The Integration of AI and Web3: Opportunities and Challenges Coexist

In recent years, the rapid development of artificial intelligence ( AI ) and Web3 technology has attracted widespread attention globally. AI has made significant breakthroughs in fields such as facial recognition, natural language processing, and machine learning, bringing tremendous changes to various industries. Web3, as an emerging internet model, is changing people's perceptions and usage of the internet. The combination of AI and Web3 has become the focus of attention for developers and investors in both the East and West, and how to effectively integrate the two is a question worth exploring in depth.

This article will focus on the current development status of AI+Web3, analyze the situation of current projects, and discuss the limitations and challenges faced in depth. It is hoped that it will provide valuable references for relevant practitioners and investors.

Ways AI Interacts with Web3

The development of AI and Web3 is like the two sides of a balance scale; AI has brought improvements in productivity, while Web3 has brought about changes in production relations. So what kind of sparks can AI and Web3 create when they collide? Let's first analyze the dilemmas and areas for improvement faced by the AI and Web3 industries, and then discuss how they can help each other solve these dilemmas.

The challenges faced by the AI industry

The core of the AI industry relies on three elements: computing power, algorithms, and data.

1. In terms of computing power: AI tasks require a large amount of computing resources for model training and inference, especially deep learning models. Acquiring and managing large-scale computing power is an expensive and complex challenge, with costs, energy consumption, and maintenance of high-performance computing devices being issues. For startups and individual developers, obtaining sufficient computing power can be difficult.

2. Algorithm aspect: Although deep learning algorithms have achieved great success, there are still some dilemmas. Training deep neural networks requires a large amount of data and computational resources, and there is insufficient interpretability of the model for certain tasks. The robustness and generalization ability of the algorithm are also important issues, as the model's performance on unseen data may be unstable.

3. Data aspect: Obtaining high-quality and diverse data remains a challenge. Data in certain areas is difficult to obtain, such as healthcare data. There are also issues with data quality, accuracy, and labeling; incomplete or biased data may lead to erroneous model behavior. At the same time, protecting data privacy and security is also an important consideration.

In addition, the black box characteristic of AI models raises issues of interpretability and transparency. For certain applications such as finance and healthcare, the decision-making process of the model needs to be interpretable and traceable, while existing deep learning models often lack transparency.

The challenges faced by the Web3 industry

The Web3 industry also has many issues that need to be addressed, including:

1. Insufficient data analysis capabilities: Web3 platforms need better data analysis capabilities to understand user behavior, predict market trends, etc.

2. Poor user experience: Many Web3 products have poor user interfaces and interaction experiences, which affects user adoption.

3. Smart Contract Security Issues: Vulnerabilities in smart contract code and hacker attacks remain a significant challenge.

4. Privacy Protection: How to achieve data sharing and value creation while protecting user privacy.

5. Scalability: The throughput and transaction speed of blockchain networks still need to be improved.

AI, as a tool to enhance productivity, has great potential in these areas.

Analysis of the Current Status of AI+Web3 Projects

Projects that combine AI and Web3 mainly focus on two major directions: leveraging blockchain technology to enhance the performance of AI projects, and using AI technology to serve the improvement of Web3 projects.

Web3 empowers AI

Decentralized Computing Power

With the rapid development of AI, the demand for GPUs has surged, leading to a supply shortage. To address this issue, some Web3 projects have begun to offer decentralized computing power services, such as Akash, Render, and Gensyn. These projects incentivize a wide range of users to provide idle GPU computing power through tokens, becoming the supply side of computing power and providing support for AI clients.

The supply side mainly includes three categories: cloud service providers, cryptocurrency miners, and enterprises with a large number of GPUs. The projects are roughly divided into two types, one type is used for AI inference ( such as Render, Akash ), and the other type is used for AI training ( such as io.net, Gensyn ).

The emergence of decentralized computing networks has provided new possibilities for AI computing supply. However, compared to centralized computing services, decentralized computing still faces challenges in terms of performance stability, availability, and complexity of use. Currently, most projects are still limited to AI inference rather than training, mainly due to the different requirements for computing power and bandwidth.

Decentralized Algorithm Model

Some projects are trying to establish decentralized AI algorithm service markets, such as Bittensor. These platforms connect multiple AI models, each with its own area of expertise. When users ask questions, the platform selects the most suitable model to provide an answer.

Compared to a single large model, decentralized algorithm model platforms have the potential to offer more diverse services. However, ensuring model quality and coordinating cooperation between different models remains a challenge.

Decentralized Data Collection

Data is the key to AI development. Some Web3 projects like PublicAI are achieving decentralized data collection through token incentives. Users can contribute data or participate in data validation to earn token rewards. This approach helps to obtain more diverse data while allowing users to share in the value of the data.

ZK protects user privacy in AI

Zero-Knowledge Proof ( ZK ) technology provides new possibilities for privacy protection in AI. ZKML ( Zero-Knowledge Machine Learning ) allows for the training and inference of machine learning models without disclosing the original data. This helps to address the conflict between privacy protection and data sharing, particularly suitable for sensitive data fields such as healthcare and finance.

AI empowers Web3

Data Analysis and Prediction

Many Web3 projects are beginning to integrate AI services to provide data analysis and forecasting. For example, Pond uses AI algorithms to predict valuable tokens; BullBear AI predicts price trends based on historical data; Numerai holds competitions for AI predictions in the stock market; Arkham uses AI for on-chain data analysis, etc.

Personalized Services

The application of AI in search and recommendation has also extended to the Web3 field. For example, Dune has launched the Wand tool, which uses large language models to write SQL queries; the Web3 media platforms Followin and IQ.wiki have integrated ChatGPT for content summarization; Kaito aims to become a Web3 search engine based on LLM.

AI Audit Smart Contract

AI shows great potential in smart contract auditing. For example, 0x0.ai provides an AI smart contract auditor that uses machine learning techniques to identify potential issues in the code. This helps improve the security and reliability of smart contracts.

Limitations and Challenges of AI+Web3 Projects

The Real Barriers to Decentralized Computing Power

1. Performance and Stability: Decentralized computing power relies on globally distributed nodes, which may experience delays and instability.

2. Availability: The degree of supply and demand matching may lead to situations where resources are insufficient or cannot meet demand.

3. Complexity of use: Users may need to understand more technical details, which increases the cost of use.

4. Training Difficulty: Currently, decentralized computing power is mainly used for AI inference, making it difficult to meet the high demands for computing power and bandwidth required for training large models.

The combination of AI and Web3 is not deep enough.

Many projects only superficially use AI without achieving real deep integration:

1. Limited application scenarios: Most applications such as data analysis, recommendation searches, etc., are essentially no different from Web2 projects.

2. Marketing is greater than substance: Some projects leverage AI concepts more at the marketing level, with limited actual innovation.

token economics issues

Some projects may overly rely on token economics while neglecting to address actual needs. Designing a reasonable token model to ensure long-term sustainable development remains a significant challenge.

Summary

The integration of AI and Web3 provides new possibilities for technological innovation and economic development. AI can offer smarter application scenarios for Web3, such as data analysis and smart contract auditing. Web3 provides a decentralized platform for computing power, data, and algorithm sharing for AI.

Despite facing many challenges at present, the combination of AI and Web3 has immense potential. In the future, with technological advancements and more innovative practices, we can expect to see a deeper integration, building a smarter, more open, and fair economic and social system.