How is the development of the AI data labeling track advocated by Zhao Changpeng now?

Rachel, Jinse Finance

On November 27, Zhao Changpeng posted on X that tasks such as AI data labeling are very suitable for completion through blockchain, leveraging a global low-cost labor force and enabling instant payment through cryptocurrency, breaking geographical limitations.

Data labeling refers to the manual or automated annotation of raw data (such as text, images, audio, etc.) to give it specific structured information. The labeled data is used to train machine learning or artificial intelligence models, such as labeling sentiment categories (positive, negative, neutral) for text, which is a form of data labeling. Using blockchain for AI data labeling is particularly suitable for data labeling scenarios that require high transparency, credibility, and distributed collaboration. This not only enhances the efficiency and quality of data labeling but also creates new possibilities for global collaboration and data trading.

What are the quality projects currently in this sector? What is the development prospect of the sector?

The Role of Blockchain in AI Data Labeling

Blockchain is a decentralized distributed ledger technology characterized by transparency, immutability, and traceability. These characteristics can address the following issues in traditional methods in data tagging:

• Data Authenticity and Tamper Resistance: Each tagged record is written to the blockchain and cannot be arbitrarily changed, ensuring the credibility of the labeling.
• Task Assignment Transparency: Blockchain can record the distribution, execution, and review process of tasks, preventing unfair task assignment or result tampering.
• Incentive Mechanism: Using blockchain smart contract technology, data annotators can automatically receive cryptocurrency or other rewards by completing tasks.
• Data Traceability: Information about the source of each label, the annotator, and the reviewer can be tracked.

Application Scenarios

• Distributed Annotation: Utilize blockchain to assign data annotation tasks to annotators worldwide, improving data processing efficiency.
• Quality Audit: Multiple annotation results are compared and audited using blockchain technology to ensure the accuracy of the annotations.
• Annotated Data Trading: Well-annotated data can be traded on the blockchain, allowing both buyers and sellers to be assured of the integrity and authenticity of the data.
• Privacy Protection: Use blockchain to encrypt and store annotated data, ensuring the security of private data.

Related Projects

• OORT DataHub: Provides a blockchain-based decentralized data labeling service that uses the Proof of Honesty algorithm for quality control. Its platform distributes tasks, audits data quality, and pays rewards through smart contracts, attracting global labelers to join while ensuring the transparency and privacy protection of labeled data.

The economic model of the project token is as follows:

Community Rewards*: By participating in data labeling and analysis, users can earn $OORT token rewards. Additionally, there may be unique NFTs linked to contributions, which provide extra benefits such as rewards for increasing annual yield (APY), device discounts, and DAO voting rights.*

Task Staking*: Participants must stake at least 210 $OORT tokens to demonstrate their commitment to the task. After completing the task, the tokens will be returned and rewards will be distributed.*

Sales Revenue Sharing*: Some NFT holders can also receive dividends from future data sales revenue, further enhancing long-term gains.*

• PublicAI: An AI ecosystem project on the Solana blockchain, aimed at connecting data demanders and global annotators, rewarding participants through a cryptocurrency incentive mechanism, while utilizing blockchain technology to record the details of the annotation process, ensuring data security and privacy.

The economic model of the project token is as follows:

Community Rewards:* 10% of the Public tokens will be used for airdrop rewards for users' early interactions. Specifically, there are three ways to obtain airdrops: Become an AI Builder: Collect high-quality internet content; *Become an AI Validator: Validate the collected content; Become an AI Developer: Train AI agents using the validated dataset.

Token Distribution***:**** The project completed a $2 million seed round financing in January 2024, with investors including IOBC Capital, Foresight Ventures, Solana Foundation, Everstate Capital, and several well-known scholars and professors in the field of artificial intelligence. The specific PublicAI token distribution details have not yet been clarified.*

Challenges Faced

Currently, several factors are constraining the development of this sector: first, AI data labeling requires high computational and storage resources; second, project performance is limited by the scalability of blockchain; third, technical standardization and regulation are not yet完善.

Among them, the second point is perhaps the biggest challenge currently faced. This is because AI data labeling and model training typically require a large amount of computing resources, while the computing power of nodes in a blockchain network is limited. How to effectively integrate and utilize distributed computing resources to meet the computational needs of AI data labeling projects while ensuring the decentralized characteristics of blockchain is a pressing issue that needs to be addressed. It is reported that Binance's Greenfield is providing storage support for this sector, and there is hope for more storage and computing resources to be practiced in this field.