Abstract: The increasing convergence and coupling between artificial intelligence (AI) and various disciplines have become prominent, serving as a significant force driving the transformation of scientific research. AI for science is initiating a new paradigm shift in scientific activities towards data-driven, automated, and intelligent processes. From an epistemological perspective, AI for science introduces new types of knowledge such as emergent intelligence and tacit knowledge, enabling a cognitive model based on high-dimensional computation. Methodologically, AI for science embraces uncertainty, addresses complex problems, and generates valuable knowledge through probability model. This approach marks the emergence of a new research model that integrates human scientists with AI scientists.