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    Reports Outline Machine Translation Findings from Telecommunications Institute (Hallucinations In Large Multilingual Translation Models)

    19-20页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Investigators discuss new findings in Machine Translation. According to news originating from Lisbon, Portugal, by NewsRx correspondents, research stated, "Hallucinated translations can severely undermine and raise safety issues when machine translation systems are deployed in the wild. Previous research on the topic focused on small bilingual models trained on high-resource languages, leaving a gap in our understanding of hallucinations in multilingual models across diverse translation scenarios." Funders for this research include European Research Council (ERC), European Union (EU), Horizon Europe Guarantee, Fundacao para a Ciencia e a Tecnologia (FCT), MAIA, NextGenAI, GENCI-IDRIS. Our news journalists obtained a quote from the research from Telecommunications Institute, "In this work, we fill this gap by conducting a comprehensive analysis-over 100 language pairs across various resource levels and going beyond English-centric directions-on both the M2M neural machine translation (NMT) models and GPT large language models (LLMs). Among several insights, we highlight that models struggle with hallucinations primarily in low-resource directions and when translating out of English, where, critically, they may reveal toxic patterns that can be traced back to the training data. We also find that LLMs produce qualitatively different hallucinations to those of NMT models. Finally, we show that hallucinations are hard to reverse by merely scaling models trained with the same data."
      原文链接:
    • NETL
    • NSTL

    Reports Summarize Artificial Intelligence Study Results from National Taiwan University of Science and Technology (Real-Time Salt Contamination Monitoring System and Method for Transmission Line Insulator Based on Artificial Intelligence)

    20-21页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Research findings on artificial intelligence are discussed in a new report. According to news originating from Taipei, Taiwan, by NewsRx correspondents, research stated, "Insulators on overhead power lines have long been exposed to the outdoors and are susceptible to pollution and salt contamination. Due to factors such as wind and gravity, pollution in the atmosphere gradually deposits on the surface of the insulator." The news journalists obtained a quote from the research from National Taiwan University of Science and Technology: "In humid and windy conditions, conductive pollutants begin to dissolve in the water on the surface of the insulator, increasing the leakage current and affecting insulation performance. This study mainly uses a data acquisition system to measure the leakage current of the insulator and weather parameters (including temperature, relative humidity, pressure, wind speed, and ultraviolet) around the insulator. Artificial intelligence is then applied to establish a prediction model for leakage current based on weather parameters. The established model accurately predicts insulator leakage current through weather parameters. In order to observe the real-time status of the insulator, this study establishes a monitoring platform that integrates the predicted leakage current with weather parameters. It allows users or maintenance personnel to connect to the server through the network to observe the predicted results and weather parameters."
      原文链接:
    • NETL
    • NSTL

    Research from Polytechnic Institute of Coimbra Provide New Insights into Robotics (Robots for Forest Maintenance)

    21-22页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Investigators publish new report on robotics. According to news reporting originating from Coimbra, Portugal, by NewsRx correspondents, research stated, "Forest fires are becoming increasingly common, and they are devastating, fueled by the effects of global warming, such as a dryer climate, dryer vegetation, and higher temperatures." Funders for this research include European Funds; Fct; Polytechnic Institute of Coimbra.
      原文链接:
    • NETL
    • NSTL

    Researchers at Hanoi National University of Education Have Reported New Data on Machine Learning (Classifying Forest Cover and Mapping Forest Fire Susceptibility In Dak Nong Province, Vietnam Utilizing Remote Sensing and Machine Learning)

    22-23页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Investigators publish new report on Machine Learning. According to news reporting from Hanoi, Vietnam, by NewsRx journalists, research stated, "Forest fires can cause significant harm to the biodiversity, air quality, economy, and industries that depend on forests, which is particularly critical given the current climate change scenario. Therefore, it is crucial to predict potential fire risks, especially in Dak Nong, a province located in the highland region of Vietnam, where forest farming is prevalent." Financial support for this research came from Van Lang University.
      原文链接:
    • NETL
    • NSTL

    Findings from University of New South Wales Advance Knowledge in Artificial Intelligence (Novel artificial intelligence-based hypodensity detection tool improves clinician identification of hypodensity on non-contrast computed tomography in ...)

