methods. In this work, we underline that existing measures for table quality evaluation fail to capture the overall semantics of the tables, and sometimes unfairly penalize good tables and reward bad ones. We propose TabEval, a novel table evaluation strategy that captures table semantics by first breaking down a table into a list of natural language atomic statements and then compares them with ground truth statements using entailment-based measures. To validate our approach, we curate a dataset comprising of text descriptions for 1,250 diverse Wikipedia tables, covering a range of topics and structures, in contrast to the limited scope of existing datasets. We compare TabEval with existing metrics using unsupervised and supervised text-to-table generation methods, demonstrating its stronger correlation with human judgments of table quality across four datasets.

Participatory urban planning is the mainstream of modern urban planning that involves the active engagement of residents. However, the traditional participatory paradigm requires experienced planning experts and is often time-consuming and costly. Fortunately, the emerging Large Language Models (LLMs) have shown considerable ability to simulate human-like agents, which can be used to emulate the participatory process easily. In this work, we introduce an LLM-based multi-agent collaboration framework for participatory urban planning, which can generate land-use plans for urban regions considering the diverse needs of residents. Specifically, we construct LLM agents to simulate a planner and thousands of residents with diverse profiles and backgrounds. We first ask the planner to carry out an initial land-use plan. To deal with the different facilities needs of residents, we initiate a discussion among the residents in each community about the plan, where residents provide feedback based on their profiles. Furthermore, to improve the efficiency of discussion, we adopt a fishbowl discussion mechanism, where part of the residents discuss and the rest of them act as listeners in each round. Finally, we let the planner modify the plan based on residents' feedback. We deploy our method on two real-world regions in Beijing. Experiments show that our method achieves state-of-the-art performance in residents satisfaction and inclusion metrics, and also outperforms human experts in terms of service accessibility and ecology metrics.

Driven by an increasing prevalence of trackers, ever more IoT sensors, and the declining cost of computing power, geospatial information has come to play a pivotal role in contemporary predictive models. While enhancing prognostic performance, geospatial data also has the potential to perpetuate many historical socio-economic patterns, raising concerns about a resurgence of biases and exclusionary practices, with their disproportionate impacts on society. Addressing this, our paper emphasizes the crucial need to identify and rectify such biases and calibration errors in predictive models, particularly as algorithms become more intricate and less interpretable. The increasing granularity of geospatial information further introduces ethical concerns, as choosing different geographical scales may exacerbate disparities akin to redlining and exclusionary zoning. To address these issues, we propose a toolkit for identifying and mitigating biases arising from geospatial data. Extending classical fairness definitions, we incorporate an ordinal regression case with spatial attributes, deviating from the binary classification focus. This extension allows us to gauge disparities stemming from data aggregation levels and advocates for a less interfering correction approach. Illustrating our methodology using a Parisian real estate dataset, we showcase practical applications and scrutinize the implications of choosing geographical aggregation levels for fairness and calibration measures.

Participatory urban planning is the mainstream of modern urban planning and involves the active engagement of different stakeholders. However, the traditional participatory paradigm encounters challenges in time and manpower, while the generative planning tools fail to provide adjustable and inclusive solutions. This research introduces an innovative urban planning approach integrating Large Language Models (LLMs) within the participatory process. The framework, based on the crafted LLM agent, consists of role-play, collaborative generation, and feedback iteration, solving a community-level land-use task catering to 1000 distinct interests. Empirical experiments in diverse urban communities exhibit LLM's adaptability and effectiveness across varied planning scenarios. The results were evaluated on four metrics, surpassing human experts in satisfaction and inclusion, and rivaling state-of-the-art reinforcement learning methods in service and ecology. Further analysis shows the advantage of LLM agents in providing adjustable and inclusive solutions with natural language reasoning and strong scalability. While implementing the recent advancements in emulating human behavior for planning, this work envisions both planners and citizens benefiting from low-cost, efficient LLM agents, which is crucial for enhancing participation and realizing participatory urban planning.