Bridging Decentralized AI and Blockchain: Challenges and Opportunities
S. K. Gaddam
Abstract
This paper investigates the technical and systemic bridges required to integrate AI with blockchain architectures. The author identifies key constraints, particularly the computational mismatch between high-throughput AI models and the limited block space/execution capacity of current blockchains. The paper evaluates various solutions, including off-chain computation oracles, sidechains, and Layer-2 scaling solutions that allow for AI processing to occur away from the main ledger while maintaining cryptographically verified proofs. The study provides a taxonomy of challenges, including data privacy, oracle reliability, and the economic sustainability of decentralized AI networks. Through empirical observation of existing projects, the research highlights the importance of verifiable computation in connecting these two domains. The findings emphasize that progress is dependent on the maturation of Zero-Knowledge Proofs and other privacy-preserving computation technologies that enable the verification of AI results without needing to re-run heavy models directly on-chain.
Key Findings
- 1Off-chain computation is necessary for integrating heavy AI models with blockchain protocols.
- 2Zero-knowledge proofs are essential for verifying the validity of off-chain AI outputs on-chain.
- 3Oracle reliability constitutes a significant 'single point of failure' for decentralized AI systems.
- 4Scalability and latency are the primary constraints for on-chain AI integration.
Topics
Citation
BibTeX
@misc{bridgingdecentralized2024,
title = {Bridging Decentralized AI and Blockchain: Challenges and Opportunities},
author = {S. K. Gaddam},
year = {2024},
howpublished = {\url{https://ieeexplore.ieee.org/document/10859075/}},
}Knowledge Explorer
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