AI in the Web3 Era: Exploring the Infinite Potential of Blockchain and Artificial Intelligence

AI in the Web3 Era: Exploring the Potential of Blockchain and AI

With the emergence of Chat-GPT, we have entered the era of disruptive innovation brought about by AIGC.

AIGC (AI Generated Content) is considered a new content production method after UGC and PGC. AI painting, AI writing, and other methods belong to the branches of AIGC. Chat-GPT is a natural language processing AI language model. What are the key elements of AI models as a form of AIGC during the training and inference processes?

Element One: Computing Power

High-quality and diverse data is the foundation of training AI models, and computing power provides the driving force for model training.

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In terms of computing power, for the AI model training stage, computing power is used to perform tasks such as backpropagation, parameter updates, and model optimization on large-scale datasets. Higher computing power can speed up the training process, allowing the model to converge faster and learn the features of the data more quickly. For the AI model inference stage, computing power is used to apply the trained model to new data instances for prediction and inference. In real-time applications, the level of computing power determines the amount of requests the model can process and the response speed.

Many complex AI algorithms require a large amount of computational resources. The development of traditional AI is limited by the performance and computing power of hardware devices. Especially when dealing with large-scale datasets or highly complex model training, more powerful computing capabilities are needed.

Currently, there is a lack of mature products and solutions for sharing intelligent computing power in the market. The traditional computing power market introduces third-party social idle computing power such as personal terminals, and computing power service operators do not have effective control over the nodes and cannot guarantee the security and trustworthiness of the computing power nodes, which greatly increases the breadth and difficulty of security protection.

Element Two: Data

Data-based privacy-protected data sharing is an important support for AIGC modeling.

In terms of data provision, AIGC’s model training requires the use of a large amount of data to achieve good performance and improve the model’s inference ability and accuracy. Taking ChatGPT as an example, GPT’s training used hundreds of billions of tokens of data. As a large AI language model, GPT’s training data includes a wide range of text sources on the Internet, including web pages, books, articles, papers, and other publicly available text resources. These data cover multiple domains and topics, enabling the model to have a wide range of knowledge and language understanding abilities.

In summary, training an AI model requires a massive amount of data, and the internal data of a single enterprise is often insufficient to meet the demand. Therefore, data sharing is necessary in this process. However, while the global amount of data is increasing rapidly, the serious privacy leaks caused by data sharing have seriously affected the full utilization of data value. IBM Security’s report in July 2022 shows that during the period from March 2021 to March 2022, 550 companies worldwide experienced data breaches, with an average loss of 4.4 million U.S. dollars per data breach. Compared to 2020, the loss increased by 13%. Therefore, how to ensure data privacy and security while conducting data circulation and value mining, and serve the growth of AIGC technology, has become an increasingly important topic in the industry.

What improvements can Web3 bring when combined with AI?

Web3, as a new generation of the Internet built on blockchain and decentralized technologies, has greater decentralization, openness, and transparency. When AI is combined with Web3, it can gain many advantages that are different from traditional AI.

Distributed computing resources:

The decentralized nature of Web3 allows computing resources from around the world to be integrated and shared. This provides larger-scale computing power for AI model training and inference. Traditional AI model training usually relies on a single computing device or cloud service provider, but combining with Web3 can use distributed computing resources in the global network, providing more efficient and elastic computing support.

Data sharing and privacy protection:

One of the core concepts of Web3 is decentralization and user ownership of data. Combined with AI, Web3 can provide users with more control and opportunities for data sharing, allowing them to participate in AI model training and data sharing in a more privacy-safe way.

Decentralized model development and deployment:

Web3’s smart contracts and distributed computing platforms can facilitate the development and deployment of AI models. Smart contracts can provide a decentralized way to manage and verify the model training process, while distributed computing platforms can use computing resources in the global network to accelerate model training and inference.

Enhance data quality and diversity:

Web3 can use incentive mechanisms and decentralized data markets to encourage users to provide more high-quality and diverse data, thereby improving the data limitations faced by traditional AI.

AIGC Platform WaterWheel in Web3.0

In the Computing Module:

Waterwheel’s computing network combines TEE technology and blockchain technology to build a trusted, open, and efficient computing sharing platform. It has the ability to take inventory of computing nodes across the network and blockchain nodes, and can manage idle computing power from around the world.

In the Data Module:

Waterwheel is a decentralized data sharing platform built on blockchain and privacy computing, establishing a global data asset network that supports data contributors to register their data and participate in data crowdfunding tasks. Through privacy computing technology, it solves the security problems of data leakage in the process of data flow, while ensuring the security and privacy of data, and brings value benefits to data contributors.

In the AIGC Creation Module:

Traditional AIGC also lacks privacy protection. Most of the unique ideas of users entered through prompt input are directly made public. The different AI model offerings and billing methods also make users pay higher costs. Since the creation process of AIGC is mainly completed by AI models, creators find it difficult to obtain reasonable returns through traditional copyright transactions.

In the Model Service Module:

Waterwheel integrates blockchain, privacy computing, and AI technology to create a secure and trustworthy model training platform. By using remote proof and privacy environment with privacy computing TEE technology, it solves the problem of mutual distrust and data leakage risks among model training parties, data providers, and computing providers, ensuring that the data and models are in a “usable but invisible” state throughout the entire model training process. It helps AI model training parties to obtain more data safely and compliantly, while hosting AI models in a privacy environment, ensuring the security and privacy of the models.

Looking forward to seeing more Web3.0 platforms promote the development and application of the AI industry!

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