How is the AI ​​data labeling track that Changpeng Zhao promoted developing now?

Rachel, Golden Finance

On November 27, Zhao Changpeng posted on X that tasks such as AI data labeling are very suitable for completion through blockchain, which can be achieved with the help of low-cost global labor and instant payment through cryptocurrency, breaking geographical restrictions.

Data labeling refers to the manual or automatic labeling of raw data (such as text, images, audio, etc.) to give it specific structured information. Labeled data is used to train machine learning or artificial intelligence models. For example, labeling text with emotional categories (positive, negative, neutral) is a kind of data labeling. The use of blockchain for artificial intelligence data labeling is particularly suitable for data labeling scenarios that require high transparency, credibility and distributed collaboration. This not only improves the efficiency and quality of data labeling, but also creates new possibilities for global collaboration and data trading.

What are the high-quality projects in this track at present? What is the development prospect of the track?

The role of blockchain in AI data labeling

Blockchain is a decentralized distributed ledger technology with characteristics such as transparency, immutability and traceability. These characteristics can solve the following problems in traditional methods in data labeling:

• Data authenticity and tamper-proof: Each marked record is written into the blockchain and cannot be changed at will, ensuring the credibility of the labeling.

• Task allocation transparency: Blockchain can record the distribution, execution and review process of tasks to prevent unfair task allocation or result tampering.

• Incentive mechanism: Using blockchain's smart contract technology, data labelers can automatically obtain cryptocurrency or other rewards by completing tasks.

• Data traceability: The source of each mark, the information of the annotator and the reviewer can be tracked.

Application scenarios

• Distributed annotation: Using blockchain, data annotation tasks are assigned to annotators around the world, making data processing more efficient.

• Quality review: The annotation results of multiple people are compared and reviewed through blockchain technology to ensure the accuracy of annotation.

• Annotated data transaction: The annotated data can be traded on the blockchain, and the buyer and seller do not need to worry about the integrity or authenticity of the data.

• Privacy protection: The blockchain is used to encrypt and store the annotated data to ensure the security of private data.

Related Projects

• OORT DataHub:Provides decentralized data annotation services based on blockchain, using the Proof of Honesty algorithm for quality control. Its platform distributes tasks, audits data quality, and pays rewards through smart contracts, attracting global annotators to join, and ensuring the transparency and privacy protection of annotated data.

The economic model of the project token is as follows:

Community Rewards: Users can be rewarded with $OORT tokens by participating in data annotation and analysis. In addition, they may also receive unique NFTs linked to their contributions, which provide additional benefits, such as rewards for increased annual yields (APY), equipment discounts, and DAO voting rights.

Task Mortgage: Participants need to pledge at least 210 $OORT tokens to show their commitment to the task. After completing the task, the tokens will be returned and rewards will be issued.

Sales Proceeds Sharing: Some NFT holders can also receive dividends from future data sales revenue, further increasing long-term returns.

• PublicAI:Solana's on-chain AI ecosystem project aims to connect data demanders and global annotators, reward participants through a cryptographic token incentive mechanism, and use blockchain technology to record the details of the annotation process to ensure 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 early user interactions. Specifically, there are three ways to obtain airdrops:Become an AI Builder: collect high-quality Internet content;Become an AI Validator: verify the collected content;

Become an AI Developer: train AI agents using verified data sets.

Token Allocation:The project completed a $2 million seed round of financing in January 2024. Investors include IOBC Capital, Foresight Ventures, Solana Foundation, Everstate Capital, and many well-known academicians and professors in the field of artificial intelligence. At present, the specific details of the PublicAI token allocation have not yet been clarified.

Challenges

Currently, several factors constrain the development of this track: first, AI data labeling requires high computing and storage resources; second, project performance is subject to the scalability of blockchain; third, technical standardization and supervision are not yet perfect.

Among them, the second point is perhaps the biggest challenge currently faced. Because AI data labeling and model training usually require a lot of computing resources, and the computing power of nodes in the blockchain network is limited. How to effectively integrate and utilize distributed computing resources to meet the computing needs of AI data labeling projects while ensuring the decentralized nature of blockchain is an urgent problem to be solved. It is reported that Greenfield, a subsidiary of Binance, is providing storage support for this track, and we look forward to having more storage and computing resources to practice in this field.