👉 Theory engineering is an approach within the field of artificial intelligence and machine learning that focuses on designing, developing, and refining theoretical frameworks to better understand and explain the behavior of complex AI systems. It involves creating mathematical models, formalisms, and conceptual structures to articulate the underlying principles governing how these systems learn, make decisions, and generalize from data. By bridging the gap between empirical observations and theoretical insights, theory engineering aims to enhance the interpretability, robustness, and reliability of AI models. This process not only aids in diagnosing issues and improving existing systems but also guides the development of new algorithms and architectures by providing a deeper understanding of the fundamental mechanisms at play. Ultimately, theory engineering seeks to make AI systems more transparent and trustworthy, fostering their wider adoption and integration into critical applications.
