About the Network

Testnet

Target Audience: Developers, data scientists, and AI enthusiasts.

Goals: To establish the world's largest decentralized AI training and inference protocol, enhancing the accessibility and capabilities of artificial intelligence.

Activities and Features: Multi-stage Testnet:

Stage 1: Deploy preliminary network framework, centralized testing of basic network connectivity and GPU compatibility.

Stage 2: Introduce basic AI training functionalities and test simple models to validate system processing capabilities.

Stage 3 : Test advanced AI functionalities such as deep learning and complex model inference, evaluate network scalability and efficiency.

Incentive Mechanisms: Offer rewards of up to 10 million AET TOKENS to incentivize participants to report vulnerabilities, provide performance optimization suggestions, and improve functionalities.

Hardware and Algorithm Testing: Conduct extensive testing using GPUs with different configurations and performance to ensure network support for a wide range of hardware. Conduct benchmark testing of various AI algorithms, collect and analyze performance data, optimize algorithm execution efficiency.

Network Bootstrapping:

Initially utilize designated GPU suppliers to bootstrap and stabilize the network, ensuring stability and reliability in the early stages of the network.

Distributed Node Incentives: Node Maintainer Rewards:

Provide additional incentives for nodes that maintain network stability and efficiency. This includes rewarding nodes that provide extra computing resources, storage, and bandwidth to ensure high performance and reliability of the network. Dynamic Incentive Adjustments: Dynamically adjust incentive strategies based on network demands and node performance to encourage continuous participation and optimize resource allocation.

Enhanced Data Privacy and Security Measures:

Zero-Knowledge Proofs: Introduce zero-knowledge proof technology to enhance data processing privacy, allowing users to verify data processing procedures and results without revealing specific data contents. Multi-Signature and Access Control: Implement multi-signature mechanisms and fine-grained access control policies to ensure the security of data and resource access, preventing unauthorized access and data leaks.

Automated Upgrade and Management of Smart Contracts:

Contract Lifecycle Management: Develop smart contract lifecycle management tools to support the automated updating and maintenance of smart contracts, reducing manual intervention and potential errors. Automated Compliance Checks: Integrate automated compliance check tools to ensure that all smart contracts comply with current regulations and standards before and after deployment.

AI Model Marketplace and Sharing Platform:

Model Sharing and Trading: Create a decentralized AI model marketplace that allows users to share, buy, and rent AI models, promoting knowledge sharing and innovation. Copyright Protection and Tracking: Implement a blockchain-based copyright management system to protect the intellectual property of AI models while tracking model usage to ensure creators' rights.

Ecosystem Development Fund: Innovation Funding Program: Establish an ecosystem development fund to support projects and teams developing innovative applications on the AIET platform, especially those that can bring about social and economic impact. Education and Training Support: Provide training and educational resources for developers and new users, including online courses, seminars, and technical support, to accelerate ecosystem growth and participation.

Mainnet

Goals: To provide an efficient, cost-effective, and user-friendly platform that enables users to quickly initiate and scale AI model training projects.

Activities and Features: Rapid Model Training:

Instant Deployment: Users experience seamless and rapid transitions from account creation to model deployment through a clear interface and predefined templates. Resource Scalability: Users can access and scale comprehensive GPU resources as needed, increasing or decreasing resource usage without time constraints.

AIET Swap and Tax Mechanism:

Bridge Support: Support the exchange of tokens from Solana and other blockchains into $AET, with a 1% transaction tax. Part of this tax is allocated for token market buybacks and burns to increase scarcity and token value.

AIET GPU Servers: Dedicated Payment: Introduce dedicated GPU servers that only accept $AET token payments, catering to the high-performance requirements of AI projects. Buyback Mechanism: Allocate 10% of platform revenue for $AET token buybacks, supporting long-term value growth of the token.

API and Ecosystem Integration:

Develop and provide robust API interfaces to facilitate developers' easy training and deployment of AI models within the AIET ecosystem. Promote integration and usage of the AIET platform through efficient APIs and toolsets, enhancing user satisfaction and engagement.

Scalable Microservices Architecture Modular Deployment: Adopting a microservices architecture allows each component of the platform to run and scale independently, enhancing system flexibility and maintainability. Fault Tolerance Mechanisms: Implement advanced fault tolerance techniques such as service replication and load balancing to ensure high availability and consistent service performance.

AI-Assisted Network Monitoring and Management AI-Driven Network Optimization:

Utilize AI algorithms for real-time analysis and optimization of network performance, automatically adjusting resource allocation and predicting and resolving potential network issues.

Intelligent Security Monitoring: Integrate AI-driven security systems for real-time monitoring and response to network attacks and anomalies, safeguarding user data and transaction security.

Decentralized Data Governance Data Governance Framework:

Establish a decentralized data governance framework that allows users to participate in data policy formulation and updates through voting and consensus mechanisms. Transparent Data Auditing: Utilize blockchain technology to record audit trails of all data operations, enhancing transparency and trustworthiness of data processing.

Integrated Development Environment (IDE) and Toolchains Cloud-based IDE:

Provide a fully integrated cloud-based development environment supporting coding, testing, deployment, and monitoring of AI models, streamlining the development process. Developer Toolbox: Develop a comprehensive set of developer tools including API debugging, performance analysis, and resource monitoring tools to assist developers in optimizing their applications.

Cross-Platform and Cross-Device Support Multi-Platform Clients:

Develop client applications for desktop, mobile, and other smart devices to ensure users can access AIET services on any device. Internet of Things (IoT) Integration: Support direct connection of IoT devices to the AIET platform, enabling them to leverage distributed AI capabilities for intelligent management and data processing.

Global Expansion and Localization Strategy Multi-Language Support:

Provide platform and documentation versions in multiple languages to meet the needs of global users. Region-Specific Partnerships: Establish partnerships with businesses and organizations in different regions to customize solutions to meet specific market regulations and requirements.