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

Glen Williams, Nicholas A. Meisel, Timothy W. Simpson, Christopher McComb

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

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 high-quality 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, data-driven 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.

Original languageEnglish (US)
Article number060903
JournalJournal of Computing and Information Science in Engineering
Volume22
Issue number6
DOIs
StatePublished - Dec 2022

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Science Applications
  • Computer Graphics and Computer-Aided Design
  • Industrial and Manufacturing Engineering

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