AI4SE和SE4AI:设定人机协同学习的路线图

IF 1 4区 工程技术 Q4 INSTRUMENTS & INSTRUMENTATION
Insight Pub Date : 2023-02-09 DOI:10.1002/inst.12417
Kara Pepe, Nicole Hutchison
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引用次数: 0

摘要

人工智能(AI)和机器学习(ML)技术在系统中变得越来越重要:无论是提供新功能还是在系统工程本身的实践中,特别是随着数字化转型提高了许多常规工程任务的自动化程度。人工智能、机器学习和自主性在复杂和关键系统中的应用鼓励了新的系统工程方法、过程和工具的发展。本文重点介绍了由美国陆军作战能力发展指挥军备中心(CCDC AC)系统工程理事会和系统工程研究中心(SERC)联合举办的一系列研讨会。这些研讨会关注于人工智能和系统工程之间的关系,并从政府、工业和学术界的数百个利益相关者那里获得输入。它们还为SERC的人工智能/自主研究路线图提供了关键方向,因为它着眼于“人机共同学习”的长期成果。虽然这些研讨会以美国为中心,但所获得的经验和见解适用于全球。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
AI4SE and SE4AI: Setting the Roadmap toward Human-Machine Co-Learning

Artificial intelligence (AI) and machine learning (ML) technology are becoming increasingly critical in systems: both to provide new capabilities and in the practice of systems engineering itself, especially as digital transformation improves the automation of many routine engineering tasks. The application of AI, ML, and autonomy to complex and critical systems encourage the development of new systems engineering methods, processes, and tools. This article highlights a series of workshops conducted jointly by the US Army Combat Capabilities Development Command Armaments Center (CCDC AC) Systems Engineering Directorate and the Systems Engineering Research Center (SERC). These workshops focus on the relationships between AI and systems engineering and elicit input from hundreds of stakeholders across government, industry, and academia. They also provide critical direction to the SERC's research roadmap on AI/autonomy as it looks towards the long-term outcome of “human-machine co-learning.” Though the workshops are US-centric, the lessons and insights gained are applicable globally.

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来源期刊
Insight
Insight 工程技术-材料科学:表征与测试
CiteScore
1.50
自引率
9.10%
发文量
0
审稿时长
2.8 months
期刊介绍: Official Journal of The British Institute of Non-Destructive Testing - includes original research and devlopment papers, technical and scientific reviews and case studies in the fields of NDT and CM.
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