AIエージェントの記憶構造：なぜパラメータが固定された脳で「過去を覚えている」のか？

Even with daily use of chat AI and AI agents, few can structurally explain the fundamental constraint that "once a model has completed training, its internal weight parameters can never be rewritten" and the contradiction between this and the phenomenon of AI "remembering" the previous context and user preferences. This video structurally unravels, from an engineering perspective, the causal relationship of the agent's memory architecture, where "short-term memory (context window)" and "long-term memory (vector store)" designed around static intelligence dynamically rewrite each input sentence, thereby circulating intelligence. #GenerativeAI #AIAgent #ContextWindow #VectorStore #InContextLearning 00:00 Opening: The Contradiction of an AI with Fixed Parameters Having Memory 00:52 Why the Structure of "Memory" is Absolutely Necessary in an Agent 02:12 AI Invariance as Seen from the Phenomenon of Data Disappearing When Switching Chats 03:33 The True Nature of Memory: The Mechanism of the Context Window (Contextual Processing Space) 04:58 Definition of Short-Term Memory: The Development and Limitations of Recent Text as an Analogy to a Workbench 06:43 The Trap of Physical Limitations (Overflow) and Accuracy Degradation (Contextual Erosion) 08:35 The Philosophy of Long-Term Memory: A Huge Data Storage Layer Established Outside the Workbench 09:47 Persistent Storage and Episodic Memory Shaping with Vector Stores 10:44 Associative Retrieval through Semantic Search (Calculation of Semantic Closeness) 12:25 The Memory Circular Loop: The Design Philosophy of Allowing Autonomous Operation While Keeping Intelligence Frozen 13:51 Boundary with Proximate Concepts: Differences in Short-Term and Long-Term Memory, and Knowledge (Parameters) 15:30 Comparison of Cost and Controllability of External Memory Systems and Additional Learning (Fine-Tuning) 17:26 Memory Conflict in Long-Term Operation: Maintenance of Time Priority Principle and Importance Scoring 19:00 Summary: Universal Design Philosophy of a Two-Layer Structure Consistent with Human Cognitive Structure