AI Models Execution

aelf is committed to integrating AI with blockchain technology to enhance user experiences and unlock new capabilities. One useful use case is verifiable off-chain AI model execution, specifically leveraging off-chain AI model execution environments and incorporating Zero-Knowledge Machine Learning (zkML) for secure and trustless on-chain verification of AI model outputs.

Integration of zkML with AELF

Zero-Knowledge Machine Learning (zkML) is a cryptographic protocol that enable parties to prove that specific inputs were processed through an AI model to generate certain outputs, without revealing the model's parameters or the input data.

zkML uses zero-knowledge proofs to ensure data privacy and protect the intellectual property of the AI models. This allows a trustless environment where users can confidently verify the accuracy and integrity of model outputs.

The process with zkML involves:

• Execution of Machine Learning Models: A trained model is used to make predictions or decisions based on input data. The execution typically happens off-chain (i.e., outside the blockchain) due to the computational intensity and the need for privacy.

• Proof Generation: After the model is executed, a cryptographic proof is generated. This proof can demonstrate that the model was run correctly and produced a specific output from a given input. The most common technology used for creating such proofs is zero-knowledge proofs (ZKPs), particularly zk-SNARKs (Zero-Knowledge Succinct Non-Interactive Argument of Knowledge). These proofs can verify the correctness of computation without revealing the underlying data or model.

• On-Chain Verification: The proofs are then verified on the aelf blockchain. Verification of the proofs will be done in the smart contracts and it doesn't involve the knowledge about the model or the private input data. This step ensures that anyone on the blockchain can trust the outcome of the machine learning execution without needing to see the data or the model, thus maintaining confidentiality and privacy.

The whole process safeguards the privacy of the data and the intellectual property inherent in the model parameters, which is crucial in sensitive sectors like healthcare or finance. It ensures that the data does not leave a secure environment and that only the necessary information (the proof and outcome) is disclosed.

AELF's Technical Implementation

aelf supports the following steps for integrating off-chain AI model execution into its blockchain:

• Off-Chain Compute Environment: Collaborating with AI partners for off-chain execution.

• Data Ingestion: Working with partners to provide tools to securely transfer data to the off-chain execution environment.

• AI Execution: Leveraging off-chain infrastructure for executing AI models.

• Proof Generation and Verification: Generating proofs and verifying them on-chain to ensure model integrity and output accuracy.

• On-Chain Integration: ZK libraries will be included in aelf smart contract to support proof verification.

• Incentivization and Governance: Implementing incentive mechanisms and governance models for decentralized decision-making and tokenized rewards.

Through off-chain execution environments, aelf benefits from scalable and powerful computational resources while maintaining the security, transparency, and decentralized nature of blockchain networks. This approach enables the development and deployment of advanced AI models in decentralized applications (dApps) and blockchain systems, opening new avenues for intelligent and autonomous decentralized services.

Charting the Course: AELF's Future Trajectory

As the field evolves, aelf will continue to adapt and enhance the integration of AI model execution on its blockchain by:

• Enhancing the efficiency and scalability of zkML proofs for more complex AI models.

• Exploring alternative zero-knowledge proof systems and their implications for computational complexity, trust assumptions, and security.

• Investigating incentive mechanisms and governance models for decentralized AI execution and verification.

• Examining the broader impacts of AI and blockchain integration on fairness, accountability, and transparency in decision-making processes.