Intensification of Data Competition and Trends Towards Data Democratization

Data is the foundation and driving force for training and improving AI models. Without sufficient and high-quality data, AI models cannot enhance their performance or adapt to different scenarios. At the same time, data is a scarce and valuable resource. Companies with access to a large amount of novel data can gain competitive advantages and bargaining power. Consequently, various parties are actively seeking and developing new data sources while protecting their own data from infringement.

However, the current data ecosystem faces some problems and challenges, such as:

• Data Monopoly: Large internet companies have formed significant data monopolies by collecting, storing, analyzing, and utilizing users’ personal data, which excludes other competitors and innovators.
• Data Privacy: Users’ personal data is obtained, misused, leaked, or sold by large internet companies without consent, violating users’ privacy rights and autonomy.
• Data Quality: Due to reasons such as opaque data sources, inconsistent data standards, and improper data processing, data quality issues arise, such as incompleteness, inconsistency, noise, or bias.
• Data Exhaustion: As AI models become increasingly complex and massive, more and higher-quality data are needed for training and improvement. However, existing data sources may not meet this demand, posing a risk of data exhaustion.

To address these problems and challenges, the industry suggests several possible solutions:

• Data Synthesis: Using techniques such as Generative Adversarial Networks (GANs), generate virtual but realistic data to expand existing datasets.
• Data Federations: Utilize encryption, distributed, and collaborative technologies to achieve cross-institutional, cross-regional, and cross-domain data sharing and collaboration while protecting data privacy and security.
• Data Marketplaces: Utilize technologies such as blockchain, smart contracts, and tokens to enable decentralized, transparent, and fair data transactions and circulation.

Among them, the model of building a data flywheel through the Web3 distributed architecture has caught our attention. Web3 refers to the next-generation internet built on blockchain technology and decentralized networks. Web3 enables users to have complete control and ownership of their data while incentivizing data sharing and exchange through tokens. This way, AI model builders can obtain users’ authorized data through the Web3 platform, and users can receive corresponding rewards. This model promotes data circulation and innovation while protecting data privacy and security.

How to Build a Decentralized Data Flywheel for Large Models

To leverage the distributed architecture of Web3 to create a decentralized big model data flywheel, we need to consider the following aspects:

Establish Data Strategy and Objectives

Before starting to collect and use data, a clear vision is needed, clarifying what is to be achieved through data and how it aligns with business goals. It’s also necessary to identify key stakeholders, metrics, and outcomes that guide the data project. For example, in an AI e-commerce platform built on Web3 infrastructure, it’s essential to establish data based on user needs, using consumer-side data to create a demand vector database. When the production side interfaces with the consumer database, payment of the corresponding Token should be done according to smart contracts.

Collect and Store Data from Multiple Sources

To create a comprehensive and diverse dataset, data should be collected and stored from various sources, such as web scraping, user interactions, sensors, etc. A reliable and scalable cloud platform, like Amazon Web Services, should be used for secure and efficient data storage and management. Data collection should be done through various vertical vector databases through contractual acquisitions.

Transform and Enrich Data

To make data suitable for machine learning purposes, it should undergo preprocessing, cleaning, labeling, enhancement, and organization. Data labeling and engineering tools, like Labelbox or AtScale, should be used to automate and optimize these processes.

Build and Train Large Models

Utilize data to build and train large-scale machine learning models that can provide accurate and reliable outputs. Base models such as ChatGPT or PaLM can be used as starting points for building custom models, or frameworks like PyTorch or TensorFlow can be employed to implement and train models.

Deploy and Manage Large Models in Production

To deliver model outputs to users and customers, models need to be deployed and managed in production environments. Platforms and tools like MLCommons or TensorBoard should be used to ensure the model’s performance, security, and scalability.

Integrate Large Models into Products and Services

To provide value to users and customers, large models should be integrated into products and services that solve their problems or meet their needs. APIs and libraries like OpenAI Playground or Hugging Face Transformers can be used to access and utilize large models for various tasks.

Collect and Analyze Feedback on Large Model Outputs from Users and Customers

To improve large models based on feedback from users and customers, their ratings, comments, opinions, clicks, purchases, etc., should be collected and analyzed. Analytical and survey tools like Google Analytics or Google Forms can be used to track and measure their behavior and opinions.

