【标题】ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING FOR MATERIALS PANEL AT IMAT 2024
【参考中译】IMAT 2024年材料小组的人工智能和机器学习
【类型】 期刊
【作者】 David Furrer; Seth Kimble; James E. Saal; Joshua Stuckner; S. Mohadeseh Taheri-Mousavi
【摘要】 Representatives from industry, government, and academia join a panel discussion on the potential that artificial intelligence and machine learning offer to the materials science and manufacturing communities. A panel session on "Artificial Intelligence and Machine Learning for Materials" was held at the International Materials, Applications & Technologies (IMAT) 2024 conference on October 2 in Cleveland. Moderated by David Furrer, FASM, the event offered an opportunity for attendees to better understand the potential of machine learning (ML) and artificial intelligence (AI) and how to begin incorporating these tools into their own work. The panel provided insights into 1) current applications and benefits of AI/ML in materials and manufacturing, 2) future opportunities in this field, and 3) guidance for individuals and organizations looking to adopt these technologies.
【参考中译】 来自工业界、政府和学术界的代表参加了一场小组讨论，讨论人工智能和机器学习为材料科学和制造界提供的潜力。10月2日在克利夫兰举行的2024年国际材料、应用与技术（IMAT）会议上举行了一场关于“材料人工智能和机器学习”的小组会议。该活动由FASM的David Furrer主持，为与会者提供了一个更好地了解机器学习（ML）和人工智能（AI）的潜力以及如何开始将这些工具融入到自己的工作中的机会。该小组就以下问题提供了见解：1）人工智能/ML在材料和制造中的当前应用和好处，2）该领域的未来机遇，以及3）为希望采用这些技术的个人和组织提供指导。
【来源】 Advanced Materials & Processes 2024, vol.182, no.8
【入库时间】 2025/4/2
【标题】Analyzing the Use of Chat Generative Pre-Trained Transformer and Artificial Intelligence
【参考中译】分析聊天生成预训练Transformer和人工智能的使用
【关键词】 Artificial intelligence; ChatGPT; Ethical considerations; Natural language processing; Transformer algorithm
【参考中译】 人工智能; ChatGPT;伦理考虑;自然语言处理; Transformer算法
【作者】 Henoch Juli Christanto; Christine Dewi; Stephen Aprius Sutresno; Andri Dayarana K. Silalahi
【摘要】 This paper introduces the concepts of Chat Generative Pre-Trained Transformer (GPT) and artificial intelligence (AI). Chat GPT utilizes the GPT language model, which is trained using deep learning techniques and the transformer algorithm. It leverages the transformer's ability to understand human language and generate natural responses in conversations. ChatGPT is utilized in various contexts such as virtual assistants, chatbots, and interactive platforms to improve user interactions with technology. Our efforts also explore the wider domain of artificial intelligence, encompassing machine learning, deep learning, and natural language processing. The advancements in artificial intelligence (AI) technology have had a significant impact on various industries. The study emphasizes the significance of ongoing enhancement, safeguarding, confidentiality, and ethical deliberations in the creation and implementation of ChatGPT and AI chatbots. Ongoing research endeavors to improve the dependability and credibility of AI chatbot systems, despite obstacles such as bias and comprehensibility AI chatbots, can facilitate tailored and efficient human-machine interactions by giving priority to ethical considerations and promoting collaboration. In contemporary research initiatives, the integration of ChatGPT and AI technologies is of great significance, as it presents unique prospects for exploration and invention. ChatGPT, due to its capacity to understand and produce written content, functions as a potent instrument for enhancing communication, resolving issues, and disseminating knowledge in several fields. Hence, it is imperative for researchers to fully grasp the capabilities and consequences of AI, particularly on platforms like ChatGPT, to optimally harness the entire potential of these technologies in their respective fields.