    23-24页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Researchers detail new data in artificial intelligence. According to news reporting from Kensington, Australia, by NewsRx journalists, research stated, "IntroductionIn acute stroke, identifying early changes (parenchymal hypodensity) on non-contrast CT (NCCT) can be challenging. We aimed to identify whether the accuracy of clinicians in detecting acute hypodensity in ischaemic stroke patients on a non-contrast CT is improved with the use of an Artificial Intelligence (AI) based, automated hypodensity detection algorithm (HDT) using MRI-DWI as the gold standard." The news correspondents obtained a quote from the research from University of New South Wales: "MethodsThe study employed a case-crossover within-clinician design, where 32 clinicians were tasked with identifying hypodensity lesions on NCCT scans for five a priori selected patient cases, before and after viewing the AI-based HDT. The DICE similarity coefficient (DICE score) was the primary measure of accuracy. Statistical analysis compared DICE scores with and without AI-based HDT using mixed-effects linear regression, with individual NCCT scans and clinicians as nested random effects. ResultsThe AI-based HDT had a mean DICE score of 0.62 for detecting hypodensity across all NCCT scans. Clinicians' overall mean DICE score was 0.33 (SD 0.31) before AI-based HDT implementation and 0.40 (SD 0.27) after implementation. AI-based HDT use was associated with an increase of 0.07 (95% CI: 0.02-0.11, p = 0.003) in DICE score accounting for individual scan and clinician effects. For scans with small lesions, clinicians achieved a mean increase in DICE score of 0.08 (95% CI: 0.02, 0.13, p = 0.004) following AIbased HDT use. In a subgroup of 15 trainees, DICE score improved with AI-based HDT implementation [mean difference in DICE 0.09 (95% CI: 0.03, 0.14, p = 0.004)]."
      原文链接:
    • NETL
    • NSTL

    Nanjing Tech University Reports Findings in Machine Learning (Computational approach inspired advancements of solid-state electrolytes for lithium secondary batteries: from first-principles to machine learning)

    24-25页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Machine Learning is the subject of a report. According to news reporting out of Nanjing, People's Republic of China, by NewsRx editors, research stated, "The increasing demand for high-security, high-performance, and low-cost energy storage systems (EESs) driven by the adoption of renewable energy is gradually surpassing the capabilities of commercial lithium-ion batteries (LIBs). Solid-state electrolytes (SSEs), including inorganics, polymers, and composites, have emerged as promising candidates for next-generation all-solid-state batteries (ASSBs)." Financial supporters for this research include National Natural Science Foundation of China, Natural Science Foundation of Anhui Province. Our news journalists obtained a quote from the research from Nanjing Tech University, "ASSBs offer higher theoretical energy densities, improved safety, and extended cyclic stability, making them increasingly popular in academia and industry. However, the commercialization of ASSBs still faces significant challenges, such as unsatisfactory interfacial resistance and rapid dendrite growth. To overcome these problems, a thorough understanding of the complex chemical-electrochemical-mechanical interactions of SSE materials is essential. Recently, computational methods have played a vital role in revealing the fundamental mechanisms associated with SSEs and accelerating their development, ranging from atomistic first-principles calculations, molecular dynamic simulations, multiphysics modeling, to machine learning approaches. These methods enable the prediction of intrinsic properties and interfacial stability, investigation of material degradation, and exploration of topological design, among other factors. In this comprehensive review, we provide an overview of different numerical methods used in SSE research. We discuss the current state of knowledge in numerical auxiliary approaches, with a particular focus on machine learningenabled methods, for the understanding of multiphysics-couplings of SSEs at various spatial and time scales. Additionally, we highlight insights and prospects for SSE advancements."
      原文链接:
    • NETL
    • NSTL

    Study Findings from Suzhou University of Science and Technology Provide New Insights into Robotics (Coverage Path Planning for Cleaning Robot Based On Improved Simulated Annealing Algorithm and Ant Colony Algorithm)

    25-26页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News-Investigators publish new report on Robotics. According to news reporting from Suzhou, People's Republic of China, by NewsRx journalists, research stated, "In the field of intelligent buildings, cleaning robots have always been a part of research. This study proposes a traversal algorithm based on the combination of simulated annealing algorithm based on monotonic heating and ant colony algorithm to solve the problem of coverage path planning during operation." Funders for this research include National Natural Science Foundation of China (NSFC), Postgraduate Research & Practice Innovation Program of Jiangsu Province, Qing Lan Project of Jiangsu, China Postdoctoral Science Foundation, Suzhou Science and Technology Development Plan Project (Key Industry Technology Innovation), Open Project Funding from Anhui Province Key Laboratory of Intelligent Building and Building Energy Saving, Anhui Jianzhu University, Postgraduate Research & Practice Innovation Program of Jiangsu Province.
      原文链接:
    • NETL
    • NSTL

