Summary
Personal data and public data are important resources and drivers of the AI industry, and there is a close relationship and interaction between them. Personal data represent individual consumption and production, and public data represent the environment and market. Under the traditional Web2.0 architecture, personal data and public data face many problems and challenges, such as privacy leaks, security risks, deprivation of rights, poor quality, and low efficiency. These problems not only harm the interests of individuals and society, but also limit the development and innovation of AI applications. This paper believes that using the Web3 system to architect AI applications is the best design to solve the problems of personal data and public data. The Web3 system is a decentralized, secure, transparent, and credible network architecture based on blockchain technology. It can make the collection process of personal data more autonomous, controlled, and protected, and allow the storage, transmission, exchange, and It is more efficient, convenient and fair to use. This article will analyze how the Web3 system has a positive impact on solving the data feedback loop of personal data and public data, data-driven innovation, data shaping public opinion, and data improving welfare, and exemplifies specific cases of Web3 system architecture AI applications.
Keywords: Web3 system; AI application; personal data; public data
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1. The relationship between personal data and public data and their roles in the AI industry
Personal data refers to personal information such as identity, attributes, behaviors, preferences, etc., usually collected and stored by users themselves or third-party platforms, such as name, age, gender, mobile phone number, location, shopping records, browsing history, etc. Personal data is private and sensitive, and needs to be authorized and protected by users, otherwise it may cause damage to users' rights or data leakage. Public data refers to information that does not involve the privacy or sensitive information of specific or identifiable natural persons, and can be widely used or referenced by all sectors of society, such as government public information, corporate financial reports, scientific research results, statistical survey results, etc.
Personal data and public data are important resources and drivers of the AI industry, and there is a close relationship and interaction between them. On the one hand, personal data is the basic input of AI applications. Through the collection, analysis and processing of large amounts of personal data, AI applications can provide more accurate and personalized services or products to meet the needs or preferences of users. At the same time, users will generate more personal data through the use of AI applications, forming a positive feedback loop and promoting the optimization and innovation of AI applications. On the other hand, public data is an important output of AI applications. Through the generation, release, and sharing of large amounts of public data, AI applications can provide more valuable and influential information or knowledge, and shape social public opinion or decision-making. At the same time, the society will also obtain more public data through the use of AI applications, forming a positive feedback loop and promoting the social value and public welfare of AI applications.
Personal data represent individual consumption and production, and public data represent the environment and market. Personal data reflects the behavior, preferences, needs, capabilities and other characteristics of individuals in different scenarios and fields, and is an important basis and result of individual consumption and production. Public data reflects the status, trends, laws, problems and other characteristics of society at different levels and dimensions, and is an important basis and result of the social environment and market. There are complex interactions and influences between personal and public data. On the one hand, personal data is affected by public data. Factors such as social environment, market changes, policies and regulations will affect individual consumption and production decisions and behaviors. On the other hand, personal data also affects public data, such as individual consumption and production behaviors will also affect social environment, market changes, policies and regulations and other factors.
2. Problems and challenges faced by personal data and public data under the traditional Web2.0 architecture
The traditional Web2.0 architecture is based on a centralized, closed, opaque, and untrustworthy network architecture. It takes Internet companies or platforms as the core and controls the collection, storage, transmission, exchange, and use of personal and public data. Under this framework, personal data and public data face many problems and challenges, such as:
· Privacy leakage: Internet companies or platforms may collect, analyze or sell users’ personal data without users’ consent or knowledge, or cause users’ personal data to be leaked or misappropriated in the event of hacker attacks or system failures³. In this way, the privacy rights of users will be violated or damaged, and even more serious consequences may be caused, such as identity theft, fraud, extortion, etc.
Security risks: Internet companies or platforms may publish or share public data containing wrong or harmful information without sufficient verification or review, or cause public data to be distorted or invalid when subject to malicious tampering or forgery. In this way, the security of public data will be threatened or destroyed, and may even lead to more serious consequences, such as misleading public opinion, influencing decision-making, and endangering interests.
Deprivation of rights and interests: Internet companies or platforms may possess or utilize users' personal data without reasonable distribution or compensation, or control or manipulate social public data without sufficient authorization or supervision. In this way, the rights and interests of users and society will be deprived or lost, and may even lead to more serious consequences, such as unequal competition, unreasonable benefits, and undemocratic governance.
