Seat of Knowledge: AI Systems with Deeply Structure Knowledge

In a series on the choices for capturing information and using knowledge in AI systems, I introduced the concept of an information-centric classification of AI systems as a complementary view to a processing-based classification such as Henry Kautz's taxonomy for neural symbolic computing. The classification emphasizes the high-level architectural choice related to information in the AI system. This blog will outline the third class in this classification and its promising role in supporting machine understanding, context-based decision making, and other aspects of higher machine intelligence. There is no access to additional information at test time. Examples include recent end-to-end deep learning (DL) systems and language models (e.g., GPT-3).