Engineering Artificial Intelligence: Framework, Challenges, and Future Direction

Abstract

Over the past ten years, the application of artificial intelligence (AI) and machine learning (ML) in engineering domains has gained significant popularity, showcasing their potential in data-driven contexts. However, the complexity and diversity of engineering problems often require the development of domain-specific AI approaches, which are frequently hindered by a lack of systematic methodologies, scalability, and robustness during the development process. To address this gap, this paper introduces the "ABCDE" as the key elements of Engineering AI and proposes a unified, systematic engineering AI ecosystem framework, including eight essential layers, along with attributes, goals, and applications, to guide the development and deployment of AI solutions for specific engineering needs. Additionally, key challenges are examined, and eight future research directions are highlighted. By providing a comprehensive perspective, this paper aims to advance the strategic implementation of AI, fostering the development of next-generation engineering AI solutions.

The paper submitted to the Journal Machine Learning: Engineering has been accepted

References in corpus (13)

• LLaMA: Open and Efficient Foundation Language Models
• A Review of Uncertainty Quantification in Deep Learning: Techniques, Applications and Challenges
• Attention Mechanisms in Computer Vision: A Survey
• On the Opportunities and Risks of Foundation Models
• Natural Language Processing: State of The Art, Current Trends and Challenges
• A Survey of Large Language Models
• A Unified Industrial Large Knowledge Model Framework in Industry 4.0 and Smart Manufacturing
• Machine Learning Approaches for Diagnostics and Prognostics of Industrial Systems Using Open Source Data from PHM Data Challenges: A Review
• MOMENT: A Family of Open Time-series Foundation Models
• Rethinking industrial artificial intelligence: a unified foundation framework
• Improved decision making with similarity based machine learning: Applications in chemistry
• Multi-modal Machine Learning for Vehicle Rating Predictions Using Image, Text, and Parametric Data
• Data Issues in Industrial AI System: A Meta-Review and Research Strategy