Recent progress on large-scale machine learning (ML) and deep learning (DL) have demonstrated great potential in both traditional artificial intelligence (AI) applications in computer science (such as natural language processing, knowledge engineering and computer vision) and AI-enabled applications in scientific domains (such as AlphaFold2). Such a trend makes AI an important high performance computing application, and ignites the innovative opportunity in the field of scientific discovery. To enable scalable AI in a high performance computing environment, we are facing quite a few challenges in architecture, software ecology and application innovation. This issue focuses on the novel ideas, methods, as well as efforts for resolving the above challenges, and to adapt HPC to new computing paradigms of knowledge and data convergent science discovery. We have eight invited papers selected for this special issue based on a peer-review procedure, which cover several different aspects that relate to the software, algorithm and application challenges mentioned above. The first part of the special issue focuses on the software methods and tools for facilitating AI and Big Data applications, which is the fundamental challenge on development and deployment of these emerging applications on high performance systems. We have two papers that discuss the design and development of AI toolkits on Sunway