The Integration of AI and Web3: Building a New Paradigm for Decentralization Intelligent Networks

Web3, as a new generation of internet model characterized by Decentralization, openness, and transparency, has a natural synergy with AI technology. In traditional centralized architectures, AI faces challenges such as computational power bottlenecks, privacy leaks, and algorithm opacity. In contrast, Web3, based on distributed technology, can provide new momentum for AI development through shared computational power networks, open data markets, and privacy computing. At the same time, AI can also empower the Web3 ecosystem, such as optimizing smart contracts and developing anti-cheating algorithms. Exploring the combination of the two is of great significance for building the next generation of internet infrastructure and unlocking the value of data and computational power.

Data-Driven: The Cornerstone of AI and Web3

Data is the core element driving the development of AI. AI models require massive amounts of high-quality data to gain deep understanding and strong reasoning capabilities, and the quality of the data directly affects the accuracy and reliability of the models.

The traditional centralized AI data model has the following problems:

• 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 silos.
• Personal privacy is at risk of leakage and abuse.

Web3 provides a new Decentralization data paradigm to address these pain points:

• Decentralization data collection platform allows users to sell idle network resources, providing real high-quality data for AI.
• The "Labeling for Profit" model incentivizes global workers to participate in data labeling, gathering expertise.
• The blockchain data trading platform provides a transparent trading environment for both data supply and demand sides.

Nevertheless, obtaining real-world data still faces challenges such as varying quality and difficulties in processing. Synthetic data may become the new star in the future data arena. Based on generative AI and simulation technologies, synthetic data can effectively complement real data and improve efficiency. In fields such as autonomous driving, financial trading, and game development, synthetic data has already shown promising application prospects.

Privacy Protection: Application of FHE Technology

Privacy protection has become a global focus, with increasingly strict regulations. This also poses challenges for AI development: some sensitive data cannot be fully utilized due to privacy risks, which limits the potential of the models.

Fully Homomorphic Encryption ( FHE ) allows computations to be performed directly on encrypted data without needing to decrypt it, yielding the same results as plaintext computations. FHE provides strong guarantees for AI privacy computing, enabling GPUs to perform model training and inference without accessing the raw data. This allows AI companies to securely open API services while protecting trade secrets.

FHEML supports the encryption of data and models throughout the entire machine learning lifecycle, ensuring the security of sensitive information and preventing the risk of data leaks. It provides a secure computing framework for AI applications and is a strong complement to ZKML.

Computing Power Revolution: Decentralization AI Computing Network

The computational complexity of current AI systems doubles every quarter, leading to a surge in computing power demand that far exceeds the existing resource supply. For example, training the GPT-3 model requires enormous computing power, equivalent to 355 years of training time on a single device. The shortage of computing power not only limits AI progress but also makes advanced models difficult for most developers to attain.

At the same time, global GPU utilization is below 40%, coupled with a slowdown in chip performance improvement and supply chain issues, resulting in tighter computing power supply. AI practitioners face a dilemma of either purchasing hardware or renting cloud resources, urgently requiring on-demand and cost-effective computing services.

The Decentralization AI computing power network aggregates idle GPU resources globally, providing an economically accessible computing power market for AI companies. Demand-side users can publish tasks, and smart contracts are assigned to miner nodes for execution. Miners receive rewards upon completion. This model improves resource utilization efficiency and helps alleviate the computing power bottleneck in fields such as AI.

In addition to the general computing power network, there are dedicated platforms focused on AI training and inference. The decentralized computing power network provides a fair and transparent market, breaking monopolies, lowering barriers, and improving efficiency. In the Web3 ecosystem, it will attract more innovative applications to join and promote the development of AI technology.

DePIN: Web3 Empowers Edge AI

Edge AI enables smart devices to possess local AI computing capabilities, achieving low-latency real-time processing while protecting user privacy. This technology has been applied in critical areas such as autonomous driving.

In Web3, DePIN ( decentralized physical infrastructure networks ) are aligned with the concept of edge AI. Web3 emphasizes Decentralization and user data sovereignty, while DePIN enhances privacy protection through local processing, reducing the risk of data breaches. The native token economy of Web3 incentivizes nodes to provide computing power, building a sustainable ecosystem.

Currently, DePIN is developing rapidly in a certain blockchain ecosystem, becoming one of the preferred deployment platforms for projects. The chain's high performance, low fees, and technological innovation provide strong support for DePIN. Currently, the market value of DePIN projects on it has exceeded 10 billion USD, with several well-known projects making significant progress.

IMO: New Model for AI Model Release

IMO ( Initial Model Offering ) is a concept pioneered by a certain protocol, which tokenizes AI models.

Under the traditional model, AI model developers find it difficult to continue benefiting from subsequent use, especially after the models are integrated into other products. Moreover, the performance of the models lacks transparency, making it hard for investors and users to assess their value, which limits market recognition.

IMO provides a new type of financing and value-sharing method for open-source AI models. Investors can purchase tokens to share in the model's profits. A certain protocol adopts specific technical standards, combining AI oracles and OPML technology to ensure the authenticity of the model and the sharing of profits.

IMO enhances transparency and trust, encourages open-source collaboration, adapts to trends in the crypto market, and injects momentum for the sustainable development of AI. It is still in the early stages, but its innovation and potential value are worth looking forward to.

AI Agent: Opening a New Era of Interaction

AI Agents can perceive their environment, think independently, and take action to achieve goals. With the help of large language models, they not only understand natural language but also plan decisions and execute complex tasks. As virtual assistants, AI Agents learn user preferences through interaction and provide personalized solutions. They can autonomously solve problems without explicit instructions, improving efficiency and creating value.

A certain open AI native application platform provides comprehensive and easy-to-use creation tools, allowing users to configure robot functions, appearance, voice, and connect to external knowledge bases, dedicated to building a fair and open AI content ecosystem. The platform has trained specialized large language models to make role-playing more human-like; its voice cloning technology significantly reduces costs and can be achieved in just 1 minute. Users can customize AI Agents on the platform for various fields such as video chatting, language learning, and image generation.

Currently, the integration of Web3 and AI is primarily focused on the infrastructure level, exploring key issues such as data acquisition, privacy protection, on-chain model hosting, efficient utilization of decentralized computing power, and large language model verification. As these infrastructures gradually improve, the combination of Web3 and AI is expected to give rise to a series of innovative business models and services.