    University of Essex Reports Findings in Machine Learning (Strategies to optimise machine learning classification performance when using biomechanical features)

    26-27页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News-New research on Machine Learning is the subject of a report. According to news reporting originating from Essex, United Kingdom, by NewsRx correspondents, research stated, "Building prediction models using biomechanical features is challenging because such models may require large sample sizes. However, collecting biomechanical data on large sample sizes is logistically very challenging." Our news editors obtained a quote from the research from the University of Essex, "This study aims to investigate if modern machine learning algorithms can help overcome the issue of limited sample sizes on developing prediction models. This was a secondary data analysis two biomechanical datasets-a walking dataset on 2295 participants, and a countermovement jump dataset on 31 participants. The input features were the three-dimensional ground reaction forces (GRFs) of the lower limbs. The outcome was the orthopaedic disease category (healthy, calcaneus, ankle, knee, hip) in the walking dataset, and healthy vs people with patellofemoral pain syndrome in the jump dataset. Different algorithms were compared: multinomial/LASSO regression, XGBoost, various deep learning time-series algorithms with augmented data, and with transfer learning. For the outcome of weighted multiclass area under the receiver operating curve (AUC) in the walking dataset, the three models with the best performance were InceptionTime with x12 augmented data (0.810), XGBoost (0.804), and multinomial logistic regression (0.800). For the jump dataset, the top three models with the highest AUC were the LASSO (1.00), InceptionTime with x8 augmentation (0.750), and transfer learning (0.653)."
      原文链接:
    • NETL
    • NSTL

    Researchers from Yulin Normal University Discuss Findings in Robotics (The Collaborative Interaction With Pokemon-go Robot Uses Augmented Reality Technology for Increasing the Intentions of Patronizing Hospitality)

    27-28页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Data detailed on Robotics have been presented. According to news reporting originating in Guangxi, People's Republic of China, by NewsRx journalists, research stated, "The aim of the present study is to present a new research model which includes both extrinsic and intrinsic determinants that influence Pokemon-Go robot users' behavioral intention to patronize hospitality firms that draw visitors with Pokemons. Pokemon-Go Robot uses Augmented Reality technology in this study." Funders for this research include course project for thinking about politics by the Teaching Affairs Office of Yulin Normal University in 2020, Yulin Normal University, Natural Science Foundation of Shandong Province. The news reporters obtained a quote from the research from Yulin Normal University, "Survey data collected from 261 usable questionnaires were tested against the research model using the structural equation modeling approach. The results present that all the proposed variables were found to be critical factors significantly influencing Pokemon-Go robot users' patronizing intention. The application area of proposed theoretical model is new; very sparse research has been undertaken on exploring Pokemon-Go robot users' intentions toward visiting hospitality firms attracting guests by virtual monsters."
      原文链接:
    • NETL
    • NSTL

    Researcher from Estonian Literary Museum Describes Findings in Artificial Intelligence [Narrating Artificial Intelligence (AI) in 2023: An Estonian Case Study on AI Lore]

    28-28页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Research findings on artificial intelligence are discussed in a new report. According to news reporting originating from Tartu, Estonia, by NewsRx correspondents, research stated, "With the rapid emergence of new technologies in contemporary societies, new reflections of human experience arise. One of the recent technologies introducing massive changes in human life as well as in human reactions is artificial intelligence (AI)." Our news editors obtained a quote from the research from Estonian Literary Museum: "In the spring of 2023, about six months after the text-based AI ChatGPT was made available to the masses (Pistilli 2023), the public discussion about AI was most intense in Estonian society. The aim of this article is to analyse the narrative motifs and ways of narrating (including a proposed typology) AI-related topics in the Estonian mainstream and alternative online media chat groups in May and September 2023 respectively. Six general types of narration on AI were detected. The collected material showed that Estonian AI lore is considerably polarised between strongly negative and positive stories. Especially during the first wave of AI discussion in May 2023 the majority of narratives had negative or doubtful tonality. One of the factors that triggers a negative or cautionary tone in AI lore seems to be the lack of transparency in AI development."
      原文链接:
    • NETL
    • NSTL