On-chain machine learning refers to the execution or verification of machine learning model components directly on a blockchain. This involves using smart contracts to process data, perform computations, or validate the integrity of AI outputs. It aims to provide transparent, auditable, and decentralized artificial intelligence services. This approach ensures that AI operations are subject to the same immutability and consensus mechanisms as other blockchain transactions.
Context
The current state of on-chain machine learning faces significant challenges regarding computational costs and scalability limitations of existing blockchains. A key debate centers on balancing the desire for full on-chain execution with practical considerations for efficiency, often leading to hybrid solutions. Future developments focus on zero-knowledge proofs and more performant layer-2 solutions to make such computations feasible. News reports might cover new protocols attempting to offer verifiable AI inference on decentralized networks.
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