The ability of artificial intelligence to understand and learn from unstructured, narrativerich media remains a significant challenge. While current models excel at perceptual tasks like object detection, they lack the cognitive capacity to build comprehensive knowledge from complex sources like films. This paper introduces ”Cogito ex Machina,” a conceptualfour-stage framework designed to enable an AI system to watch, understand, and learn from cinematic media. The proposed architecture systematically processes multi-modal data (visual, auditory, textual) to extract raw perceptual information (Stage 1), transforms this data into high-level semantic concepts (Stage 2), performs cognitive reasoning to validate andenrich these concepts using a pre-existing knowledge base (Stage 3), and finally integrates thenewly acquired knowledge into a formal ontology (Stage 4). By treating film as a holisticsource of information, this framework provides a roadmap toward developing AI agentscapable of semantic knowledge acquisition from the world’s vast repository of narrativecontent. The paper outlines the theoretical underpinnings of each stage and discusses aprototypical implementation that serves as a proof-of-concept for the system’s foundationallayer.Keywords: Artificial Intelligence, Knowledge Acquisition, Computer Vision, Natural LanguageUnderstanding, Multi-Modal Learning, Knowledge Representation, RDF, Ontology.