Gadi Singer is Vice President and Director of Emergent AI Research at Intel Labs leading the development of the third wave of AI capabilities. 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.

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.

Gadi Singer is Vice President and Director of Emergent AI Research at Intel Labs leading the development of the third wave of AI capabilities. The previous blog in this series introduced the concept of an information-centric classification of AI systems as a highly valuable view that is complementary to processing-based classifications such as Henry Kautz' taxonomy for neural symbolic computing. It also previewed a classification that emphasizes the high-level architectural choice related to information in the AI system. The first class of systems in this classification system with its'Fully Encapsulated Information' was detailed in the previous blog of this series. Systems in this class incorporate all information required for AI tasks in the weights and model parameters without leveraging any additional adjunct sources of information.

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).