Transitioning Science to Practice

IF 1 4区 工程技术 Q4 INSTRUMENTS & INSTRUMENTATION Insight Pub Date : 2024-04-11 DOI:10.1002/inst.12485
Stuart D. Harshbarger, Rosa R. Heckle
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引用次数: 0

Abstract

National security challenges require a new approach to collaborative problem solving to address emergent challenges or opportunities. To effectively address these challenges, development of artificial intelligence (AI) technologies including machine learning (ML) and deep learning (DL), is underway. Advancing AI/ML capabilities requires transdisciplinary research encompassing the fusion of technology and emergent scientific discovery. Achieving this requires a departure from traditional research and development (R&D) methods. New development processes need to support the understanding that research progresses iteratively technology insertion is incremental, and the final capability is evolutionary. We propose a novel systems engineering/research model called the vortical model. The vortical model introduces an iterative framework through which emerging advances in research outcomes are effectively demonstrated and validated for integration, as new capabilities, at varying technology insertion points. Our goal is to facilitate the transfer of knowledge from emerging research for swift, effective integration into the organization's mission capabilities.

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将科学转化为实践
国家安全挑战需要一种新的协作解决问题的方法,以应对新出现的挑战或机遇。为了有效应对这些挑战,包括机器学习(ML)和深度学习(DL)在内的人工智能(AI)技术正在发展之中。推进人工智能/ML 能力需要跨学科研究,包括技术与新兴科学发现的融合。要实现这一目标,就必须摆脱传统的研究与开发(R&D)方法。新的开发流程需要支持这样一种认识,即研究的进展是迭代式的,技术的植入是渐进式的,而最终的能力是演进式的。我们提出了一种名为涡旋模型的新型系统工程/研究模式。涡旋模型引入了一个迭代框架,通过该框架,研究成果中的新进展可以作为新能力,在不同的技术插入点得到有效的展示和验证。我们的目标是促进新兴研究成果的知识转移,以便迅速、有效地集成到组织的任务能力中。
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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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