Poor quality: Internet companies or platforms may use or provide noisy or biased personal data or public data without adequate cleaning or labelling, or develop or deploy flawed data without adequate testing or evaluation or vulnerable AI applications. In this way, the quality of personal data and public data will be affected or reduced, and may even lead to more serious consequences, such as misleading users, affecting performance, and endangering security.
· Inefficiency: Internet companies or platforms may isolate or monopolize users’ personal data or society’s public data without adequate coordination or cooperation, or operate or maintain outdated or low-quality data without adequate optimization or updating effective AI applications. In this way, the use of personal data and public data will be limited or wasted, and may even lead to more serious consequences, such as hindering innovation, affecting competition, and reducing efficiency.
The above are some of the main problems and challenges faced by personal data and public data under the traditional Web2.0 architecture. They not only damage the interests of individuals and society, but also limit the development and innovation of AI applications. To solve these problems and challenges, we need a new network architecture, the Web3 system.
3. The positive impact of AI application of Web3 system architecture on solving personal data and public data problems
The Web3 system is a decentralized, secure, transparent, and credible network architecture based on blockchain technology. It takes users or communities as the core, and empowers users and communities to collect, store, transmit, and collect personal and public data. Sovereignty and autonomy of exchange and use. Under this architecture, personal data and public data can be better resolved and optimized, such as:
· Privacy protection: The Web3 system allows users to protect their personal data through encryption, hashing and other technologies. Only users with private keys or authorization codes can access or modify their personal data. At the same time, the Web3 system allows users to control their personal data through technologies such as smart contracts. Only requests that meet the conditions or rules set by the user can obtain or use the user's personal data. In this way, the user's privacy rights can be guaranteed and respected, and will not be violated or damaged.
· Security guarantee: The Web3 system allows the community to protect the public data of the society through consensus mechanisms, incentive mechanisms and other technologies. Only information that has been verified or rewarded by the majority of nodes can become public data. At the same time, the Web3 system allows the community to verify social public data through technologies such as distributed ledgers and immutability. Only information that is recorded on the blockchain and cannot be modified can be used as public data. In this way, the security of public data can be threatened and compromised without being misled or affected.
Protection of rights and interests: The Web3 system allows users to use their personal data through tokens, NFT and other technologies. Users can obtain corresponding benefits or services by selling, leasing or mortgaging their personal data. At the same time, the Web3 system allows users to participate in social public data through technologies such as governance and voting. Users can obtain corresponding rights or responsibilities through proposals, voting or supervision of social public data. In this way, the rights and interests of users and society can be guaranteed and distributed without deprivation or loss.
· Quality improvement: The Web3 system allows users to improve their personal data through labeling, evaluation and other technologies. Users can improve their accuracy and usability by cleaning, labeling or evaluating their personal data. At the same time, the Web3 system allows the community to improve the public data of the society through collaboration, competition and other technologies, and the community can improve its diversity and innovation through collaboration, competition or rewards for the public data of the society. In this way, the quality of personal and public data can be improved and optimized without being compromised or degraded.
· Efficiency improvement: The Web3 system allows users to use social public data through interconnection, intercommunication and other technologies. Users can obtain more information or knowledge through interconnection, intercommunication or exchange of social public data. At the same time, the Web3 system allows the community to utilize users' personal data through open, shared and other technologies. The community can provide more services or products by opening, sharing or using users' personal data. In this way, the utilization of personal and public data can be enhanced and optimized without being restricted or wasted.
The above are some of the main impacts of Web3 system architecture AI applications on solving personal data and public data problems. They not only promote the interests of individuals and society, but also promote the development and innovation of AI applications. To illustrate these impacts, we will illustrate specific cases of AI applications of Web3 system architecture.
4. Specific cases of AI application in Web3 system architecture
Data feedback loops, data-driven innovation, data shaping public opinion, data improving welfare are some concepts about the role and impact of data in different domains and levels:
· The data feedback cycle refers to the cyclical process formed between data production and consumption, that is, data producers provide better products or services by collecting and analyzing data, while data consumers use products or services to produce better products or services. much data. For example, search engines provide more accurate and relevant search results by collecting and analyzing users' search histories and click behaviors, and users generate more search histories and click behaviors by using search engines.
· Data-driven innovation refers to the process of using data to create new products, services, business models or social value, that is, data as the raw material, tool or goal of innovation. For example, Rolls-Royce has changed its business model by installing sensors on aircraft engines to collect and analyze data to provide more optimized repair and maintenance services.
