Glossary
Machine Learning
AI systems that learn patterns from data. Sentient uses unsupervised learning for archetypal discovery.
Definition
Machine Learning (ML) encompasses AI systems that learn patterns from data rather than following explicit programming. Sentient OS employs ML throughout: unsupervised learning for archetypal clustering and behavioral segment discovery, embedding models for semantic alignment, and anomaly detection for fraud identification. The platform does not rely on supervised learning with labeled datasets for core intelligence-archetypal clustering discovers segments without predefined labels. ML models operate on vector representations, enabling similarity computation, clustering, and causal inference. The architecture is designed for ML at scale: vector computation, stream processing, and model deployment. ML is the engine; causality and determinism are the design principles.
Why It Matters
Machine learning powers Sentient's intelligence-from archetypal discovery to anomaly detection. The platform uses ML for pattern recognition, not pattern prescription.
Related Pages
Related Terms
Unsupervised Learning
ML approach that discovers hidden patterns without labeled data. Used in Layer 5 for behavioral archetype identification.
Archetypal Clustering
Unsupervised learning to identify behavioral clusters like 'The Skeptical Innovators' rather than demographic groups.
Anomaly Detection
Identifying artificial patterns in traffic (bots, fake engagement) through vector space anomalies, not keywords.
Predictive Analytics
Forecasting future outcomes using historical patterns. Sentient goes beyond to prescriptive/deterministic.
Persona Vectors
Mathematical representations of customers as points in complex space, enabling computable similarity and distance.
Explore the Full Platform
See how these concepts come to life inside Sentient OS.