Big Data Intelligence

This chapter reviews the birth, evolution, and key advancements of big data intelligence, tracing the development of artificial intelligence from early logical reasoning and expert systems to modern data-driven machine learning and deep learning paradigms. It emphasizes the shift toward a dual-driven framework integrating knowledge and data to address challenges such as the “black box” nature of deep models, limited interpretability, and poor generalization. The chapter explores multiple knowledge representation theories, including the integration of symbolic knowledge, physical laws, causal knowledge, and visual knowledge with data, highlighting their synergies in enhancing model interpretability, robustness, and reasoning capabilities. Additionally, it presents practical applications across diverse fields: digital humans, intelligent education, scientific machine learning (e.g., bioinformatics, weather forecasting, asteroid discovery), visual scene understanding, and visual question answering (VQA). Finally, it discusses current challenges and future directions, underscoring the critical role of knowledge-data coordination in driving technological innovation and societal transformation.