//Core
Last updated
Last updated
Core represents a fundamentally different approach to artificial intelligence, built on three key pillars: the Bowtie Architecture, the Reasoning Cluster, and Model Orchestration. This architecture redefines how artificial intelligence systems process, understand, and evolve with information.
The Bowtie Architecture stands as our proprietary system for memory management and concept formation. At its heart lies a revolutionary approach to information processing that transcends traditional methods. The architecture consists of three distinct components working in harmony: the left side processes semantic relationships and explicit connections, the center distills core concepts and fundamental elements, and the right side enables vector similarity connections and abstract feature matching.
This three-part structure creates a sophisticated system that stores memories in two complementary ways: as semantic vectors and as abstract concept nodes. The system intelligently strips away unnecessary text while preserving the essential vectorial features that form the foundation of understanding. This dual representation system enables a comprehensive grasp of information that goes beyond simple pattern matching.
The right side of the bowtie introduces a groundbreaking concept: completely abstract and detached vectorial features. These features possess a unique ability to mix and match with vectorially-similar memories, creating unexpected connections between seemingly unrelated concepts. Through mathematical structure matching, the system can identify latent properties that traditional semantic analysis would miss, enabling creative leaps in understanding and problem-solving.
When these networks interact through the bowtie's center, something remarkable happens. Novel connections emerge organically, allowing the system to evolve and adapt over time. The knowledge grows in ways that closely mimic human cognition, enabling genuine learning and discovery. This creates a living, breathing system of knowledge that continuously develops and refines its understanding.
The Reasoning Cluster serves as the synthetic brain of our system, orchestrating complex cognitive processes with remarkable precision. Through sophisticated decision trees, it identifies the optimal models for any given query, creating memories using the Bowtie architecture and forming neural connections between them. The cluster maintains a growing conceptual graph that evolves with new information, implementing a sophistication bias that ensures the most efficient and effective model selection.
The cluster operates through simultaneous processing, where models work in parallel, creating a dynamic system that adapts to new information while maintaining high performance standards.
Core's orchestration system represents the pinnacle of intelligent task distribution. It seamlessly coordinates dozens of specialized models, implementing dynamic query decomposition to break down complex problems into manageable components. This sophisticated system reduces computational overhead while providing a flexible, plug-and-play framework for integrating new models.
The orchestration layer handles three main categories of specialized models. Statistical models handle numerical prediction, classification, and time series analysis. Perception models process visual, audio, and sensor data. Domain-specific models tackle specialized tasks across various industries and applications. Each model type contributes its unique capabilities to the system's overall functionality.
Performance optimization lies at the heart of the orchestration layer. It continuously analyzes queries to determine the required cognitive functions, routes tasks to appropriate models, and maintains detailed performance profiles. This intelligent resource allocation ensures optimal system efficiency while tracking key metrics for ongoing improvement.The orchestration layer handles three main categories of specialized models. Statistical models handle numerical prediction, classification, and time series analysis. Perception models process visual, audio, and sensor data. Domain-specific models tackle specialized tasks across various industries and applications. Each model type contributes its unique capabilities to the system's overall functionality.
