Matrix Layer Protocol
  • I. Project overview
  • II. The Contradiction between Terminal Devices and Centralized Networks
    • 1. Data Control and Privacy Issues
    • 2. Decentralization Needs and Hardware Bottlenecks
    • 3. Market Monopoly of Centralized Platforms
    • 4. High Costs and Low Efficiency of Decentralized Networks
    • 5. The Contradiction of Terminal Devices as Data Entry Points
    • Trends in Solutions
  • III. Technical Solution: AI-Driven Terminal Device Network Communication Method
    • 1. AI-Driven Intelligent Routing and Peer-to-Peer Data Transmission
    • 2. AI Applications in Decentralized Networks
    • 3. AI Computing Power Management for Terminal Devices
    • 4. Data Privacy and Security Protection
    • 5. Adaptive Network Communication Protocol
  • IV. Technical Advantages and Protocol Value of MLP
    • 1. Technical Advantages
    • 2.Protocol Value
  • V. MLPhone: Application Product Based on MLP
    • 1. Decentralized Communication and Data Management
    • 2. Decentralized Finance (DeFi) and UBI Identity Verification
    • 3. Smart IoT Device Management and Integration
    • 4. Access to Metaverse and Web3 Applications Leveraging
    • 5. AI and Automated Device Management
  • VI. AI Ecosystem Platform Overview
    • 1. AI Ecosystem Platform - AICIAR Platform Introduction
    • 2. Introduction to AI Investment Advisor
    • 3. Introduction to other AI components
  • VII. The Ecological Application Development of MLP and MLPhone
    • 1. Expansion of Decentralized Device Network
    • 2. Ecological Development of Digital Identity and Data Autonomy
    • 3. Decentralized Finance Ecological (DeFi)
    • 4. Support for Web3 and the Metaverse Ecological
    • 5. Development and Ecological Prosperity of Decentralized Applications (DApp)
  • VIII. Token Economic Model and Mechanism
    • 1. Token Allocation
    • 2. Basic Pool Mining (PoW)
    • 3. NFT Series
    • 4. Accelerated Pool Staking (PoS)
    • 5. Promotion Incentive
  • IX. MLP and MLPhone Project Development Roadmap
  • X. Token Investment Risk Notice
    • 1. Market Volatility Risk
    • 2. Technical Risk
    • 3. Privacy and Data Security Risk
    • 4. Regulatory and Legal Risk
    • 5. User Operation Risk
Powered by GitBook
On this page
  1. VI. AI Ecosystem Platform Overview

3. Introduction to other AI components

  • AI Data Annotation and Incentive Mechanism: MLP encourages community members to participate in AI data annotation tasks, optimizing the performance of the platform's core AI models through this collaborative effort. Users who complete data annotation tasks can receive token rewards, which not only improves the quality of model training but also enhances the community's sense of participation and belonging. Data annotation tasks are decentralized and assigned to different community members, ensuring fairness and transparency in the data annotation process. Each annotation task undergoes multiple reviews to ensure data quality. Users can not only gain economic incentives but also obtain additional ecological benefits through a points system, such as participating in new feature testing or acquiring exclusive platform NFTs.

  • AI Model Marketplace KKNET: The AI ecosystem platform also includes a decentralized AI model marketplace, KKNET, where developers can publish and trade their own AI models. Protected by the security of smart contracts, developers can ensure privacy and security in the model trading process, while monetizing the value of their models through tokenization mechanisms, allowing users to rent or purchase these models for various application scenarios. KKNET platform is not only a marketplace for model trading but also a window for developers to showcase and promote their technology. Developers can create detailed pages for their models on KKNET, including model performance, application scenarios, and usage tutorials, allowing potential users to better understand the value of the models. KKNET also supports real-time model updates, enabling developers to continuously optimize and iterate models based on user feedback, thereby enhancing the competitiveness and user satisfaction of the models.

  • AI Virtual Humans: Through the GANTRY platform, developers and users can create their own AI virtual humans and apply them to virtual assistants, entertainment, and personalized services, increasing the interactivity and fun of the platform and promoting innovative applications of virtual images in decentralized ecosystems. AI virtual humans not only have a high degree of customization, but users can also adjust the appearance, voice, and personality traits of the virtual humans through a simple interface. The GANTRY platform also supports the continuous evolution of virtual humans through machine learning, understanding users' habits and preferences to provide more personalized services. For example, virtual humans can act as DeFi advisors, helping users manage investment portfolios, or as social companions, participating in users' daily entertainment activities.

MLP's AI ecosystem platform comprehensively supports the intelligent development of decentralized finance and other fields through the open-source framework of the AICIAR platform, personalized services of AI investment advisors, and innovative applications of the KKNET model market and AI virtual humans. The various components within the platform work in concert, bringing more possibilities and innovation opportunities to the MLP ecosystem. Through community co-construction and decentralized governance, MLP is gradually realizing a diversified, intelligent, and decentralized future.

Previous2. Introduction to AI Investment AdvisorNextVII. The Ecological Application Development of MLP and MLPhone

Last updated 5 months ago