FEATURE ARTICLE Systems Engineering and Artificial Intelligence: Evolution, Challenges & Application by René King Project Performance International
Introduction
Copyright © 2024 by Project Performance International. All rights reserved. Authored for PPI SyEN
1. Introduction The rapid integration of Artificial Intelligence (AI) in systems engineering (SE) offers an opportunity to reflect on the evolution of AI, the underlying principles for its application, and the wide range of areas where it can be included. Since its inception in the 1950s, AI has aimed to replicate human intelligence in machines, focusing on capabilities such as data processing, pattern recognition, problem-solving, and autonomous decision-making (Smith & Roberts, 2022). AI encompasses a diverse array of technologies and methodologies, including systems with predefined decision-making logic and those capable of learning from data. Machine Learning (ML) is a key area of focus in modern AI applications. AI is perceived not as a substitute for SE practitioners but as a valuable tool that enhances their capabilities and alleviates the workload of repetitive tasks. It also paves the way for novel innovations and improves various industrial processes. Engineers face the challenge of harnessing these advanced technologies, optimizing their potential, and persisting in leading the development of adaptive solutions that meet the changing demands of society (Smith et al., 2023). Key AI technologies integral to SE include: 1. Machine Learning (ML): ML empowers systems to learn from experience and improve autonomously without being explicitly programmed. This technology is particularly advantageous for applications like predictive analytics, anomaly detection, and system optimization. ML derives its core principles from statistics and computer science, bolstered by advancements in computational power and memory capabilities of modern hardware like CPUs and GPUs (Johnson, 2021). ML can be categorized into three primary learning models: •
• May 2024
Supervised Learning: This model utilizes labeled datasets to learn the mapping between input and output pairs, which can be applied in scenarios like image recognition from labeled images (Lee & Kim, 2021). Unsupervised Learning: In this model, algorithms learn to identify patterns or group data [Contents]
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