Decentralized storage structure and its application in artificial intelligence

Decentralized storage structure and its application in artificial intelligence

It mainly proposes a method to store AI learning results by adapting the blockchain storage structure, and describes the system architecture, operation process, and application scenarios in detail. The following is a summary of the key points:

Decentralized storage and blockchain technology:

Blockchain storage structure: The document proposes a method to use blockchain to store AI learning results, verifying and storing machine learning content through hash codes on nodes without directly storing underlying media or files.

Community consensus: Hash codes are determined through a competitive process among community learners on a distributed network to form a consensus interpretation of machine learning.

Nodes and hash codes:

Node composition: The nodes of the blockchain contain hash codes generated by community learners, which represent machine learning content after verification.

Verification process: Hash codes are verified through competition and consensus processes among community learners to ensure that hash codes represent valid machine learning.

Data storage and query:

Storage method: Learning content is stored on the blockchain in the form of hash codes instead of actual media files, which can reduce the demand for storage space.

Query function: Customers can query and determine new insights by establishing query conditions and searching the blockchain to leverage the community's extensive AI learning results.

System architecture and components:

Architecture overview: The system includes mining software, custom blockchain and simple RESTful API, which realize a decentralized AI computing platform.

Component functions: Mining software (VOSAI Learner) allows miners to perform machine learning calculations, custom blockchain (PATHWAY) serves as a decentralized database, and RESTful API serves as an interface for customers to interact with the platform.

Incentive mechanism and tokens:

Token economy: The system uses tokens (such as VOSAI tokens) to incentivize miners to perform machine learning calculations. The distribution of tokens is based on work units (UoW) and work quality (QoW).

Market mechanism: Tokens can be bought and sold on the market to form a token economy. Miners can obtain tokens by completing learning tasks and sell or exchange them on the market.

Application scenarios and advantages:

Wide application: The system can be applied to multiple industries such as agriculture, construction, law enforcement, infrastructure, etc., to improve efficiency and accuracy through decentralized AI computing.

Outstanding advantages: Compared with traditional cloud computing, the system has higher transparency, security, scalability and cost-effectiveness, and can promote the popularization and innovation of AI technology.

Future development and governance:

Open ecosystem: The system is built on open source technology and systems, managed by a governance body, and provides a decentralized computing platform that is easy to migrate and expand.

Innovation promotion: Through token incentives and open platforms, developers, miners, and partners are encouraged to participate in the construction of the AI ​​ecosystem and jointly promote the development and application of AI technology.

What is a decentralized storage structure and how is it applied to the AI ​​field in the document?

A decentralized storage structure is a way to store data in multiple nodes in a decentralized manner without relying on a single centralized server. In the document, this structure is applied to the AI ​​field, using blockchain technology to store hash codes for AI learning instead of actual media files to achieve decentralized AI data storage and query.

What are the key features of the blockchain storage structure proposed in the document?

The key features include: using hash codes to represent AI learning content, verifying hash codes through competition and consensus processes among community learners on a distributed network, storing hash codes instead of actual media files to reduce storage space requirements, and allowing customers to obtain new insights by querying the blockchain.

Briefly describe the system architecture and component functions in the document.

The system architecture includes mining software (VOSAI Learner), a custom blockchain (PATHWAY), and a simple RESTful API. The mining software allows miners to perform machine learning calculations, the custom blockchain acts as a decentralized database to store hash codes, and the RESTful API acts as an interface for clients to interact with the platform.

What role do tokens play in the system proposed in the document?

Tokens (such as VOSAI tokens) act as an incentive mechanism in the system. Miners obtain tokens by completing machine learning computing tasks. The distribution of tokens is based on units of work (UoW) and quality of work (QoW). Tokens can be bought and sold on the market to form a token economy, which incentivizes more participants to join the construction of the AI ​​ecosystem.

What are the advantages of the system in the document over traditional cloud computing?

Compared with traditional cloud computing, the system in the document has higher transparency, security, scalability, and cost-effectiveness. Through a decentralized storage structure and blockchain technology, the system can achieve more efficient and secure AI computing, and promote the popularization and innovation of AI technology. At the same time, the introduction of a token economy also provides more economic incentives for participants.