On November 27, Zhao Changpeng posted on X that tasks such as AI data labeling are very suitable to be completed through blockchain, leveraging global low-cost labor and enabling instant payment through cryptocurrency, breaking geographical restrictions.
Data labeling refers to the process of manually or automatically annotating 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, such as categorizing the sentiment of text (positive, negative, neutral) which is a form of data labeling. Using blockchain for AI data labeling is particularly suitable for data annotation scenarios that require high transparency, reliability, 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.
Currently, what are the high-quality projects in this sector? What is the development outlook for the sector?
The Role of Blockchain in AI Data Labeling
Blockchain is a decentralized distributed ledger technology characterized by transparency, immutability, and traceability. These features can address the following issues in traditional methods in data tagging:
Data Authenticity and Tamper-Proofing: Each tagged record is written into the blockchain and cannot be altered at will, ensuring the credibility of the annotations.
Task Allocation Transparency: Blockchain can record the distribution, execution, and review processes of tasks, preventing unfair task allocation or result tampering.
Incentive Mechanism: By using blockchain's smart contract technology, data annotators can automatically receive cryptocurrency or other rewards for completing tasks.
Data Traceability: Information about the source of each tag, the annotator, and the reviewer can be tracked.
Application Scenarios
Distributed Annotation: Utilizing blockchain to assign data annotation tasks to annotators worldwide, resulting in higher data processing efficiency.
Quality Audit: The results of multi-person annotations are compared and audited using blockchain technology to ensure annotation accuracy.
Annotated Data Trading: Annotated data can be traded on the blockchain, allowing 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, utilizing the Proof of Honesty algorithm for quality control. Its platform distributes tasks, reviews data quality, and pays rewards through smart contracts, attracting global labelers to join and ensuring the transparency and privacy protection of the 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, users may receive unique NFTs linked to their contributions, which provide extra benefits such as enhanced annual yield rewards of (APY), device discounts, and DAO voting rights.*
Task Staking*: Participants must stake at least 210 $OORT tokens to demonstrate their commitment to the task. Tokens will be returned and rewards will be issued upon completion of the task.*
Sales Revenue Sharing*: Some NFT holders can also receive dividends from future data sales revenue, further enhancing long-term returns.*
PublicAI: An AI ecosystem project on the Solana chain, aiming to connect data demanders with 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 the airdrop: Become an AI Builder: Collect high-quality internet content; *Become an AI Validator: Validate the collected content; Become an AI Developer: Use verified datasets to train AI agents.
Token Distribution***: ***The project completed a $2 million seed round of financing in January 2024, with investors including IOBC Capital, Foresight Ventures, Solana Foundation, Everstate Capital, and several well-known professors and academicians from the field of artificial intelligence. Currently, the specific details of the PublicAI token distribution 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 still not完善.
Among them, the second point may be the biggest challenge currently faced. This is because AI data labeling and model training usually 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 computing needs of AI data labeling projects while ensuring the decentralized characteristics of the blockchain is an urgent problem that needs to be solved. It is reported that Binance's Greenfield is providing storage support for this sector, and we hope to see more storage and computing resources put into practice in this field.
The content is for reference only, not a solicitation or offer. No investment, tax, or legal advice provided. See Disclaimer for more risks disclosure.
How is the AI data labeling track advocated by Zhao Changpeng developing now?
Rachel, Golden Finance
On November 27, Zhao Changpeng posted on X that tasks such as AI data labeling are very suitable to be completed through blockchain, leveraging global low-cost labor and enabling instant payment through cryptocurrency, breaking geographical restrictions.
Data labeling refers to the process of manually or automatically annotating 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, such as categorizing the sentiment of text (positive, negative, neutral) which is a form of data labeling. Using blockchain for AI data labeling is particularly suitable for data annotation scenarios that require high transparency, reliability, 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.
Currently, what are the high-quality projects in this sector? What is the development outlook for the sector?
The Role of Blockchain in AI Data Labeling
Blockchain is a decentralized distributed ledger technology characterized by transparency, immutability, and traceability. These features can address the following issues in traditional methods in data tagging:
Application Scenarios
Related Projects
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, users may receive unique NFTs linked to their contributions, which provide extra benefits such as enhanced annual yield rewards of (APY), device discounts, and DAO voting rights.*
Task Staking*: Participants must stake at least 210 $OORT tokens to demonstrate their commitment to the task. Tokens will be returned and rewards will be issued upon completion of the task.*
Sales Revenue Sharing*: Some NFT holders can also receive dividends from future data sales revenue, further enhancing long-term returns.*
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 the airdrop: Become an AI Builder: Collect high-quality internet content; *Become an AI Validator: Validate the collected content; Become an AI Developer: Use verified datasets to train AI agents.
Token Distribution***: ***The project completed a $2 million seed round of financing in January 2024, with investors including IOBC Capital, Foresight Ventures, Solana Foundation, Everstate Capital, and several well-known professors and academicians from the field of artificial intelligence. Currently, the specific details of the PublicAI token distribution 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 still not完善.
Among them, the second point may be the biggest challenge currently faced. This is because AI data labeling and model training usually 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 computing needs of AI data labeling projects while ensuring the decentralized characteristics of the blockchain is an urgent problem that needs to be solved. It is reported that Binance's Greenfield is providing storage support for this sector, and we hope to see more storage and computing resources put into practice in this field.