Cortensor
  • Home
  • Abstract
    • Value Proposition
    • Whitepaper
      • Page 1: Introduction and Vision
      • Page 2: Architecture and Technical Overview
      • Page 3: Incentive Structure and Tokenomics
      • Page4: Development Roadmap and Phases
      • Page5: Summary
  • Introduction
    • What is Cortensor?
    • Key Features & Benefits
    • Vision & Mission
  • Getting Started
    • Quick Start Guide
    • System Requirements
    • Installation & Setup
      • Getting Test ETH
      • Setup Own RPC Endpoint
      • Router Node Setup
        • Router API Reference
  • Core Concepts
    • Decentralized AI Inference
      • Community-Powered Network
      • Gamification and Quality Control
      • Incentive Structure
    • Universal AI Accessibility
    • Multi-layer Blockchain Architecture
  • Technical Architecture
    • Design Principles
    • Node Roles
    • Node Lifecycle
      • Ephemeral Node State
    • Node Reputation
    • Network & Flow
    • Type of Services
    • Coordination & Orchestration
      • Multi-Oracle Node Reliability & Leadership Rotation
    • AI Inference
      • Open Source Models
        • Centralized vs Decentralized Models
      • Quantization
      • Performance and Scalability
    • Consensus & Validation
      • Proof of Inference (PoI) & Proof of Useful Work (PoUW
      • aka Mining
      • Proof of Useful Work (PoUW)
      • Proof of Useful Work (PoUW) State Machine
        • Miner & Oracle Nodes in PoUW State Machine
      • Sampling in Large Distributed Systems
      • Parallel Processing
      • Embedding Vector Distance
    • Multi-Layered Blockchain Architecture
    • Modular Architecture and Smart Contract Interactions
      • Session Queue
      • Node Pool
      • Session Payment
    • Mining Overview
    • User Interaction & Node Communication
      • Session, Session Queue, Router, and Miner in Cortensor
    • Data Management
      • IPFS Integration
    • Security & Privacy
    • Dashboard
    • Development Previews
      • Multiple Miners Collaboration with Oracle Node
      • Web3 SDK Client & Session/Session Queue Interaction
    • Technical Threads
      • AI Agents and Cortensor's Decentralized AI Inference
    • Infographic Archive
  • Community & Ecosystem
    • Tokenomics
      • Network Incentive Allocation
      • Token Allocations & Safe Wallet Management
    • Staking Pool Overview
    • Contributing to Cortensor
    • Incentives & Reward System
    • Governance & Compliance
    • Safety Measures and Restricted Addresses
    • Buyback Program
    • Liquidity Additions
    • Partnerships
      • Partnership Offering for Demand-Side Partnerships
    • Community Testing
      • Closed Alpha Testing Phase #1
        • Closed Alpha Testing Phase #1 Contest: Closing & Winners Announcement
      • Closed Alpha Testing Phase #2
      • Closed Alpha Testing Phase #3
      • Discord Roles & Mainnet Privileges
      • DevNet Mapping
      • DevNet Modules & Parameters
    • Jobs
      • Technical Writer
      • Communication & Social Media Manager
      • Web3 Frontend Developer
      • Distributed Systems Engineer
  • Integration Guide
    • Web2
      • REST API
      • WebSocket
      • Client SDK
    • Web3
      • Web3 SDK
  • Use Cases
  • Roadmap
    • Technical Roadmap: Launch to Next 365 Days Breakdown
    • Long-term Vision: Beyond Inference
  • Glossary
  • Legal
    • Terms of Use
    • Privacy Policy
    • Disclaimer
    • Agreement for Sale of Tokens
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On this page
  • Proof of Useful Work (PoUW)
  • Level 1: Network Liveness
  • Level 2: Capability Assessment
  • Gamified Supply-Side Development
  • Quality Control Mechanisms
  • Intelligent Routing
  • Multi-Layer Validation
  • Fraud Prevention
  • Benefits of Gamification and Quality Control
  • Synthetic Data Generation
  • Future Developments
  1. Core Concepts
  2. Decentralized AI Inference

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:

    1. Randomly selected nodes generate short questions on selected topics.

    2. Other nodes provide completions for these questions.

    3. 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:

    1. Nodes are challenged with more complex AI tasks within time constraints.

    2. 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:

  1. Public Participation: Anyone can provide and run binary/system images to become validators.

  2. Competitive Environment: Nodes compete to improve their performance and capabilities.

  3. 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

  1. Router Validation: Initial check of task completion and basic quality assessment.

  2. Guard Node Validation: In-depth verification of results and scoring for accuracy.

  3. 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

  1. Continuous Improvement: Encourages nodes to upgrade their hardware and optimize performance.

  2. Dynamic Network Adaptation: The network evolves to meet changing demands and technological advancements.

  3. High-Quality Results: Rigorous validation ensures reliable and accurate AI inference outputs.

  4. 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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Last updated 8 months ago