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    Design for Artificial Intelligence: Proposing a Conceptual Framework Grounded in Data Wrangling

    Source: Journal of Computing and Information Science in Engineering:;2022:;volume( 022 ):;issue: 006::page 60903
    Author:
    Williams, Glen;Meisel, Nicholas A.;Simpson, Timothy W.;McComb, Christopher
    DOI: 10.1115/1.4055854
    Publisher: The American Society of Mechanical Engineers (ASME)
    Abstract: The intersection between engineering design, manufacturing, and artificial intelligence offers countless opportunities for breakthrough improvements in how we develop new technology. However, achieving this synergy between the physical and the computational worlds involves overcoming a core challenge: few specialists educated today are trained in both engineering design and artificial intelligence. This fact, combined with the recency of both fields’ adoption and the antiquated state of many institutional data management systems, results in an industrial landscape that is relatively devoid of highquality data and individuals who can rapidly use that data for machine learning and artificial intelligence development. In order to advance the fields of engineering design and manufacturing to the next level of preparedness for the development of effective artificially intelligent, datadriven analytical and generative tools, a new design for X principle must be established: design for artificial intelligence (DfAI). In this paper, a conceptual framework for DfAI is presented and discussed in the context of the contemporary field and the personas which drive it.
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      Design for Artificial Intelligence: Proposing a Conceptual Framework Grounded in Data Wrangling

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    contributor authorWilliams, Glen;Meisel, Nicholas A.;Simpson, Timothy W.;McComb, Christopher
    date accessioned2023-04-06T12:52:56Z
    date available2023-04-06T12:52:56Z
    date copyright10/17/2022 12:00:00 AM
    date issued2022
    identifier issn15309827
    identifier otherjcise_22_6_060903.pdf
    identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4288689
    description abstractThe intersection between engineering design, manufacturing, and artificial intelligence offers countless opportunities for breakthrough improvements in how we develop new technology. However, achieving this synergy between the physical and the computational worlds involves overcoming a core challenge: few specialists educated today are trained in both engineering design and artificial intelligence. This fact, combined with the recency of both fields’ adoption and the antiquated state of many institutional data management systems, results in an industrial landscape that is relatively devoid of highquality data and individuals who can rapidly use that data for machine learning and artificial intelligence development. In order to advance the fields of engineering design and manufacturing to the next level of preparedness for the development of effective artificially intelligent, datadriven analytical and generative tools, a new design for X principle must be established: design for artificial intelligence (DfAI). In this paper, a conceptual framework for DfAI is presented and discussed in the context of the contemporary field and the personas which drive it.
    publisherThe American Society of Mechanical Engineers (ASME)
    titleDesign for Artificial Intelligence: Proposing a Conceptual Framework Grounded in Data Wrangling
    typeJournal Paper
    journal volume22
    journal issue6
    journal titleJournal of Computing and Information Science in Engineering
    identifier doi10.1115/1.4055854
    journal fristpage60903
    journal lastpage6090310
    page10
    treeJournal of Computing and Information Science in Engineering:;2022:;volume( 022 ):;issue: 006
    contenttypeFulltext
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