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Book chapter
Re-orienting toward the Science of the Artificial: Engineering AI Systems
Systems Engineering and Artificial Intelligence, pp.149-174
Springer International Publishing
11/02/2021
Abstract
AI-enabled systems are becoming more pervasive, yet system engineering techniques still face limitations in how AI systems are being deployed. This chapter provides a discussion of the implications of hierarchical component composition and the importance of data in bounding AI system performance and stability. Issues of interoperability and uncertainty are introduced and how they can impact emergent behaviors of AI systems are illustrated through the presentation of a natural language processing (NLP) system used to provide similarity comparisons of organizational corpora. Within the bounds of this discussion, we examine how the concepts from Design science can introduce additional rigor to AI complex system engineering.
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Details
- Title
- Re-orienting toward the Science of the Artificial
- Publication Details
- Systems Engineering and Artificial Intelligence, pp.149-174
- Resource Type
- Book chapter
- Publisher
- Springer International Publishing; Cham
- Number of pages
- 26
- Copyright
- © Springer Nature Switzerland AG 2021
- Identifiers
- 99381512427206600
- Academic Unit
- Intelligent Systems and Robotics; Hal Marcus College of Science and Engineering
- Language
- English