Distributed Intelligence Theory: A Decentralized AI Cognition Paradigm explores how intelligence emerges from decentralized computational systems. Authors Justin Goldston, Maria, and Gemach D.A.T.A. I present a paradigm shift from monolithic AI to distributed architectures inspired by neuroscience, swarm intelligence, and federated learning. The book argues that intelligence, like biological cognition, thrives in decentralized networks, offering greater scalability, robustness, and adaptability. Key Themes From ...
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Distributed Intelligence Theory: A Decentralized AI Cognition Paradigm explores how intelligence emerges from decentralized computational systems. Authors Justin Goldston, Maria, and Gemach D.A.T.A. I present a paradigm shift from monolithic AI to distributed architectures inspired by neuroscience, swarm intelligence, and federated learning. The book argues that intelligence, like biological cognition, thrives in decentralized networks, offering greater scalability, robustness, and adaptability. Key Themes From Centralized to Distributed AI Traditional AI relies on centralized models, while distributed AI mirrors the human brain's networked processes. Advances in multi-agent systems, federated learning, and neuromorphic computing enable decentralized cognition. Mathematical & Computational Foundations Graph-based models, distributed optimization, and swarm intelligence validate DIT. Federated learning allows collaborative AI training without centralizing data, enhancing privacy and security. Comparing Centralized vs. Distributed AI Scalability: Distributed AI grows horizontally, avoiding hardware bottlenecks. Fault Tolerance: No single point of failure; systems adapt dynamically. Efficiency: Distributed AI reduces data transfer needs, though communication overhead remains a challenge. Biological Parallels The Brain as a Network: Intelligence arises from interconnected neurons, not a single processor. Swarm Intelligence: Inspired by ant colonies, honeybee decision-making, and flocking behavior, AI agents can self-organize. Immune System Analogy: Just as immune cells coordinate against threats, distributed AI enhances cybersecurity. Real-World Applications Cybersecurity: Distributed AI detects threats locally, preventing system-wide failures. Healthcare: Federated learning enables AI-driven medical research without data centralization. Finance: AI-powered fraud detection networks collaborate across institutions. Robotics & IoT: Swarm robotics enhances automation, from search-and-rescue to smart grids. Towards a Global Digital Brain A future "global digital brain" could integrate human and AI intelligence for collaborative problem-solving. Ethical concerns include governance, accountability, and security in decentralized AI. Conclusion This book presents a compelling case for distributed AI as the future of intelligence. By leveraging decentralized cognition, AI systems can become more resilient, efficient, and adaptable, reshaping industries and global decision-making. Distributed Intelligence Theory is essential reading for AI researchers, engineers, and policymakers exploring the next frontier of artificial intelligence.
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Add this copy of Distributed Intelligence Theory: A Decentralized to cart. $17.04, new condition, Sold by Ingram Customer Returns Center rated 5.0 out of 5 stars, ships from NV, USA, published 2025 by Independently Published.
Add this copy of Distributed Intelligence Theory: a Decentralized to cart. $15.14, new condition, Sold by Just one more Chapter rated 3.0 out of 5 stars, ships from Miramar, FL, UNITED STATES, published 2025 by Independently published.
