Navigating unknown three-dimensional (3D) rugged environments is challenging for multi-robot systems. Traditional discrete systems struggle with rough terrain due to limited individual mobility, while modular systems--where rigid, controllable constraints link robot units--improve traversal but suffer from high control complexity and reduced flexibility. To address these limitations, we propose the Multi-Robot System with Controllable Weak Constraints (MRS-CWC), where robot units are connected by constraints with dynamically adjustable stiffness. This adaptive mechanism softens or stiffens in real-time during environmental interactions, ensuring a balance between flexibility and mobility. We formulate the system's dynamics and control model and evaluate MRS-CWC against six baseline methods and an ablation variant in a benchmark dataset with 100 different simulation terrains. Results show that MRS-CWC achieves the highest navigation completion rate and ranks second in success rate, efficiency, and energy cost in the highly rugged terrain group, outperforming all baseline methods without relying on environmental modeling, path planning, or complex control. Even where MRS-CWC ranks second, its performance is only slightly behind a more complex ablation variant with environmental modeling and path planning. Finally, we develop a physical prototype and validate its feasibility in a constructed rugged environment. For videos, simulation benchmarks, and code, please visit https://wyd0817.github.io/project-mrs-cwc/.

This paper presents a novel data-driven approach to mitigating employee attrition using machine learning and data engineering techniques. The proposed framework integrates data from various human resources systems and leverages advanced feature engineering to capture a comprehensive set of factors influencing attrition. The study outlines a robust modeling approach that addresses challenges such as imbalanced datasets, categorical data handling, and model interpretation. The methodology includes careful consideration of training and testing strategies, baseline model establishment, and the development of calibrated predictive models. The research emphasizes the importance of model interpretation using techniques like SHAP values to provide actionable insights for organizations. Key design choices in algorithm selection, hyperparameter tuning, and probability calibration are discussed. This approach enables organizations to proactively identify attrition risks and develop targeted retention strategies, ultimately redu

A 2021 paper in Nature by Mirhoseini et al.30 about the use of reinforcement learning (RL) in the physical design of silicon chips raised eyebrows, drew critical media coverage, and stirred up controversy due to poorly documented claims. The paper, authored by Google researchers, withheld critical methodological steps, and most inputs needed to reproduce its results. Our meta-analysis shows how two separate evaluations filled in the gaps and demonstrated that Google RL lags behind human chip designers, a well-known algorithm (simulated annealing), and generally available commercial software, while also being slower. Crosschecked data indicates that the integrity of the Nature paper is substantially undermined, owing to errors in conduct, analysis, and reporting. Before publishing, Google rebuffed internal allegations of fraud which still stand.

This paper presents a theoretical framework for an AI-driven data quality monitoring system designed to address the challenges of maintaining data quality in high-volume environments. We examine the limitations of traditional methods in managing the scale, velocity, and variety of big data and propose a conceptual approach leveraging advanced machine learning techniques. Our framework outlines a system architecture that incorporates anomaly detection, classification, and predictive analytics for real-time, scalable data quality management. Key components include an intelligent data ingestion layer, adaptive preprocessing mechanisms, context-aware feature extraction, and AI-based quality assessment modules. A continuous learning paradigm is central to our framework, ensuring adaptability to evolving data patterns and quality requirements. We also address implications for scalability, privacy, and integration within existing data ecosystems. While practical results are not provided, it lays a robust theoretical foundation for future research and implementations, advancing data quality management and encouraging the exploration of AI-driven solutions in dynamic environments.

Medicaid is a federal-state program that provides healthcare to over 80 million low-income Americans, including pregnant women, children, and individuals with disabilities. Up against a host of problems, including rising healthcare costs, disparity in access, and the management of chronic conditions among at-risk groups, Medicaid is one of the biggest healthcare payers in the U.S. Just as Medicare does, the use of Artificial Intelligence (AI) offers a major opportunity to change the delivery of care and operational efficiency in Medicaid [1] [16]. While there has been extensive conversation about AI in Medicare, the unique population and requirements of Medicaid require customized AI applications [1]. Chronic disease management, improving admin tasks, and a reduction in costs are amongst the ways AI tools can help, especially by focusing on social determinants of health (SDOH) that are important for Medicaid populations. The study will assess the ability of AI-enabled systems to reinforce Medicaid in handling its particular challenges while facilitating fair and quality care for its entire population of beneficiaries [8] [9].

