Biological systems inherit the knowledge to build, operate, and manage a society of cells with complex organizational structures, where autonomous components execute specific tasks and collaborate in groups to fulfill systemic goals using shared knowledge. This knowledge enables them to receive information through various senses and create a knowledge representation in the form of associative memory and an event-driven interaction history, consisting of various entities, relationships, and behaviors. The General Theory of Information, posited by the late Prof. Mark Burgin, allows us to model knowledge in the form of named sets/Fundamental Triads that capture various entities, relationships, interactions, and behaviors.
In this paper, we describe a digital genome implementation of distributed software systems that exhibit autopoietic and meta-cognitive behaviors using a knowledge representation capturing various entities, relationships, interactions, and behaviors. The resulting associative memory and event-driven interaction history allow us to implement self-regulating software that manages its specified cognitive workflows. Two use cases are demonstrated.
The first use case leverages a digital genome to design, deploy, operate, and manage a distributed video streaming service. The system uses associative memory and an event-driven interaction history to maintain structural stability and ensure smooth communication among distributed components. This allows the application to self-regulate and adapt to changing conditions while maintaining expected behaviors.
The second use case demonstrates the implementation of a medical knowledge-based digital assistant designed to assist in the early diagnostic process. The digital assistant is intended to reduce the knowledge gap between patients and medical professionals. It leverages medical knowledge from various sources, including large language models, to provide accurate and timely information. The assistant uses associative memory and an event-driven interaction history to create a comprehensive knowledge representation. This helps in understanding the patient's symptoms and medical history, facilitating better communication and decision-making between patients and doctors. By bridging the knowledge gap, the digital assistant enhances the early diagnostic process, leading to more accurate diagnoses and improved patient outcomes. It supports medical professionals by providing relevant information and insights based on past interactions and learned knowledge.
Cognizing oracles, supersymbolic computing, structural machines, knowledge structures, and knowledge networks are key concepts used in the applications described in these use cases. Cognizing oracles interpret observations and make informed decisions using associative memory and an event-driven interaction history. Supersymbolic computing integrates symbolic and subsymbolic representations to enhance its information processing capabilities. Structural machines are unconventional knowledge processors that work with knowledge structures, ensuring that software systems can self-regulate and adapt. Knowledge structures organize representations of entities, relationships, interactions, and behaviors, enabling autopoietic and cognitive behaviors in software systems. A knowledge network facilitates the sharing and integration of information across different components. These concepts collectively enable the creation of intelligent, adaptive systems that manage complex tasks and improve user experiences, such as in distributed software systems and medical knowledge-driven digital assistants.
These advances demonstrate the usefulness of a theory that relates information, knowledge, matter, and energy and provides a schema for knowledge representation and operations, enabling a new class of digital automata.
