Seat of Knowledge: Information-Centric Classification in AI

There is much discussion on the processing needed to propel further advancement in artificial intelligence (AI). Among others exploring this space, Henry Kautz proposed a taxonomy for neural-symbolic computing, parsing the integration of differentiable and symbolic information at the processing level and introducing six types of systems. In this series of blogs, I offer a different perspective: an information-centric classification with emphasis on the type, structure, and representation of knowledge and its implications for the attributes of the systems that deploy them. Such classification is needed for categorizing and assessing the structure and representation of the information integrated as AI systems learn and perform. In this blog, I will offer that an information-centric characterization of solutions can bring clarity to the underlying choices made and their material implications on the quality and efficiency of AI systems.