【标题】RECOMMENDED READING: "The Rise of Artificial Intelligence: Will robots actually replace people?"
【参考中译】推荐读物：“人工智能的崛起：机器人真的会取代人吗？"
【类型】 期刊
【作者】 Ashley Stahl
【摘要】 In this 2022 article for Forbes, Stahl states robots and AI are expected to be integrated into our daily lives by 2025, impacting industries like healthcare, customer service, and logistics. Research reveals conflicting views on job displacement, with around half of experts anticipating a rise in income disparity as a result of the automation of numerous jobs. The other half believes, like the Industrial Revolution, robotics and AI will spur the development of new industries and jobs.
【参考中译】 斯塔尔在2022年为福布斯撰写的这篇文章中表示，到2025年，机器人和人工智能预计将融入我们的日常生活，影响医疗保健、客户服务和物流等行业。研究揭示了人们对失业问题的相互矛盾的看法，大约一半的专家预计，由于大量工作的自动化，收入差距将加剧。另一半人认为，就像工业革命一样，机器人和人工智能将刺激新产业和就业机会的发展。
【来源】 XRDS: Crossroads 2023, vol.30, no.1
【入库时间】 2024/5/31
【标题】RESEARCH PAPERS: "Exploring the Impact of Artificial Intelligence and Robots on Higher Education Through Literature-based Design Fictions"
【参考中译】研究论文：“通过基于文献的设计小说探索人工智能和机器人对高等教育的影响”
【作者】 A. M. Cox
【摘要】 The potential long-term effects of robotics and AI on higher education are covered in this paper. It explores the use of design fictions, which are imaginative narratives depicting future scenarios involving AI and robots in learning, administration, and research. The paper offers eight design fictions that offer various viewpoints and debate angles on AI. The author highlights the need for additional research and advancement in this sector by acknowledging the shortcomings and gaps in the fictions.
【参考中译】 本文涵盖了机器人和人工智能对高等教育的潜在长期影响。它探讨了设计小说的使用，这些小说是一种富有想象力的叙述，描绘了在学习、管理和研究中涉及人工智能和机器人的未来场景。该论文提供了八部设计小说，提供了有关人工智能的各种观点和辩论角度。作者承认小说中的缺点和差距，强调了该领域需要进行额外研究和进步。
【标题】Tracking and predicting technological knowledge interactions between artificial intelligence and wind power: Multimethod patent analysis
【参考中译】跟踪和预测人工智能与风电之间的技术知识互动：多方法专利分析
【关键词】 Wind power; Artificial intelligence; Patent data analysis; Co-occurrence network analysis; Link prediction
【参考中译】 风电;人工智能;专利数据分析;共生网络分析;环节预测
【作者】 Jinfeng Wang; Lu Cheng; Lijie Feng; Kuo-Yi Lin; Luyao Zhang; Weiyu Zhao
【摘要】 To track the dynamics of AI and wind power technology knowledge interaction and predict future interaction directions, this study proposes a multiview and multilayer patent analysis framework based on three data-driven methods: DMC co-occurrence networks, LDA, and link prediction. The framework is applied to collate and analyse patents related to wind power technologies using artificial intelligence from 2010 to 2021. We find that the number of AI and wind power technology knowledge interactions increases significantly over time, but the network is sparse overall and still has much room for improvement. Second, the AI and wind power technology knowledge interaction patterns show a shift from machine learning models (generation-side wind power technology) to deep learning models (generation-side and transmission- and distribution-side wind power technology) to hybrid AI models (generation, transmission, distribution, and power consumption in the whole process of wind power technology). Finally, possible future directions of interaction between AI and wind power are predicted. The proposed framework is expected to yield a new empirical perspective on green energy technology development. Additionally, the obtained results provide a comprehensive understanding of AI application research in wind power generation.
【参考中译】 为了跟踪人工智能和风电技术知识交互的动态，预测未来交互的方向，本研究提出了一个基于DMC共现网络、LDA和链接预测三种数据驱动方法的多视图、多层次专利分析框架。该框架用于整理和分析2010年至2021年与使用人工智能的风电技术相关的专利。我们发现，随着时间的推移，AI和风电技术知识互动的数量大幅增加，但网络总体上是稀疏的，仍有很大的改进空间。第二，AI与风电技术知识交互模式呈现出从机器学习模型(发电侧风电技术)向深度学习模型(发电侧和输变电侧风电技术)向混合型AI模型(风电技术全过程发、输、配、耗)的转变。最后，对未来人工智能与风电互动的可能方向进行了展望。预计拟议的框架将为绿色能源技术发展提供一个新的经验视角。此外，所获得的结果为人工智能在风力发电中的应用研究提供了全面的理解。
【来源】 Advanced engineering informatics 2023, vol.58