The field of data visualization and analysis is moving towards creating more interactive, scalable, and user-friendly tools. Researchers are focusing on developing techniques that can handle large datasets and provide insights into complex relationships and patterns. One of the key areas of development is the use of artificial intelligence and machine learning to enhance data analysis and visualization. This includes the application of AI-assisted analysis, automated reporting, and predictive modeling to various domains such as academic research, area studies, and materials science. Noteworthy papers in this area include Embedding Atlas, which provides a low-friction, interactive embedding visualization tool, and ForeCite, which predicts future citation rates of academic papers using pre-trained language models. Other notable papers, such as VizCV and From Text to Network, demonstrate the potential of AI-assisted visualization and knowledge graph construction in advancing our understanding of complex research topics.