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
    • Team
  • 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
  • Introduction
  • Vision
  • Problem Statement
  • Cortensor's Solution
  • Conclusion
  1. Abstract
  2. Whitepaper

Page 1: Introduction and Vision

Introduction

Artificial Intelligence (AI) is rapidly transforming industries and societies by offering unprecedented capabilities for automation, decision-making, and data analysis. However, the centralization of AI resources, data, and computing power poses significant challenges to accessibility, transparency, and innovation. To address these challenges, Cortensor proposes a decentralized AI inference network that democratizes access to AI technologies, empowers communities, and fosters a robust ecosystem of AI-driven applications.

Cortensor is a decentralized, community-powered platform designed to provide scalable, secure, and efficient AI inference services. By leveraging blockchain technology, Cortensor ensures that AI resources are distributed across a global network of nodes, creating a decentralized infrastructure that is both resilient and inclusive. Our vision is to make advanced AI tools accessible to everyone, regardless of their technical or financial resources, by integrating open-source models and incentivizing participation through a native token economy.

Vision

Cortensor aims to revolutionize the AI landscape by creating a decentralized network that offers:

  • Universal AI Accessibility: A platform where AI inference services are available to all, with easy integration across Web2 and Web3 ecosystems through REST API and Web3 SDK support.

  • Incentivized Community Participation: A network that rewards contributors for providing computational resources, validating tasks, and engaging with the community. By integrating a native token, $COR, Cortensor ensures that all participants are fairly compensated for their contributions.

  • Scalable AI Infrastructure: A robust, scalable platform that can handle a wide range of AI tasks, from simple classifications to complex generative models, making it suitable for a variety of applications across different industries.

  • Open-Source AI Models: Cortensor is committed to integrating open-source AI models, starting with Llama, and expanding to other models over time. This ensures that the platform remains flexible, adaptable, and aligned with the latest advancements in AI research.

  • Decentralized Governance: Empowering the community to have a say in the platform's future development and direction through decentralized governance mechanisms.

Problem Statement

The current AI ecosystem is dominated by a few large entities that control the majority of AI resources and data. This centralization leads to several issues:

  • Limited Accessibility: Access to advanced AI models and computing power is often restricted to those who can afford expensive infrastructure, creating barriers for smaller players and innovators.

  • Lack of Transparency: Centralized AI systems operate as "black boxes," making it difficult to understand or verify the processes behind AI decision-making.

  • Scalability Challenges: Centralized infrastructures are often bottlenecks, limiting the scalability of AI applications and preventing widespread adoption.

  • Innovation Stifling: The concentration of AI resources in the hands of a few entities stifles innovation and hinders the development of new, cutting-edge applications.

Cortensor's Solution

Cortensor addresses these challenges by creating a decentralized AI inference network that:

  • Decentralizes AI Resources: By distributing AI computation across a global network of nodes, Cortensor democratizes access to AI tools and ensures that no single entity controls the majority of resources.

  • Enhances Transparency: Through the use of blockchain technology and decentralized validation mechanisms, Cortensor provides a transparent, verifiable, and secure AI infrastructure.

  • Promotes Scalability: The decentralized nature of Cortensor allows the network to scale effortlessly as more nodes join, ensuring that AI services can meet growing demand without compromising performance.

  • Fosters Innovation: By incentivizing community participation and supporting open-source models, Cortensor creates an environment where innovation can thrive, enabling the development of novel AI applications that benefit society at large.

Conclusion

Cortensor represents the next evolution in AI infrastructure, bringing together the power of decentralization, open-source collaboration, and community-driven innovation. By addressing the limitations of centralized AI systems, Cortensor paves the way for a future where AI is accessible, transparent, and scalable for all.


Disclaimer: This page and the associated documents are currently a work in progress. The information provided may not be up to date and is subject to change at any time.

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Last updated 9 months ago