The development of artificial intelligence (AI) technology has undergone several revolutions. The rapid advancements in big data, machine learning, and deep learning have fueled the emergence of a data-driven AI paradigm, enabling breakthroughs in fields such as image recognition, natural language processing, and speech recognition. However, data-driven AI heavily relies on large-scale, labeled datasets, which are costly and time-consuming to obtain, and it often lacks interpretability. In contrast, human knowledge-driven AI leverages structured domain knowledge for reasoning and problem-solving, allowing AI systems to understand complex scenarios. However, it faces challenges in adapting to new situations and achieving robust generalization. The data and knowledge dual-driven AI integrates the strengths of both paradigms, aiming to develop intelligent and interpretable systems while addressing concerns related to human safety and ethics, ultimately advancing toward human-centered AI. This chapter will delve into the underlying theory, techniques, and applications of these three AI paradigms, providing a comprehensive perspective on their future potential and key challenges.