The Integration of Web3 and AI: A New Era of Data-Driven, Privacy Protection, and Computing Power Revolution

Web3, as an emerging paradigm of decentralized internet, presents a natural opportunity for integration with artificial intelligence technology. The development of AI under traditional centralized architecture faces numerous challenges, such as Computing Power bottlenecks, privacy breaches, and Algorithm black boxes. Web3, based on distributed technology, injects new momentum into AI through shared Computing Power networks, open data markets, and privacy computing. At the same time, AI can also empower the Web3 ecosystem in many ways, such as optimizing smart contracts and developing anti-cheating algorithms. Therefore, exploring the combination of Web3 and AI is of great significance for building the next generation of internet infrastructure and unlocking the value of data and Computing Power.

Data-driven: A 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 capabilities. 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 major issues:

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

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

• Users can sell idle network resources to AI companies to collect network data in a decentralized manner, providing real and high-quality data for AI model training.
• Adopting the "Labeling for Earnings" model, incentivizing global workers to participate in data labeling through tokens, gathering global expertise.
• The blockchain data trading platform provides a transparent trading environment for both data supply and demand sides, incentivizing data innovation and sharing.

Nevertheless, there are still some issues with data acquisition in the real world, such as inconsistent data quality, high processing difficulty, and insufficient diversity and representativeness. Synthetic data may be a highlight in the future of the Web3 data field. Based on generative AI technologies and simulations, synthetic data can mimic the properties of real data, serving as an effective supplement to improve data utilization efficiency. In fields like 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, and the introduction of relevant regulations reflects 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.

Fully Homomorphic Encryption ( FHE ) allows for direct computational operations on encrypted data without the need to decrypt the data, and the computation results are consistent with the plaintext data computation results.

FHE provides solid protection for AI privacy computing, allowing GPU Computing Power to perform model training and inference tasks in an environment without touching the original data. This brings significant advantages to AI companies, enabling them to safely open API services while protecting trade secrets.

FHEML supports encryption of data and models throughout the entire machine learning lifecycle, ensuring the security of sensitive information and preventing the risk of data leakage. In this way, FHEML enhances data privacy and provides a secure computing framework for AI applications.

FHEML is a complement to ZKML, where ZKML proves the correct execution of machine learning, while FHEML emphasizes computing on encrypted data to maintain data privacy.

Computing Power Revolution: AI Computing in Decentralized Networks

The current AI systems' computational complexity is rapidly increasing, leading to a surge in computing power demand, far exceeding the supply of existing computational resources. This shortage of computing power not only restricts the advancement of AI technology but also makes advanced AI models difficult to reach for most researchers and developers.

At the same time, the global GPU utilization rate is low, coupled with the slowdown in microprocessor performance improvements and chip shortages caused by supply chain and geopolitical factors, making the computing power supply issue even more severe. AI practitioners face a dilemma of either purchasing hardware or renting cloud resources, urgently needing on-demand, cost-effective computing services.

Some decentralized AI Computing Power networks aggregate idle GPU resources worldwide to provide an economical and user-friendly Computing Power market for AI companies. Computing Power demanders can publish computing tasks on the network, and smart contracts assign tasks to nodes contributing Computing Power. The nodes execute the tasks and submit results, receiving rewards after verification. This solution improves resource utilization efficiency and helps to address the Computing Power bottleneck issues in fields such as AI.

In addition to the general decentralized Computing Power network, there are dedicated Computing Power platforms focused on AI training and inference.

Decentralization computing power networks provide a fair and transparent computing power market, breaking monopolies, lowering application barriers, and improving computing power utilization efficiency. In the web3 ecosystem, decentralized computing power networks will play a key role in attracting more innovative applications to join and jointly promote the development and application of AI technology.

DePIN: Web3 empowers Edge AI

Edge AI enables computing to occur at the source of data generation, achieving low latency and real-time processing while protecting user privacy. This technology has been applied in key areas such as autonomous driving.

In the Web3 space, DePIN enhances user privacy protection and reduces the risk of data leakage by processing data locally. The native token economic mechanism of Web3 can incentivize DePIN nodes to provide computing power, building a sustainable ecosystem.

Currently, DePIN is developing rapidly within a certain public chain ecosystem and has become one of the preferred platforms for project deployment. The high transaction processing capability, low fees, and technological innovations of this public chain provide strong support for DePIN projects. At present, the market value of DePIN projects on this public chain has exceeded 10 billion USD, and several well-known projects have made significant progress.

IMO: New Paradigm in AI Model Release

The concept of IMO was first proposed by a certain protocol to tokenize AI models.

In traditional models, AI model developers find it difficult to obtain continuous revenue from the subsequent use of the models, especially after the models are integrated into other products and services. Moreover, the performance and effectiveness of AI models often lack transparency, which limits their 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 to share in the profits generated by the models in the future. A certain protocol uses specific technical standards, combining AI oracles and OPML technology to ensure the authenticity of AI models 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 crypto market, and injects momentum into the sustainable development of AI technology. Although the IMO is currently in the early experimental stage, 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 corresponding actions to achieve set goals. Supported by large language models, AI Agents can not only understand natural language but also plan decisions and execute complex tasks. They can serve as virtual assistants, learning preferences through interaction with users and providing personalized solutions. Even without explicit instructions, AI Agents can autonomously solve problems, improve efficiency, and create new value.

Some open AI-native application platforms provide a comprehensive and user-friendly set of 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. By leveraging generative AI technology, they empower individuals to become super creators. These platforms have trained specialized large language models to make role-playing more human-like; voice cloning technology can accelerate personalized interaction in AI products and significantly reduce voice synthesis costs. The AI Agents customized using these platforms can currently be applied in various fields such as video chatting, language learning, and image generation.

The current exploration of the integration of Web3 and AI focuses more on the infrastructure layer, addressing key issues such as how to obtain high-quality data, protect data privacy, host models on the chain, efficiently utilize decentralized Computing Power, and validate large language models. As these infrastructures gradually improve, the integration of Web3 and AI will give birth to a series of innovative business models and services.