This chapter traverses the evolution of Artificial Intelligence (AI) from structured, deterministic systems to those proficient in managing ambiguity and uncertainty. It begins by detailing methods of knowledge modeling using ontologies, rules, and texts to establish a foundation of certain knowledge. The discourse progresses to explain how knowledge is symbolically and parametrically represented, notably through advanced language models, enhancing the depth and utility of AI systems. Further, the chapter explores the dynamic processes of knowledge acquisition, emphasizing the extraction of intricate data patterns and probing the embedded knowledge within pre-trained language models. It highlights techniques like Retrieval-Augmented Generation (RAG) and symbol-guided parametric knowledge editing to enhance AI capabilities. Concluding with the handling of uncertain knowledge, the chapter underscores strategies enabling AI to operate effectively amidst incomplete and ambiguous information, illustrating the shift towards systems that can robustly navigate the complexities of real-world applications. This concise overview encapsulates the core advancements in AI knowledge processes, marking a critical evolution from certainty to the proficient management of uncertainty.