From Philosophical Idea to the Backbone of Modern AI
A schema is a mental or structural framework that helps us organize and interpret information. It's like a blueprint that shapes how we, and now machines, understand the world—connecting abstract concepts to concrete examples.
Ancient Greece - 18th Century
Plato & Aristotle: Introduced "Forms" and "Categories" as abstract templates for reality.
Immanuel Kant: Defined the "Transcendental Schema" as the bridge between abstract concepts and sensory experience.
"A hidden art in the depths of the human soul." - Kant
Early-Mid 20th Century
Bartlett (1930s): Showed memory is an active reconstruction based on personal mental schemas.
Piaget (1950s): Applied schemas to child development, introducing Assimilation (fitting new info into existing schemas) and Accommodation (changing schemas for new info).
1970s
E.F. Codd: Revolutionized data organization with the relational database model.
The "database schema" was born, providing a formal blueprint for storing and retrieving structured information, leading to concepts like Normalization to ensure data integrity.
2000s - 2011
Schema.org (2011): Search engines collaborated on a universal vocabulary to structure web data, enabling rich search results.
2012 - Present
Knowledge Graphs: Schemas form the blueprint for vast networks of interconnected facts that power AI assistants.
Large Language Models (LLMs): Structured data helps ground LLMs, reducing errors and improving factual accuracy.
Schemas will help create more accurate and explainable AI by providing factual grounding and logical structure.
AI will learn to automatically discover, build, and evolve schemas from unstructured data on its own.
Schemas will define the objects, rules, and interactions within the metaverse and other virtual environments.