opML: Optimistic Machine Learning on Blockchain

• URL: http://arxiv.org/abs/2401.17555v2
• Date: Mon, 5 Feb 2024 05:23:59 GMT
• Title: opML: Optimistic Machine Learning on Blockchain
• Authors: KD Conway, Cathie So, Xiaohang Yu, Kartin Wong,
• Abstract summary: We introduce opML (Optimistic Machine Learning on chain), an innovative approach that empowers blockchain systems to conduct AI model inference. opML lies a interactive fraud proof protocol, reminiscent of the optimistic rollup systems. opML offers cost-efficient and highly efficient ML services, with minimal participation requirements.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The integration of machine learning with blockchain technology has witnessed increasing interest, driven by the vision of decentralized, secure, and transparent AI services. In this context, we introduce opML (Optimistic Machine Learning on chain), an innovative approach that empowers blockchain systems to conduct AI model inference. opML lies a interactive fraud proof protocol, reminiscent of the optimistic rollup systems. This mechanism ensures decentralized and verifiable consensus for ML services, enhancing trust and transparency. Unlike zkML (Zero-Knowledge Machine Learning), opML offers cost-efficient and highly efficient ML services, with minimal participation requirements. Remarkably, opML enables the execution of extensive language models, such as 7B-LLaMA, on standard PCs without GPUs, significantly expanding accessibility. By combining the capabilities of blockchain and AI through opML, we embark on a transformative journey toward accessible, secure, and efficient on-chain machine learning.

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