The Integration of AI and Web3: Building the Next Generation of Internet Infrastructure

Web3, as a new internet paradigm that is decentralized, open, and transparent, has a natural opportunity for integration with AI. Under the traditional centralized architecture, AI faces challenges such as computing power bottlenecks, privacy leaks, and algorithmic black boxes. Web3, based on distributed technology, can provide new impetus for AI development through shared computing power networks, open data markets, and privacy computing. At the same time, AI can also empower Web3 in many ways, such as optimizing smart contracts and anti-cheating algorithms, assisting in ecological construction. Exploring the combination of the two is crucial for building the infrastructure of the next generation of the internet and releasing the value of data and computing power.

Data-Driven: The Solid Foundation of AI and Web3

Data is the core driving force behind the development of AI. AI models need to digest a large amount of high-quality data to gain deep understanding and strong reasoning abilities. Data not only provides the training foundation for machine learning models but also determines the accuracy and reliability of the models.

The traditional centralized AI data acquisition and utilization model has the following issues:

• The cost of data acquisition is high, making it difficult for small and medium-sized enterprises to bear.
• Data resources are monopolized by tech giants, creating data islands.
• Personal data privacy is at risk of leakage and abuse.

Web3 can solve the pain points of traditional models with a new decentralized data paradigm:

• Users can sell idle networks to AI companies, decentralizing the collection of network data to provide real and high-quality data for AI model training.
• Adopt the "label to earn" model, incentivizing global workers to participate in data annotation through tokens, gathering global expertise, and enhancing data analysis capabilities.
• The blockchain data trading platform provides a transparent trading environment for both data supply and demand sides, incentivizing data innovation and sharing.

However, there are also problems with acquiring real-world data, such as inconsistent quality, high processing difficulty, and insufficient diversity and representativeness. Synthetic data may be the star of the Web3 data track in the future. Based on generative AI technology and simulations, synthetic data can mimic the attributes of real data, serving as an effective supplement to improve data utilization efficiency. In fields such as autonomous driving, financial market trading, and game development, synthetic data has shown mature application potential.

Privacy Protection: The Role of FHE in Web3

In the data-driven era, privacy protection has become a global focus, with regulations like the EU GDPR reflecting a strict safeguarding of personal privacy. However, this also brings challenges: some sensitive data cannot be fully utilized due to privacy risks, limiting the potential and reasoning capabilities of AI models.

FHE (Fully Homomorphic Encryption) allows computation operations to be performed directly on encrypted data without the need for decryption, and the computation results are consistent with those obtained from plaintext data. FHE provides solid protection for AI privacy computing, enabling GPU computing power to execute model training and inference tasks without accessing the original data environment, bringing significant advantages to AI companies.

FHEML supports encrypted processing of data and models throughout the entire machine learning lifecycle, ensuring the security of sensitive information and preventing the risk of data leakage. FHEML strengthens data privacy and provides a secure computing framework for AI applications. FHEML complements ZKML, which proves the correct execution of machine learning, while FHEML emphasizes computing on encrypted data to maintain data privacy.

Power Revolution: AI Computing in Decentralized Networks

The computational complexity of current AI systems doubles every three months, leading to a surge in demand for computing power that far exceeds the supply of existing computing resources. This not only limits the advancement of AI technology but also makes advanced AI models unattainable for most researchers and developers. The global GPU utilization rate is below 40%, coupled with a slowdown in microprocessor performance improvements and chip shortages caused by supply chain and geopolitical factors, further exacerbating the issue of computing power supply.

A decentralized AI computing power network aggregates idle GPU resources globally to provide an economically accessible computing power market for AI companies. Demand-side computing power can post computational tasks on the network, and smart contracts will assign tasks to miner nodes that contribute computing power. Miners execute tasks and submit results, receiving rewards after verification. This solution improves resource utilization efficiency and helps address the computing power bottleneck issues in fields such as AI.

In addition to the general decentralized computing network, there are also dedicated computing networks focused on AI training and inference. Decentralized computing networks provide a fair and transparent computing market, breaking monopolies, lowering application barriers, and improving computing efficiency. In the Web3 ecosystem, decentralized computing networks will play a key role in attracting more innovative dapps to join, collectively promoting the development and application of AI technology.

DePIN: Web3 Empowers Edge AI

Edge AI allows computation to occur at the source of data generation, achieving low latency and real-time processing while protecting user privacy. Edge AI technology has been applied in critical fields such as autonomous driving. In the Web3 space, we refer to this as DePIN. Web3 emphasizes decentralization and user data sovereignty, and DePIN enhances user privacy protection by processing data locally, reducing the risk of data leakage; the Web3 native token economic mechanism can incentivize DePIN nodes to provide computing resources, building a sustainable ecosystem.

Currently, DePIN is developing rapidly within a certain public blockchain ecosystem, becoming one of the preferred platforms for project deployment. The high TPS, low transaction fees, and technological innovations of this public blockchain provide strong support for DePIN projects. Currently, the market value of DePIN projects on this public blockchain exceeds 10 billion USD, and several well-known projects have made significant progress.

IMO: New Paradigm for AI Model Release

The concept of IMO was first proposed by a certain protocol, which aims to tokenize AI models. In the traditional model, AI model developers find it difficult to generate continuous revenue from subsequent usage, and the transparency of model performance and effectiveness is insufficient, limiting market recognition and commercial potential.

IMO provides new funding support and value-sharing methods for open-source AI models, allowing investors to purchase IMO tokens and share in the model's subsequent profits. A certain protocol uses a specific ERC standard, combining AI oracles and OPML technology to ensure the authenticity of the AI model and that token holders can share in the profits.

The IMO model enhances transparency and trust, encourages open-source collaboration, adapts to trends in the cryptocurrency market, and injects momentum for the sustainable development of AI technology. IMO is currently in the initial trial stage, but as market acceptance increases and participation expands, its innovation and potential value are worth looking forward to.

AI Agent: A New Era of Interactive Experience

AI agents can perceive their environment, think independently, and take actions to achieve their goals. Supported by large language models, AI agents not only understand natural language but can also plan decisions and execute complex tasks. They can serve as virtual assistants, learning user preferences through interaction to provide personalized solutions. Even in the absence of explicit instructions, AI agents can autonomously solve problems, improve efficiency, and create new value.

A certain AI native application platform provides a comprehensive and easy-to-use set of creation tools, supporting users to configure robot functions, appearance, voice, and connect to external knowledge bases, aiming to build a fair and open AI content ecosystem. The platform trains specialized large language models, making role-playing more humanized; voice cloning technology accelerates personalized interaction of AI products, reducing the cost of voice synthesis by 99%, with voice cloning achievable in just 1 minute. By utilizing this platform to customize AI Agents, it can currently be applied in various fields such as video chatting, language learning, and image generation.

The current integration of Web3 and AI is primarily focused on exploring the infrastructure layer, such as acquiring high-quality data, protecting data privacy, on-chain model hosting, improving the efficient use of decentralized computing power, and validating large language models. As these infrastructures gradually improve, the integration of Web3 and AI will give rise to a series of innovative business models and services.