AI Agents Architecture
The architecture for training AI agents within the Aurory AI ecosystem is depicted in the provided diagram.
Last updated
The architecture for training AI agents within the Aurory AI ecosystem is depicted in the provided diagram.
Last updated
Here is a detailed explanation of each component and the overall process
Users: Users are integral to the training process. They interact with the system by providing training data and making requests to the model.
Training Data: Users contribute training data, which is essential for improving the AI model. This data is hashed and stored on the blockchain for verification and integrity purposes.
Hash: Each piece of training data is hashed to create a unique identifier. This ensures the authenticity and security of the data, making it tamper-proof once stored on the blockchain.
Blockchain: The blockchain serves as a decentralized ledger that records the hashes of the training data. It provides a transparent and secure way to store and verify data contributions. And the blockchain also manages rewards for users who provide valuable training data.
Training Process: This is where the actual machine learning and model training occur. The training data collected from users is used to train and improve the AI models. The process involves multiple stages where data is validated and approved through consensus mechanisms on the blockchain.
Model: The AI model is continuously updated and refined based on the training data. This model is what the users ultimately interact with when making requests and receiving responses.
Request and Response: Users make requests to the AI model, which processes these requests and generates appropriate responses. This interaction is also recorded and validated on the blockchain.
Approve and Reward Mechanism: Once the training data is submitted, it goes through an approval process. If approved, the user who provided the data is rewarded. This incentivizes users to contribute high-quality data.
Data Contribution: Users submit training data, which is hashed and stored on the blockchain. This ensures data integrity and transparency.
Approval and Reward: The submitted data is reviewed and approved. Approved data entries are rewarded, encouraging users to participate in the training process.
Training Process: The approved training data is used to train the AI model. This process involves multiple stages and iterations to ensure the model improves over time.
Model Interaction: Users can interact with the AI model by making requests. The model processes these requests using the trained data and provides responses.
Continuous Improvement: The cycle repeats with more data contributions, approvals, and training, leading to a continuously improving AI model.
The architecture leverages blockchain technology to ensure the security, transparency, and integrity of the training data. By rewarding users for their contributions, it creates an ecosystem where the AI model is constantly improved through community participation. This decentralized approach not only enhances the quality of the AI agents but also fosters a collaborative environment for innovation and development within the Aurory AI platform.
