# Embedding Model **Entity class:** AI model class **Domain:** Artificial intelligence / Information retrieval **Maturity:** Developed ## Definition An embedding model maps an input into a vector whose geometry supports similarity, clustering, retrieval, ranking, or downstream prediction. ## Mechanism and significance Embeddings compress rich inputs into operational representations, so their usefulness and bias depend on the training objective and the distinctions the vector space preserves. ## Relationships - **Research dossier:** [[research/ASI and RSI Timeline Research Moonshots|ASI and RSI Timeline Research Moonshots]] - **Ontology route:** [[ASI and RSI Timeline Ontology#Model Architecture and Compute Economics|Model Architecture and Compute Economics]] - **Primary fields:** [[Machine Intelligence]] · [[AI Infrastructure]] · [[Compute Race]] - **Adjacent concepts:** [[Decoder-Only Transformer]] · [[BERT Encoder]] · [[Reinforcement Fine-Tuning]] · [[Decision Model]] ## Sources and provenance - [[research/ASI and RSI Timeline Research Moonshots|ASI and RSI Timeline Research Moonshots]] — immediate source for this node's role in the broadcast research map. ## Evidence boundary The research dossier establishes why this entity or concept belongs in the Moonshots ontology. Time-sensitive organizational, product, policy, and performance claims should be checked against the linked primary source or a current authoritative source before reuse as settled fact.