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AI Connect Whitepaper
  • Project Introduction
  • 1. Project Background and Vision
  • 2. Introduction to Computing Power Networks
    • 2.1 Data as a New Factor of Production, Giving Rise to Computing Power Networks
    • 2.2 The Current Distribution of Computing Power Supply and Demand Shows Decentralized and Unbalanced
    • 2.3 The Metaverse Era, Where New Technologies Like VR and AR Are Closely Related to High Bandwidth a
    • 2.4 Computing Power Networks Refer to the Integration of Cloud, Network, and Edge for Unified Comput
    • 2.5 Computing Power Networks Build the Network Foundation for the Development of the Metaverse
  • 3. Market Demand for the Development of Computing Power Networks
    • 3.1 The AI Wave Boosts Computing Power Demand, and Achieving Scalability of Intelligent Technology R
    • 3.2 Industry Applications of Large Model Training Also Require a Large Amount of Intelligent Computi
    • 3.3 The AI Application Has a Long Tail Effect, and Achieving Scalability Requires First Achieving Un
  • 4. AIConnect Computing Power Network Construction Plan
    • 4.1 Computing Power Supply Services
    • 4.2 The Infrastructure and Marketization of Computing Power Scheduling
    • 4.3 The Commercial Demand for Computing Power Scheduling
  • 5. Global Development of Computing Power Networks and DePIN
    • 5.1 Development Advantages of DePIN Combined with AI and Crypto
    • 5.2 AIConnect's Investment in AI Edge Computing Model Training and Development
  • 6. Joint Construction and Participation in AIConnect's Computing Power Network
    • 6.1 Introduction to the Role of AIC Token Assets in AIConnect
    • 6.2 Design Advantages of AIC Tokens
    • 6.3 Introduction to the Business Model of AIC Tokens
      • 6.3.1 AIC Token Production
      • 6.3.2 Node Participation
  • 7. Development Roadmap
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  1. 3. Market Demand for the Development of Computing Power Networks

3.2 Industry Applications of Large Model Training Also Require a Large Amount of Intelligent Computi

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Last updated 1 year ago

Large model training in vertical industries also requires a significant amount of intelligent computing power. Moreover, multi-scenario applications based on large models are continuously expanding. AI is penetrating various industries, driving rapid growth in the scale of intelligent computing power. In 2022, the penetration of AI applications across various industries continued to deepen, especially in finance, telecommunications, manufacturing, and healthcare. To achieve business growth, maintain strong competitiveness, and capture a larger market share, companies are increasingly entering the AI field, using new technologies to enhance the user experience of traditional businesses, and the growth of AI applications is rapid. According to IDC's "2022-2023 Global Artificial Intelligence Computing Power Development Assessment Report," it is expected that by the end of 2023, 50% of the manufacturing supply chain will adopt artificial intelligence technology to enhance business experience. In the future, as the empowering role of AI technology in traditional industries becomes increasingly apparent, it will inevitably give rise to a greater demand for intelligent computing power.