Common problems in playing online mobile and computer games were related to toxic behavior and abusive communication among players. Based on different reports and studies, the study also discusses the impact of online hate speech and toxicity on players' in-game performance and overall well-being. This study investigates the capability of pre-trained language models to classify or detect trash talk or toxic in-game messages The study employs and evaluates the performance of pre-trained BERT and GPT language models in detecting toxicity within in-game chats. Using publicly available APIs, in-game chat data from DOTA 2 game matches were collected, processed, reviewed, and labeled as non-toxic, mild (toxicity), and toxic. The study was able to collect around two thousand in-game chats to train and test BERT (Base-uncased), BERT (Large-uncased), and GPT-3 models. Based on the three models' state-of-the-art performance, this study concludes pre-trained language models' promising potential for addressing online hate speech and in-game insulting trash talk.

Fox News correspondent David Spunt has the latest on questions over whether the former president can hold office again on Special Report. A liberal reporter added fuel to online fire that a conservative news outlet was duped by a former President Trump impersonator, or even artificial intelligence – resulting in an embarrassing editor's note. Last week, Trump called into right-wing channel Real America's Voice for an interview that resulted in online speculation that the outlet had spoken with an impostor. Audio was shaky, and speculation erupted that Trump either had a cold, poor service or something more malicious, such as someone impersonating the 45th president, or modern technology generating the interview with old clips of Trump. Zachary Petrizzo, a politics reporter for the left-wing Daily Beast, took things a step further and reported that Real America's Voice owner Robert Sigg told him that the company would investigate whether the call was some sort of prank.

A year of incredible science news was complemented with wide-ranging commentary at Scientific American. Our opinion section featured some of the best and brightest minds, taking us to the front lines of COVID, teaching us about the many fraught Supreme Court decisions involving science and evidence, and more. We learned, for example, about the pitfalls of artificial intelligence, how racists misuse evolutionary biology, and how our children's troubled mental health is another ongoing epidemic. Whether they were thought-provoking, deeply moving or challenged long-held beliefs, here are some of our editors' favorite opinion articles of 2022. This year, language models proved they can write humanlike text, with one AI chatbot generating such impressive responses that it convinced an engineer it was sentient.

While 2022 may not have enjoyed as many AAA releases as in past years, the ones that weren't delayed into 2023 were stellar and the indie development scene more than made up for the lack of big-budget titles. Some of our favorite releases this year came from small, ambitious teams that delivered fresh ideas. As is tradition, the Engadget team came together to extol the virtues of our favorite releases from the past 12 months. Bayonetta 3 is a delicious amplification of the series' most ridiculous themes. It indulges in absurdity without disrupting the rapid-fire combat or Bayonetta's unrivaled sense of fashion and wit. Bayonetta 3 is joyful, mechanically rich and full of action, plus it allows players to transform into a literal hell train in order to take down massive beasts bent on destroying the multiverse. The Bayonetta series just keeps getting weirder, but that doesn't mean it's losing its sense of satisfying gameplay along the way. In the franchise's third installment, Bayonetta is powerful, confident and funny; she's a drag queen in a universe loosely held together by witchcraft, and the chaos of this combination is truly magical. Sure, you've played Animal Crossing, Stardew Valley, Hades and The Binding of Isaac – but what if you could play all of them at once, in a single adorable demonic package? Cult of the Lamb is part social and farming simulator, part dungeon-crawling roguelike and all-around fantastic. After being sacrificed and resurrected, you're instructed by a grand, dark deity to start your own cult, managing worship services, agriculture, cooking, marriages, deaths and much more.

We may not have had quite as much unfettered reading time as we did in the lockdown days of the COVID pandemic, but Engadget's editors have still managed to pick out, peruse and ponder a broad variety of this year's most intriguing books. Whether we learned how to wield a wok, listened to life lessons from Hideo Kojima, or dove into the seedy underbelly of an alt-universe 1940's San Francisco, here are a few of our favorites from 2022. Classic noir cinema was a staple in my house growing up -- I mean, my first celebrity crush was on The Thin Man series co-star, Myrna Loy -- so any story from the days when mugs were mooks and gals were dames holds sway over my heart. But The Thin Man, like the rest of the media made at that time, only showed a very narrow, very male, very white view of life. Christopher Moore's latest novel, Razzmatazz, adds some much needed color to the otherwise black-and-white world of noir.