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Artículo: AMZ-B0G238W56B
Designing Long-Term Memory Systems for AI: Engineering Knowledge Retention, Recall, and Lifelong Learning in AI Systems
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Kindle
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0.20 kg
0.20 kg
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Amazon
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USA
Sobre este producto
- Designing Long-Term Memory Systems for AI is a groundbreaking guide that explores how artificial intelligence can move beyond short-term reasoning toward genuine understanding, reflection, and self-improvement. This book demystifies the science and engineering behind long-term memory in AI — showing you how to build systems that learn continuously, recall intelligently, and evolve autonomously over time. Whether you’re developing intelligent assistants, autonomous agents, or multi-agent frameworks, this book provides the conceptual depth and technical clarity you need to design memory systems that think long after training ends. Written by an AI systems engineer and educator with deep expertise in memory-augmented architectures, this book draws from cutting-edge research in neural memory networks, cognitive modeling, and agentic AI frameworks like LangGraph, AutoGen, and CrewAI. Every concept is explained through real-world case studies and verified working code, ensuring not just theory but hands-on applicability for developers, researchers, and technical professionals. About the Technology: At its core, this book is about bridging cognition and computation. You’ll learn how to:Build vector-based and semantic memory systems using tools like FAISS, Pinecone, and Chroma.Integrate knowledge graphs and embeddings for structured recall.Implement reflection and feedback loops that enable AI to learn from experience.Develop persistent agents that store, retrieve, and refine knowledge dynamically.Apply meta-learning and continual learning strategies to create self-evolving AI systems. What's Inside:Cognitive models of human memory and their computational analogs.Detailed architectures for storage, indexing, and retrieval in long-term AI memory.Step-by-step examples for building memory-enabled agents with LangChain and LangGraph.Design patterns for reflective learning, reinforcement through feedback, and ethical forgetting.Scalable approaches for managing, pruning, and optimizing memory over time.Governance frameworks for transparency, consent, and human-centered AI memory design.Who This Book Is For: This book is designed for:AI Engineers and Developers building intelligent systems that need to retain and recall knowledge over long periods.Data Scientists and Researchers exploring lifelong learning and self-evolving AI.System Architects and Technologists designing multi-agent or autonomous frameworks.Educators and Students seeking a practical yet profound understanding of how memory transforms AI from reactive to truly intelligent. As AI shifts toward autonomy, the next generation of intelligent systems will depend on persistent memory — not just models that respond, but systems that evolve. Understanding how to engineer long-term memory isn’t optional; it’s becoming essential. Those who master it now will shape the architecture of tomorrow’s self-improving AI. If you’re ready to move beyond models that simply predict and build AI systems that truly understand, remember, and grow, then this book is your essential guide. Start reading Designing Long-Term Memory Systems for AI today — and learn how to build the next generation of intelligent, autonomous, and adaptive machines.
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