【参考中译】 本文介绍了聊天生成预训练Transformer（GPT）和人工智能（AI）的概念。Chat GPT利用GPT语言模型，该模型是使用深度学习技术和Transformer算法训练的。它利用变形者理解人类语言并在对话中产生自然反应的能力。ChatGPT用于虚拟助理、聊天机器人和交互平台等各种环境中，以改善用户与技术的交互。我们还努力探索更广泛的人工智能领域，包括机器学习，深度学习和自然语言处理。人工智能（AI）技术的进步对各个行业产生了重大影响。该研究强调了在创建和实施ChatGPT和AI聊天机器人过程中持续增强、保护、保密和道德考虑的重要性。正在进行的研究努力提高人工智能聊天机器人系统的可靠性和可信度，尽管存在偏见和可理解性等障碍，人工智能聊天机器人可以通过优先考虑道德因素和促进协作来促进量身定制和高效的人机交互。在当代研究计划中，ChatGPT和AI技术的整合具有重要意义，因为它为探索和发明提供了独特的前景。ChatGPT由于其理解和生成书面内容的能力，可以作为加强沟通，解决问题和传播多个领域知识的有力工具。因此，研究人员必须充分掌握人工智能的能力和后果，特别是在ChatGPT等平台上，以最佳方式利用这些技术在各自领域的全部潜力。
【来源】 Revue d'Intelligence Artificielle 2024, vol.38, no.4
【标题】A Systematic Review on Artificial Intelligence in Orthopedic Surgery
【参考中译】人工智能在骨科手术中应用的系统评价
【关键词】 Artificial intelligence; Deep learning; Machine learning; Generative adversarial network; Convolutional neural network; Orthopedic; Anomaly diagnosis; Medical image
【参考中译】 人工智能;深度学习;机器学习;生成对抗网络;卷积神经网络;骨科;异常诊断;医学图像
【作者】 Nabila Ounasser; Maryem Rhanoui; Mounia Mikram; Bouchra El Asri
【摘要】 This systematic review aims to assess the efficacy of Artificial Intelligence (AI) applications in orthopedic surgery, with a focus on diagnostic accuracy and outcome prediction. In this review, we expose the findings of a systematic literature review awning the papers published from 2016 to October 2023 where authors worked on the application of an AI techniques and methods to an orthopedic purpose or problem. After application of inclusion and exclusion criteria on the extracted papers from PubMed and Google Scholar databases, 75 studies were included in this review. We examined, screened, and analyzed their content according to PRISMA guidelines. We also extracted data about the study design, the datasets included in the experiment, the reported performance measures and the results obtained. In this report, we will share the results of our survey by outlining the key machine and Deep Learning (DL) techniques, such as Convolutional Neural Network (CNN), Autoencoders and Generative Adversarial Network, that were mentioned, the various application domains in orthopedics, the type of source data and its modality, as well as the overall quality of their predictive capabilities. We aim to describe the content of the articles in detail and provide insights into the most notable trends and patterns observed in the survey data.
【参考中译】 这项系统性综述旨在评估人工智能（AI）在骨科手术中应用的有效性，重点关注诊断准确性和结果预测。在这篇评论中，我们揭示了系统性文献评论的结果，涵盖了2016年至2023年10月发表的论文，其中作者致力于将人工智能技术和方法应用于骨科目的或问题。对从PubMed和Google Scholar数据库中提取的论文应用纳入和排除标准后，本综述纳入了75项研究。我们根据PRISMA指南检查、筛选和分析了他们的内容。我们还提取了有关研究设计、实验中包含的数据集、报告的性能指标和获得的结果的数据。在本报告中，我们将通过概述所提到的关键机器和深度学习（DL）技术（例如卷积神经网络（CNN）、自动编码器和生成对抗网络）、骨科中的各个应用领域、源数据类型及其形式以及预测能力的整体质量来分享我们的调查结果。我们的目标是详细描述文章的内容，并深入了解调查数据中观察到的最显着的趋势和模式。