Participants

The network comprises various elements, including data, algorithms, and participants. However, the most crucial element of the network is the participants.

The network uses incentives to encourage participants to interact with the AI and data, contribute their expertise and abilities, and make the network iteratively upgrade. In this article, we will dive into the three main participants of the network and the critical concept of Corpus.

Corpus

Corpus is a collection of data organized into a dataset. In Web3Go Data Intelligence Network, Corpus includes both on-chain and off-chain data. On-chain data comprises data structured on the blockchain, such as a specific transaction in an address or a decentralized exchange's trading volume over a specific period. Off-chain data includes news, research reports, social media posts, and any other content.

To create a trainable model, the Corpus needs to have the following features:

• Size: The more Corpus, the better. A large, specialized dataset is critical for training a proprietary network.

• High-quality data: High-quality data is essential for Corpus. Since Corpus requires vast amounts of data, even minor errors in the training data can lead to massive errors in the outputs of the deep learning system.

• Data cleaning: Data cleaning is also critical for creating and maintaining high-quality Corpus. Data cleaning allows for the identification and elimination of any errors or duplicate data, creating a more reliable Corpus for model training.

Data Activity Participants

For on-chain data, data analysts need to clean and structure the data according to business logic, which requires professional handling capabilities specific to data. For off-chain data such as news, research reports, and tweets, part-of-speech tagging and word segmentation are required to clean the data. This part of the work requires a certain understanding of the blockchain industry, which is data literacy. Analysts and annotators use the data collaboration platform to clean and organize the data, while validators check the data. These structured and validated on-chain and off-chain data are used to enrich the corpus and strengthen the neural network. All participants receive system tokens as incentives for their labor.

Validator

Validators are responsible for verifying and ensuring the quality of the Corpus, identifying errors, and correcting them. Their work is also rewarded with system tokens.

Consumer

Consumers use the data insights generated by AI based on on-chain and off-chain data to make decisions. They pay system tokens to access the data's usage rights and can report data errors to the system, which is also rewarded with system tokens.

In conclusion, Web3Go Data Intelligence Network has the vision to build a reasonable production relationship and enhance the productivity of human, data, and AI through human production, cleaning, and verification of data. The whole process is incentivized with system tokens to keep the network iterative. Four roles are designed to participate in the network's construction: Annotator, Validator, Consumer, and AI. The network leverages a decentralized model to create a platform for data processing, analysis, and insights that are reliable, efficient, and trustworthy.