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Slider is the name for AI model access through oracles.
AI Model Access Through Oracles Enables blockchain applications to utilize external AI computations securely via oracles, enhancing smart contracts with AI-driven insights.
Integrating AI through oracles into decentralized finance (DeFi) can significantly enhance the functionality and efficiency of smart contracts by enabling them to utilize real-world data and advanced computation capabilities that are not inherently available on the blockchain.
Example: AI-Powered Credit Scoring for DeFi Lending Platforms
Scenario: A DeFi lending platform wants to offer personalized loan terms based on the borrower's creditworthiness. However, traditional credit scores are not directly accessible due to the decentralized and pseudonymous nature of blockchain.
Solution: The platform can use an AI model to determine credit scores based on alternative data sources, such as on-chain transaction history, decentralized identity data, and even data from social media or other off-chain sources.
Implementation Steps:
Data Collection: The AI model collects data from multiple sources. For on-chain data, it might look at a user’s transaction history, wallet balance, interaction with other smart contracts, etc. For off-chain data, it could integrate more traditional data points, potentially via user permission if privacy is a concern.
AI Model Training: The AI model is trained off-chain to predict credit scores based on the collected data. This training involves machine learning techniques that analyze patterns of creditworthiness from the historical data.
Oracle Integration: Once the AI model is trained, its insights (i.e., the credit scores) need to be used in smart contracts. This is where oracles come into play. The AI model runs off-chain due to its computational and data privacy requirements. Oracles act as trusted intermediaries that fetch the AI-generated credit scores from the off-chain environment and feed them into the blockchain in a secure, tamper-proof manner.
Smart Contract Usage: The DeFi smart contract uses the credit scores provided by the oracles to dynamically adjust loan terms. For example, borrowers with higher credit scores might receive lower interest rates and higher borrowing limits. The contract can automatically adjust these parameters based on the credit score input it receives.
Advantages:
Personalized Financial Products: Borrowers receive rates and terms tailored to their specific risk profile, which could increase platform adoption and user satisfaction.
Increased Trust and Security: Utilizing oracles for secure data transfer helps ensure that the AI model's outputs are accurately reflected in smart contracts without exposing the blockchain to manipulated data.
Scalability and Efficiency: Automating credit assessment with AI reduces the need for manual processing, scaling the platform’s capabilities with minimal additional cost.
This example illustrates how AI, when integrated with blockchain through oracles, can unlock sophisticated, real-time analytics for DeFi applications, making them more adaptable, secure, and user-friendly.
Example: AI-Enhanced Risk Management for DeFi Insurance
Scenario: A decentralized insurance platform aims to provide coverage for various risks associated with smart contracts, such as bugs, exploits, or failures in DeFi protocols. Traditional insurance models struggle to accurately assess and price these unique digital risks due to their complexity and the rapid pace of technological change in the blockchain space.
Solution: The platform employs an AI model to continuously analyze and predict the risk associated with insuring different DeFi protocols and smart contracts. This AI-driven approach allows for dynamic adjustment of premiums and coverage limits based on real-time risk assessment.
Implementation Steps:
Data Aggregation: The AI model aggregates data from multiple blockchain sources, including transaction histories, smart contract activity, and interactions across various DeFi protocols. It might also use off-chain data such as security audit reports, developer activity, and community engagement metrics.
AI Model Development: Off-chain, the AI model is developed to analyze the collected data and predict the likelihood of a contract failure or exploit. The model uses techniques like anomaly detection, pattern recognition, and predictive analytics to assess risk levels.
Oracle Function: Once the AI model has determined risk scores for different protocols or contracts, these scores are communicated to the blockchain via oracles. Oracles serve as the bridge, securely feeding this critical information into the decentralized insurance platform's smart contracts.
Smart Contract Integration: Using the risk scores provided by the oracles, the smart contracts on the insurance platform automatically adjust policy terms. This can include setting premiums, determining coverage limits, and even triggering automatic payouts when certain conditions are met, all based on the AI-assessed risk levels.
Advantages:
Dynamic Pricing: Insurance premiums and coverage limits can be adjusted in real time, reflecting the current risk assessment, which enhances fairness and economic efficiency.
Proactive Risk Management: The platform can proactively manage exposures to high-risk contracts and protocols by adjusting their insurance offerings based on the latest AI insights.
Automated Claims Processing: With AI monitoring the state and health of insured contracts, the platform can automate claims assessments and payouts when anomalies or failures are detected, improving response times and reducing administrative overhead.
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