FileMarket AI
  • 🌐FileMarket: Data Platform for Human and AI Agents
  • ⛩️1. Introduction
    • 1.1 Overview
    • 1.2 Key Features
  • ⚙️2. System Architecture
    • 2.1 Multi-chain Data Platform
    • 2.2 Community-driven Data Collection in Social Media
    • 2.3 Datasets Tokenization
    • 2.4 AI-Powered Data Processing
    • 2.5 Decentralized Compute Network
    • 2.6 Decentralized Storage
    • 2.7 Decentralized Exchanges (DEXs) in AI Data Economy
  • 💱3. FileMarket AI Data Economy
    • 3.1 How the Data Economy Works
    • 3.2 The Key Participants & Their Roles
    • 3.3 Future Vision: A Fully Autonomous AI Data Marketplace
  • 🕹️4. Gamified Data Collection
    • 4.1 Data Quests
      • 4.1.1 Types of Data Quests
      • 4.1.2 Compensation and Rewards
      • 4.1.3 Boosting Earnings with Referrals
      • 4.1.4 Gamified Earning System
      • 4.1.5 Participation Process
    • 4.2 Scaling Through Gamification
      • 4.2.1 Key Strategies
  • 🗓️5. Technical Roadmap
    • Phase 1: Product Market Fit and SEED Round
    • Phase 2: Data Platform Launch and Multi-Chain Expansion
    • Phase 3: AI Compute & Validator Network launch and Multichain Expansion
  • 🧮6. Tokenomics
    • 6.1 Overview
    • 6.2 Multi-Token Architecture
      • 6.2.1 $DATA Token
      • 6.2.2 $DATASET_X Tokens
    • 6.3 Token Allocation & Vesting
      • 6.3.1 $DATA Token Allocation
      • 6.3.2 DatasetX Token Allocation
    • 6.4 Unlock Schedule & Sell Pressure Management
      • 6.4.1 $DATA Unlocks
      • 6.4.2 DatasetX Unlocks
    • 6.5 TGE Roadmap
    • 6.6 Why the Multi-Token Model Matters
  • ❓7. FAQ
  • 📥8. CONTACT US
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  1. 2. System Architecture

2.4 AI-Powered Data Processing

FileMarketAI leverages advanced AI Agents to streamline data processing, ensuring efficiency, scalability, and top-tier quality.

Core Features:

  • Automated AI Labeling: Fast, accurate data labeling reduces costs and manual workload.

  • AI Validation & Quality Assurance: Smart algorithms detect anomalies, ensuring data consistency.

  • Human-in-the-Loop (HITL): Human validators refine AI outputs for complex, context-sensitive data.

  • Self-Labeling with AI Double-Check: Contributors label their data, with AI rigorously verifying accuracy.

  • Continuous Learning: AI improves over time through feedback, enhancing precision with each dataset.

This hybrid model guarantees high-quality, reliable datasets for AI training and fine-tuning.

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Last updated 4 months ago

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