NAT101 Lesson 05: Knowledge Graph скачать в хорошем качестве

NAT101 Lesson 05: Knowledge Graph

🧠 Lesson 05: The Knowledge Graph Your brain does not store facts in isolation. It weaves them into a web of meaning. When new information connects to an existing schema, it becomes easier to retrieve, and harder to forget. In this lesson, we break down Schema Theory and the direct AI parallel: vector embeddings + RAG, meaning organized in math space. In this lesson, we explore: • Schema Theory (Piaget, Bartlett): why knowledge is stored as connected frameworks • Assimilation vs. accommodation (fit the schema, or reshape it) • Why isolated facts have no retrieval path (no connections, no context) • The library analogy: books on the floor vs. an organized catalog • Vector embeddings: positioning concepts by meaning in high-dimensional space • Cosine similarity: how AI “clusters” related ideas • RAG: retrieving the right context before generation • “Breach Protocol” schema-building strategies to learn anything faster • Build Objective: create a Schema Graph (template track + system prompt track) 📚 RESOURCES & REFERENCES Cognitive Science: • Bartlett, F. C. (1932). Remembering: A Study in Experimental and Social Psychology • Piaget, J. (1952). The Origins of Intelligence in Children • Rumelhart, D. E. (1980). “Schemata: The building blocks of cognition” (reading comprehension frameworks) AI / ML: • Mikolov et al. (2013). Efficient Estimation of Word Representations in Vector Space (Word2Vec) • Lewis et al. (2020). Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks (RAG) • Johnson, Douze, Jégou (2017). FAISS: Similarity Search and Clustering of Dense Vectors 🔗 CONNECT • Instagram: @outofthenorm_ai • LinkedIn: Ryan Norman