Core Concepts

As an Artificial Intelligence blockchain integrating onchain and offchain machine learning, Conwai is positioned as a central hub for AI applications, evisioning the following core concepts.

AI Model Access Through Oracles Enables blockchain applications to utilize external AI computations securely via oracles, enhancing smart contracts with AI-driven insights.

Basic Machine Learning Onchain Facilitates direct execution of basic machine learning processes on the blockchain, ensuring transparent and secure AI computations.

Offchain AI Access Through APIs Provides APIs for complex AI model access hosted offchain, combining blockchain security with powerful AI capabilities.

Marketplaces for Tokens of Different Models Features decentralized marketplaces for trading AI model-related tokens, facilitating the buying, selling, or trading of AI services.

Blockchain Empowered Model Fingerprint Verification Employs blockchain technology for the verification of AI model fingerprints, ensuring authenticity and preventing unauthorized use.

Decentralized Compute Distributes AI tasks across multiple blockchain nodes, enhancing privacy and reducing centralized failure risks.

Upgradeable Contracts Based on Machine Learning Output Supports contracts that can be updated based on insights derived from machine learning outputs, enabling dynamic responses to changing conditions or new data.

Smart Contract Auditing via AI Utilizes AI algorithms to automatically audit smart contracts on the blockchain, ensuring code security and compliance, thereby reducing vulnerabilities and enhancing trust.

AI-Driven Governance Protocols Incorporates machine learning to optimize governance decisions within the blockchain network, allowing for smarter, data-driven consensus mechanisms and policy adjustments.

Real-time AI Analytics for Blockchain Data Provides real-time analytics powered by AI to analyze blockchain transactions and trends, offering valuable insights for traders and decision-makers directly on the platform.

AI-Optimized Transaction Routing Utilizes machine learning to optimize the routing of transactions across the blockchain network, improving speed and efficiency by dynamically selecting the best pathways based on current network conditions.

Decentralized Data Lakes for AI Training Supports the creation of decentralized data lakes where anonymized data can be pooled securely for training AI models, enhancing the diversity and quality of datasets available for machine learning without compromising data privacy.

Gamified Crowdsourced Data Labeling Generate custom datasets labeled by a wide range of users through gamefied applications.