Model Inference

Definition ∞ Model inference, in the context of digital assets and blockchain, typically refers to the process of using a trained artificial intelligence or machine learning model to make predictions or decisions based on new input data. This could involve forecasting market movements, detecting fraudulent transactions, or optimizing smart contract execution. The accuracy and speed of this process are critical for real-time applications. It represents the practical application of AI in the digital asset domain.
Context ∞ The discussion surrounding model inference often focuses on its potential to provide analytical advantages and automate complex operations within cryptocurrency markets and decentralized finance. A key debate involves ensuring the transparency and explainability of AI models used in sensitive financial contexts. Future developments are directed towards integrating verifiable computation techniques to allow for trustless model inference on blockchain networks, enhancing auditability.