· Data shaping public opinion refers to the process of using data to influence or manipulate public cognition, attitude or behavior, that is, data as the source, carrier or target of public opinion. For example, news media use data to report or comment on certain events or topics in order to influence or manipulate public opinion or positions.
· Data to improve welfare refers to the process of using data to improve the health, education, safety or happiness of individuals or society, that is, data as the basis, means or result of welfare. For example, in a small town called Bolzano in northern Italy, the government installed monitors in the homes of retirees to provide more timely and effective social services and medical support by collecting and analyzing data.
The value of data transmission refers to the use of data to realize the connection between demand and supply. Data is consumption and data is production. Tokenized data is transmitted through the chain to realize the value transmission of the AI industry and traditional industries. For example, smart contracts can enable data providers, AI service providers, and traditional service providers to create new value and new services in a collaborative environment.
The following are some specific cases of AI applications based on the Web3 system architecture, which respectively demonstrate how the Web3 system can have a positive impact on solving the data feedback loop of personal data and public data, data-driven innovation, data shaping public opinion, and data improving welfare:
· Data feedback loop: NuCypher is a blockchain-based decentralized encryption service platform that allows users to exchange data securely and reliably with third parties without revealing their private keys or plaintext data. For example, a medical institution can use NuCypher to encrypt and share its patients' medical records to an AI research institution, and the latter can use NuCypher to decrypt and analyze these medical records, and return the analysis results to the former. In this way, both parties can realize a safe and efficient data feedback loop, improve the quality and value of data, and promote the optimization and innovation of medical AI applications.
· Data-driven innovation: Ocean Protocol is a blockchain-based decentralized data market platform that enables data exchange and collaboration between data providers and demanders without going through middlemen or platforms. For example, a climate change research institution can use the Ocean Protocol to publish and sell the weather data it collects to an AI developer, who can use the Ocean Protocol to buy and use the weather data to develop a climate prediction model and share the model results Return to the former. In this way, both parties can achieve a fair and efficient data-driven innovation, improve the mobility and utilization of data, and promote the development and innovation of climate AI applications.
· Data shapes public opinion: Gnosis is a blockchain-based decentralized prediction market platform that allows users to use their knowledge and insights to predict the probability of future events, and incentivize correct predictions through tokens. For example, a political activist can use Gnosis to create and participate in a prediction market about the outcome of a country's general election, while other users can use Gnosis to bet or observe this prediction market based on their own information or beliefs. In this way, users can realize a transparent and efficient data shaping public opinion, improve the credibility and influence of data, and shape public opinion and decision-making of political AI applications.
· Data improves welfare: GoodDollar is a blockchain-based decentralized inclusive financial platform, which allows users to obtain a digital currency called GoodDollar by contributing their personal data or public data, thereby realizing an unconditional basic income. For example, residents of a poor area can use GoodDollar to upload and share personal or public data such as their living conditions, health status, education status, etc. to an AI charity, and the latter can use GoodDollar to download and analyze these personal or public data To provide the corresponding assistance or service, and return GoodDollar to the former as a reward or compensation. In this way, users can realize a safe and efficient data improvement welfare, improve the value and significance of data, and improve the welfare and public welfare of social AI applications.
Data transmission value: FlerkenS is a blockchain-based decentralized AI application platform, which allows users to build a point-to-point data value transmission platform by building, deploying and running their own AI applications, linking personal data and Production and consumption network of public data. For example, a music lover can use FlerkenS to create and run a music recommendation AI application, while other users can use FlerkenS to access and use this music recommendation AI application. In this way, the two parties can realize a point-to-point data value transmission, improve the liquidity and utilization of data, and promote the development and innovation of music AI applications.
The above are some specific cases of AI applications based on the Web3 system architecture. They respectively show how the Web3 system can have a positive impact on solving the data feedback loop of personal data and public data, data-driven innovation, data shaping public opinion, and data improving welfare.
In conclusion
This paper analyzes the relationship between personal data and public data and their roles in the AI industry, analyzes the problems and challenges faced by personal data and public data under the traditional Web2.0 architecture, and demonstrates the use of Web3 system architecture AI applications to solve personal data and Positive impact of public data issues, and exemplifies specific cases of AI applications for Web3 system architecture. This paper believes that using the Web3 system architecture AI application is the best design to solve the problems of personal data and public data. It can make the collection process of personal data more autonomous, control and protection, and make the storage, transmission, exchange and use of public data more efficient. , convenience and fairness, thereby promoting the interests of individuals and society, and promoting the development and innovation of AI applications.
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