Gamification and Quality Control
Cortensor employs an innovative approach to network development and quality assurance through gamification and a robust quality control system. This strategy ensures the continuous improvement of the network's capabilities while maintaining high standards of service.
Proof of Useful Work (PoUW)
Cortensor implements a novel consensus mechanism called Proof of Useful Work (PoUW), which combines network security with practical AI tasks.
Level 1: Network Liveness
Purpose: Ensure basic functionality and responsiveness of inference nodes.
Process:
Randomly selected nodes generate short questions on selected topics.
Other nodes provide completions for these questions.
Validation nodes assess and score the responses.
Outcome: Establishes a minimum performance threshold for node participation.
Level 2: Capability Assessment
Purpose: Evaluate and rank nodes based on their processing speed and capabilities.
Process:
Nodes are challenged with more complex AI tasks within time constraints.
Performance is measured in terms of token speed and task completion quality.
Outcome: Categorizes nodes based on their capabilities, enabling efficient task allocation.
Gamified Supply-Side Development
Cortensor incentivizes network growth through a gamified approach:
Public Participation: Anyone can provide and run binary/system images to become validators.
Competitive Environment: Nodes compete to improve their performance and capabilities.
Tiered Progression: Nodes advance through levels, unlocking access to more complex tasks and higher rewards.
Quality Control Mechanisms
Intelligent Routing
Router nodes analyze task requirements and node capabilities.
Ensures optimal matching of AI inference tasks to appropriate nodes.
Multi-Layer Validation
Router Validation: Initial check of task completion and basic quality assessment.
Guard Node Validation: In-depth verification of results and scoring for accuracy.
Reputation System: Nodes build reputation scores based on performance and result quality.
Fraud Prevention
Multiple validation nodes assess each task to detect and prevent malicious behavior.
Consensus-based scoring system ensures fair and accurate evaluations.
Benefits of Gamification and Quality Control
Continuous Improvement: Encourages nodes to upgrade their hardware and optimize performance.
Dynamic Network Adaptation: The network evolves to meet changing demands and technological advancements.
High-Quality Results: Rigorous validation ensures reliable and accurate AI inference outputs.
Fair Reward Distribution: Performance-based incentives align node operator interests with network goals.
Synthetic Data Generation
As a byproduct of the PoUW process, Cortensor generates valuable synthetic data:
Use Cases: Training data for AI models, benchmarking, and network optimization.
Future Potential: Development of on-demand synthetic data generation frameworks.
Future Developments
Cortensor plans to enhance its gamification and quality control features:
Advanced PoUW algorithms for more diverse AI tasks.
Integration of federated learning principles into the validation process.
Expansion of synthetic data generation capabilities for specific industry applications.
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