#4536 Concept-Based AI Model; +Statistical Analysis Concepts-Based Model
#4536 Concept-Based AI Model; +Statistical Analysis Concepts-Based Model #4536 Describe an AI model that is trained on concepts rather than words To describe an AI model trained on concepts rather than words , we have to move away from the surface-level mechanics of Next-Token Prediction (which powers current Large Language Models) and shift toward architectures designed for Functional Analysis , structural relationships , and semantic invariants . While standard LLMs use tokens (words or sub-words) as their fundamental currency, a true Concept Model maps the underlying principles, behaviors, and properties of the world. Here is a blueprint for how such a model is structured, how it learns, and how it operates. 1. The Core Architecture: Hyperdimensional Conceptual Spaces Instead of a linear sequence of text tokens, a concept model uses a continuous, multi-dimensional geometric space where vectors represent dense, distilled principles rather than semantic labels. Sub-Concept Dec...