Glossary
Unsupervised Learning
An AI approach that discovers hidden patterns without needing labeled examples. Used to find behavioral groups that demographics miss. For behavioral archetype identification.
Definition
Unsupervised Learning is the machine learning paradigm where algorithms discover hidden patterns in data without labeled examples. Unlike supervised learning (which requires 'this is a cat' labels), unsupervised learning finds structure on its own. Sentient OS uses unsupervised learning in Layer 5 (Pattern Recognition) for archetypal clustering: the algorithm discovers behavioral segments-'The Skeptical Innovators,' 'The Impulse Explorers'-from persona vectors without predefined categories. No demographic labels, no survey data; the data reveals the segments. Unsupervised learning scales to millions of entities and adapts as behavior evolves. It's the foundation of audience intelligence that transcends demographics.
Why It Matters
Unsupervised learning is how Sentient discovers archetypes. No labels required-the data reveals behavioral segments that predict action.
Related Pages
Related Terms
Archetypal Clustering
Unsupervised learning to identify behavioral clusters like 'The Skeptical Innovators' rather than demographic groups.
Machine Learning
AI systems that learn patterns from data. Sentient uses this to discover behavioral patterns automatically.
Persona Vectors
Mathematical representations of customers as points in complex space, enabling computable similarity and distance.
Customer Segmentation
Dividing audiences into groups. Sentient goes beyond age and gender to behavioral patterns that actually predict response.
Vector Spaces
A mathematical space where people, products, and content are represented so that 'closeness' means compatibility. The foundation for precise matching. Mathematics instead of databases.
Explore the Full Platform
See how these concepts come to life inside Sentient OS.