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

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

AIGC (AI Generated Content) is considered to be a new content production method after UGC and PGC. AI painting, AI writing, etc. are all branches of AIGC. Chat-GPT is a large AI language model for natural language processing, AI model As a specific form of AIGC, what are the key elements in the training process and reasoning process?

Element 1: computing power

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

In terms of computing power provision, for the AI model training phase, computing power is used to perform tasks such as backpropagation, parameter updating, and model optimization on large-scale data sets. Higher computing power can speed up the training process, enabling the model to converge faster and learn the characteristics of the data. For the AI model inference phase, 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 and response speed that the model can handle.

Many complex AI algorithms require massive computing resources. The development of traditional AI is limited by the performance and computing power of hardware devices. Especially when processing large-scale data sets or conducting highly complex model training, more powerful computing power is required.

At present, there is still a lack of mature products and solutions for the sharing of intelligent computing power on 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 the ability to effectively control the nodes. The security and credibility of computing power nodes cannot be guaranteed, which greatly increases the breadth and difficulty of security protection.

Element 2: Data

Data sharing based on privacy protection is an important support for AIGC modeling.

In terms of data provision, AIGC's model training needs to use a large amount of data to obtain good performance and improve the reasoning ability and accuracy of the model. Taking ChatGPT as an example, the training of GPT uses data of tens of billions of tokens. As a large-scale AI language model, GPT's training data includes a wide range of text sources on the Internet, including web pages, books, articles, theses, and other publicly available text resources. These data cover multiple domains and topics, enabling models with broad knowledge and language understanding capabilities.

All in all, training a large AI model requires massive amounts of data, and the internal data of a single enterprise is often insufficient to meet the demand. Therefore, data sharing is required in this process. However, while the amount of global data is growing rapidly, privacy leakage caused by data sharing has seriously affected Make full use of the value of data. According to the report of IBM Security in July 2022, data breaches occurred in 550 companies around the world between March 2021 and March 2022, and the average loss caused by a data breach reached 4.4 million US dollars, an increase of 13% compared to 2020. Therefore, how to carry out data circulation and value mining under the premise of ensuring data privacy and security, and serve the growth of AIGC technology has become a topic of increasing concern in the industry.

What improvements can the combination of Web3 and AI bring?

As a new generation of Internet built on blockchain and decentralized technology, Web3 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 enables computing resources on a global scale to be integrated and shared. This provides greater computing power for AI model training and inference. Traditional AI model training usually relies on a single computing device or cloud service provider, but combined with Web3, distributed computing resources in the global network can be utilized to provide more efficient and elastic computing power support.

Data sharing and privacy protection:

One of the core concepts of Web3 is decentralization and the power of users over data. Combined with AI, Web3 can provide users with more control and data sharing opportunities, enabling them to participate in AI model training and data sharing in a more private and secure manner.

Decentralized model development and deployment:

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

Enhance data quality and diversity:

Web3 can encourage users to provide more high-quality and diverse data through an incentive mechanism and a decentralized data market, thereby improving the data limitation problem faced by traditional AI.

Take the AIGC platform WaterWheel of Web3.0 as an example

In the computing power module:

Waterwheel's computing power network combines TEE technology and blockchain technology to build a credible, open, and efficient computing power sharing platform. It has the ability to coordinate and inventory the entire network computing power nodes and blockchain nodes, and can manage idle resources around the world. computing power.

In the data module:

Waterwheel is a decentralized data sharing platform based on blockchain and privacy computing, builds a global data asset network, supports data contributors to register data and participate in data crowdfunding tasks, and solves the data circulation process through privacy computing technology In order to solve the security issues of data leakage in the medium, on the premise of ensuring data security and privacy, it will bring value benefits to data contributors.

Authoring modules in AIGC:

The traditional AIGC also lacks privacy protection. Most of the unique ideas of users will be directly disclosed through the prompt input. Different AI model provision and billing methods also make users pay higher costs. Since the creation process of AIGC is mainly composed of After the AI model is completed, it is difficult for creators to obtain reasonable income through traditional copyright transactions.

In the model serving module:

Waterwheel integrates blockchain, privacy computing, and AI technology to create a safe and credible model training platform. By using the remote certification and privacy environment of privacy computing TEE technology, it solves the gap between model training parties, data providers and computing power providers. Mutual distrust and data leakage risk issues, ensuring that data and models are in a state of "available and invisible" throughout the model training process, helping AI model trainers to obtain more data in a safe and compliant manner, while hosting AI models in In the privacy environment, the security and privacy of the model are guaranteed.

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