# General Function Approximation **Entity class:** Machine-learning concept **Domain:** Artificial intelligence / Learning theory **Maturity:** Developed ## Definition General function approximation treats learning systems as mechanisms for estimating broad classes of relationships from data rather than as collections of hand-authored rules for one task. ## Mechanism and significance Its power comes from reusable learning machinery; its limits appear when training distributions, objectives, or observability fail to capture the world the model must act within. ## Relationships - **Research dossier:** [[research/ASI and RSI Timeline Research Moonshots|ASI and RSI Timeline Research Moonshots]] - **Ontology route:** [[ASI and RSI Timeline Ontology#Scientific Intelligence and Recursive Improvement|Scientific Intelligence and Recursive Improvement]] - **Primary fields:** [[AI for Science]] · [[Scientific Acceleration]] · [[Machine Intelligence]] - **Adjacent concepts:** [[Computational-Substrate Self-Modification]] · [[The Bitter Lesson]] · [[Fixed Point of Recursive Self-Improvement]] · [[Metacognition]] ## 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.