From data-driven to knowledge-driven AI
In the KDAI Lab, Artur Schweidtmann and Qian Tao jointly strengthen the current data-driven AI by integrating fundamental knowledge from applied natural sciences. We conduct research on knowledge-driven AI, and show its potential in two applied science domains: medical imaging and chemical engineering. At the same time, our research can be used more broadly, because it studies the fundamental methodology for bringing knowledge into all the key components of AI: data collection, algorithm design, user interaction and implementation. We expect that knowledge-driven AI will be more interpretable and reliable than purely data-driven AI, and that it will further stimulate future scientific development.