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

Web3, as a new decentralized, open, and transparent internet paradigm, has a natural synergy with AI. Under traditional centralized architectures, AI computing and data resources face numerous challenges, such as computing power bottlenecks, privacy breaches, and algorithm opacity. Web3, based on distributed technology, can provide new momentum for AI development through shared computing power networks, open data markets, and privacy computing. At the same time, AI can also bring many benefits to the Web3 ecosystem, such as optimizing smart contracts and improving anti-cheating mechanisms. Therefore, exploring the collaborative development of Web3 and AI is of great significance for building future internet infrastructure and fully leveraging the value of data and computing power.

Data-Driven: The Cornerstone of AI and Web3

Data is the key to driving AI progress, just as fuel is to an engine. AI models need to digest vast amounts of high-quality data to gain deep insights and powerful reasoning capabilities. Data is not only the foundation for training machine learning models but also determines the accuracy and reliability of the models.

The traditional centralized AI data acquisition and usage model has the following main 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 tech companies, creating data silos.
• Personal data privacy is at risk of leakage and misuse.

Web3 offers a new decentralized data paradigm that is expected to address these pain points:

• Users can sell idle network resources to AI companies, achieving decentralized data collection.
• Adopting the "Earn by Annotating" model, incentivizing global workers to participate in data annotation through tokens.
• The blockchain data trading platform provides a transparent trading environment for both data suppliers and demanders.

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 an important direction for the future of the Web3 data field. Based on generative AI technology and simulation, synthetic data can mimic the characteristics of real data, serving as an effective supplement to improve data usage efficiency. In fields such as autonomous driving, financial market trading, and game development, synthetic data has shown mature application prospects.

Privacy Protection: The Application of FHE in Web3

In the data-driven era, privacy protection has become a global focus. The introduction of regulations such as the EU's General Data Protection Regulation (GDPR) reflects a strict protection 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 computation on encrypted data without the need for decryption, and the computation results are consistent with the plaintext data results. FHE provides strong guarantees for AI privacy computing, enabling GPU computing power to perform model training and inference tasks without accessing the original data. This brings significant advantages to AI companies, allowing them to securely open API services while protecting trade secrets.

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. In this way, FHEML enhances data privacy protection and provides a secure computing framework for AI applications.

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

Computing Power Revolution: AI Computing in Decentralized Networks

The computational complexity of current AI systems doubles every three months, resulting in a surge in computing power demand that far exceeds the supply of existing computing resources. For example, the training of a well-known AI model requires immense computing power, equivalent to 355 years of training time on a single device. Such a shortage of computing power not only hinders the advancement of AI technology but also makes advanced AI models difficult for most researchers and developers to reach.

At the same time, the global GPU utilization rate is below 40%, coupled with a slowdown in the performance improvement of microprocessors, and a chip shortage caused by supply chain and geopolitical factors, making the computing power supply issue even more severe. AI practitioners face a dilemma: either purchase hardware themselves or rent cloud resources, and they urgently need an on-demand, cost-effective computing service.

The decentralized AI computing power network aggregates idle GPU resources from around the world to provide AI companies with an economical and easily accessible computing power market. Demanders of computing power can post computational tasks on the network, and smart contracts allocate the tasks to nodes that contribute computing power. The nodes execute the tasks and submit the 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 general decentralized computing networks, there are dedicated computing platforms 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 applications to join and jointly promote the development and application of AI technology.

DePIN: Web3 Empowering Edge AI

Edge AI enables computation 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 critical fields such as autonomous driving.

In the Web3 domain, the name we are more familiar with is DePIN. Web3 emphasizes decentralization and user data sovereignty, while DePIN enhances user privacy protection through local data processing, reducing the risk of data leakage; the native token economic mechanism of Web3 can incentivize DePIN nodes to provide computing resources, building a sustainable ecosystem.

Currently, DePIN is developing rapidly in certain public chain ecosystems, becoming one of the popular choices for project deployment. The high performance, low transaction fees, and technological innovations of these public chains provide strong support for DePIN projects. The market capitalization of DePIN projects on some public chains has exceeded ten billion dollars, and some well-known projects have made significant progress.

IMO: New Paradigm of AI Model Release

The IMO concept 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 when the models are integrated into other products and services. In addition, the performance and effectiveness of AI models often lack transparency, which limits their market recognition and commercial potential.

IMO provides a new type of funding support and value-sharing method for open-source AI models, allowing investors to purchase IMO tokens and share in the subsequent profits of the models. A certain protocol uses specific technical standards, combining on-chain AI oracles and OPML technology to ensure the authenticity of AI models and the sharing of profits among token holders.

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

AI Agent: A New Era of Interactive Experience

AI agents can perceive their environment, think independently, and take corresponding actions to achieve their 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 user preferences through interaction and providing personalized solutions. Even without explicit instructions, AI agents can autonomously solve problems, improve efficiency, and create new value.

A certain AI-native application platform provides a comprehensive and user-friendly suite of creation tools, supporting users in configuring robot features, appearance, voice, and connecting to external knowledge bases, dedicated to building a fair and open AI content ecosystem. By leveraging generative AI technology, it empowers individuals to become super creators. The platform has trained a specialized large language model, making role-playing more human-like; voice cloning technology can accelerate personalized interactions in AI products, reducing voice synthesis costs by 99%, with voice cloning achievable in just 1 minute. The customized AI Agent from this platform can currently be applied in various fields such as video chatting, language learning, and image generation.

In terms of the integration of Web3 and AI, current attention is more focused on exploring the infrastructure layer, such as high-quality data acquisition, data privacy protection, on-chain model hosting, improving the efficiency of decentralized computing power usage, and verifying large language models. As these infrastructures gradually improve, the integration of Web3 and AI is expected to give rise to a series of innovative business models and services.