The Integrated Development of AI and Web3

Artificial intelligence and Web3 technology have developed rapidly in recent years, attracting widespread attention. AI has made significant breakthroughs in areas such as facial recognition and natural language processing, bringing transformation to various industries. Web3, on the other hand, is based on blockchain, achieving decentralized data sharing and user autonomy. The combination of the two will bring great potential.

The Interaction Between AI and Web3

Challenges Facing the AI Industry

The core elements of AI include computing power, algorithms, and data. In terms of computing power, acquiring large-scale computing resources is costly. Regarding algorithms, deep learning models still face issues such as lack of interpretability. As for data, obtaining high-quality data and protecting privacy are challenges. Additionally, the interpretability of AI models and business models are also challenges.

Improvement space in the Web3 industry

Web3 has room for improvement in data analysis, user experience, and security. AI can help enhance data analysis capabilities, optimize user experience, and strengthen security. For example, many Web3 protocols integrate AI tools like ChatGPT to enhance their services.

Analysis of the Current Status of AI+Web3 Projects

Web3 empowers AI

Decentralized Computing Power

With the surge in AI demand, GPUs are in short supply. Some Web3 projects like Akash and Render offer decentralized computing power, incentivizing users to contribute idle computing resources through tokens. Currently, this is primarily used for AI inference and is difficult to support large-scale training.

Decentralized Algorithm Model

Projects like Bittensor are building a decentralized AI algorithm market, connecting different AI models to provide users with the most suitable services.

Decentralized Data Collection

Projects like PublicAI incentivize users to contribute data for AI training through tokens, promoting a win-win situation for data providers and AI developers.

ZK protects user privacy in AI

Projects like BasedAI utilize zero-knowledge proof technology to achieve data sharing and AI model training while protecting privacy.

AI empowers Web3

Data Analysis and Prediction

Many Web3 projects integrate AI services to provide investment strategies, on-chain analysis, etc. For example, Pond uses AI to predict valuable tokens.

Personalized Service

Platforms like Dune use AI to improve user experience, such as automatically generating SQL queries. Some content platforms also integrate AI for content summarization.

AI Audit Smart Contract

Projects like 0x0.ai use AI to identify potential vulnerabilities in smart contracts, enhancing security.

Challenges Faced by AI+Web3 Projects

1. Decentralized computing power still struggles to match centralized services in terms of performance, stability, and other aspects.

2. Most projects only superficially combine AI and have not achieved deep integration and innovation.

3. Some projects overly rely on the narrative of token economics, with questionable practicality.

Summary

The integration of AI and Web3, although facing challenges, holds great potential. AI can provide intelligent services for Web3, while Web3 offers new development space for AI. In the future, it is expected to build a more intelligent, open, and fair economic and social system.