Beyond the Tip of the Iceberg: Submerged Mathematical and Engineering Challenges in Applied Artificial Intelligence Systems
The recent rise of large language models (LLMs) and other generative artificial intelligence (AI) tools like agents represent only the visible tip of the iceberg in artificial intelligence. Beneath the surface lies a vast foundation of advanced mathematics, statistics, signal processing techniques, countless smaller/domain-specific models, optimization algorithms, distributed computing infrastructure, specialized engineering tools, and domain knowledge that make these headline-grabbing AI systems possible. This paper uses the iceberg metaphor to illuminate these hidden layers and surveys the technical background, including the mathematical and statistical underpinnings of AI, but also limitations of LLMs in many engineering applications such as power system fault detection, simulation-based optimization, and time-series forecasting. By exploring these “submerged” components of the AI iceberg, this paper provides a comprehensive perspective on the true breadth of modern AI, bridging the gap between public perceptions of AI and the complex reality beneath with multiple supporting case studies. The findings contribute to the existing body of knowledge by underscoring that meaningful progress in AI requires not only visible breakthroughs in models and interfaces, but also continuous advances in the less glamorous but critical supporting layers and applications of the AI stack.