{"title":"Transitioning Science to Practice","authors":"Stuart D. Harshbarger, Rosa R. Heckle","doi":"10.1002/inst.12485","DOIUrl":null,"url":null,"abstract":"<div>\n \n <p>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.</p>\n </div>","PeriodicalId":13956,"journal":{"name":"Insight","volume":null,"pages":null},"PeriodicalIF":1.0000,"publicationDate":"2024-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Insight","FirstCategoryId":"5","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1002/inst.12485","RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"INSTRUMENTS & INSTRUMENTATION","Score":null,"Total":0}
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.
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.