3. Value Data Layer

3.1 Importance of the Data Layer At the core of AI development, the efficiency of data selection is crucial. The GAEA value data layer aims to build more valuable datasets through efficient data screening and optimization, providing reliable reference samples for AI training, thereby enhancing the intelligence level and application value of AI.

3.2 Data Collection and Processing Mechanism GAEA connects to users' networks and utilizes their idle network resources to access public networks and collect the necessary data. GAEA nodes are responsible for filtering redundant data, splitting datasets, and gathering the required information. This process includes the use of ZK processors and OP processors to form a dynamic GAEA dataset, ensuring data diversity and the reliability of technical screening.

3.2.1 Data Collection Users contribute their idle network bandwidth and computational power by running GAEA nodes. These resources are used to access public networks and gather a wide variety of data sources, including text, images, videos, and more. By leveraging users' idle resources, GAEA is able to collect data on a large scale and with high efficiency, ensuring the diversity and richness of the datasets.

3.2.2 Data Filtering and Splitting GAEA uses ZK (Zero-Knowledge) processors to perform initial filtering of the collected data, removing duplicate and irrelevant data to ensure the purity and relevance of the dataset. This process is achieved through Natural Language Processing (NLP) and image recognition technologies, enabling automated data screening and classification. After filtering, OP (Operation Processor) processors split the filtered data into smaller data chunks, facilitating distributed processing and training. This process not only enhances data processing efficiency but also ensures the high quality and diversity of the datasets.

3.2.3 Data Optimization and Feedback Loop GAEA nodes control access to the public network and engage in continuous optimization based on feedback from the AI models. This iterative feedback loop helps improve data quality, ensuring that the datasets evolve to meet the ever-changing demands of AI training. This process enhances the value of the data, making it more relevant and effective for AI applications.

3.3 Construction of GAEA’s Value Data Layer GAEA’s value data layer is built through the following steps:

1. Data Collection: Utilize users' idle network resources to access public networks and collect the necessary data.

2. Data Processing: Use ZK and OP processors to filter and split the collected data, creating high-quality datasets.

3. Data Optimization: Interact with mainstream AI models to optimize data collection strategies, forming a more effective optimization feedback loop.

3.4 Data Optimization Feedback Loop GAEA nodes control access to public networks to collect data that better aligns with AI expectations, completing the entire system’s optimization feedback loop. This mechanism ensures the efficiency and relevance of data selection, improving the quality and effectiveness of AI training.

3.5 Commercial Data and Market Analysis According to market research reports, the global AI market is expected to grow at a compound annual growth rate (CAGR) of 42% over the next five years, reaching $1.81 trillion by 2028. At the same time, the data processing and management market is also expanding rapidly, with projections to reach $230 billion by 2028. Through its unique value data layer and decentralized network architecture, GAEA is positioned to play a significant role in these two rapidly growing markets, providing efficient data processing and optimization services to meet AI technology’s demand for high-quality data.