Key Stages of the Data Flywheel

Building upon the mentioned aspects, let’s explore in more detail how to utilize the data flywheel in large model applications built on Web3’s unified infrastructure for personal and public data value. This type of data flywheel needs to consider the following important stages:

Data Acquisition: Data is obtained point-to-point through AI application portals and users are incentivized with Tokens. This means that users can earn a return by sharing their data, as opposed to being exploited and controlled by large companies like in Web 2.0. Possible data acquisition methods include web scraping, user interactions, sensors, etc. These data can be verified, authorized, and rewarded through smart contracts on the Web3 platform, thus protecting users’ data rights and privacy.

Data Transformation: Data is vectorially labeled and a data quantification system is established. Tokens are paid for point-to-point links of distributed unit data, and the data is priced through smart contracts during labeling. This means that data can be preprocessed, cleaned, labeled, enhanced, and organized to suit machine learning purposes. These processes can be standardized, coordinated, and incentivized through smart contracts on the Web3 platform, thereby improving data quality and efficiency.

Model Development: Train vertical large models with vector database data in segmented domains. This implies the use of data to build and train large-scale machine learning models that provide accurate and reliable outputs. These models can be designed, optimized, and evaluated through smart contracts on the Web3 platform, enhancing their performance and adaptability.

Model and Data Consumption: Both are priced via smart contracts, and any API user must pay through smart contracts for using the model and data. This means that models and data can be integrated into products and services, providing value to users and customers, such as natural language understanding, computer vision, recommendation systems, etc. These products and services can be traded, distributed, and rewarded through smart contracts on the Web3 platform, thus enabling data circulation and innovation.

Model and Data Feedback: How to collect and analyze user and customer feedback on model outputs and data. This means improving models and data based on user and customer ratings, comments, opinions, clicks, purchases, etc. This feedback can be collected, analyzed, and rewarded through smart contracts on the Web3 platform, thereby achieving continuous optimization of models and data.

Objectives of Decentralized Data Flywheel

The goal of the decentralized big model data flywheel is not only to train large models but also to achieve business intelligence. Real-time updated data is used not just for the training of large models to leverage its public value but also to realize the personal value of users through point-to-point data transmission systems. It aims to bridge the gap between consumer data and production data, establishing an industrial chain system that connects the supply side with the demand side, forming a truly decentralized business society, and realizing data democratization, autonomy, and value creation.

To achieve this goal, we can implement it in the following ways:

The data flywheel can improve the training efficiency and effectiveness of large models. By using the Web3 distributed architecture, users can have complete control and ownership of their data, while also sharing and exchanging data through a Token incentive mechanism. Thus, AI model builders can acquire authorized data from users via the Web3 platform, and users can receive corresponding rewards. This model can promote data circulation and innovation while also protecting data privacy and security. These data can be used to build and train large-scale machine learning models that provide accurate and reliable outputs, such as natural language understanding, computer vision, recommendation systems, etc.

The data flywheel can bridge consumer data with production data. By using smart contracts for pricing, any API user needs to pay through smart contracts for using the model and data. This means models and data can be integrated into products and services, providing value to users and customers. These products and services can be traded, distributed, and rewarded through smart contracts on the Web3 platform, thus enabling data circulation and innovation. In this way, consumer data can establish a consumer vector database, and when the production side interfaces with the consumer database, Token payment is required according to smart contracts. This method can establish an industrial chain system that connects the supply and demand sides, thus improving business efficiency and effectiveness.

The data flywheel can form a truly decentralized business society. By using a data flywheel of large model applications built on Web3’s unified personal and public data value infrastructure, collaboration and mutual winning among users, suppliers, and platforms can be achieved. The upcoming data protection laws are difficult to implement in the Web2.0 environment and cannot completely protect user data and anti-data monopoly from a technical perspective. In contrast, under the technical environment of the distributed big model data flywheel structure, users can earn a return by sharing their data, instead of being exploited and controlled by large companies like in Web 2.0. Developers can build and train high-performance large models using users’ authorized data and integrate them into products and services. Platforms can promote data and model innovation by providing secure, transparent, and fair trading and circulation mechanisms. This method can achieve data democratization, autonomy, and value creation.

Conclusion

Building a decentralized big model data flywheel through the Web3 distributed architecture is a promising solution that can address some of the existing problems and challenges in the current data ecosystem and promote data circulation and innovation. To achieve this goal, we need to consider multiple aspects, from establishing data strategies and objectives to collecting and analyzing user feedback, while avoiding some common pitfalls. We also need to consider how to use the data flywheel of large model applications built on Web3’s unified personal and public data value infrastructure, thereby achieving collaboration and mutual benefits among users, suppliers, and platforms. We hope this article can provide you with some useful information and insights.

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