The Integration of Web3 and AI: Building a New Generation of Internet Infrastructure

Web3, as a decentralized, open, and transparent new paradigm of the internet, has a natural opportunity for integration with AI. Under the traditional centralized architecture, AI computation and data resources are strictly controlled, facing numerous challenges such as computing power bottlenecks, privacy leaks, and algorithmic black boxes. Web3, based on distributed technology, injects new momentum into AI development through shared computing power networks, open data markets, and privacy computing. At the same time, AI can also bring many capabilities to Web3, such as smart contract optimization and anti-cheating algorithms, contributing to its ecological construction. Exploring the combination of Web3 and AI is crucial for building the next generation of internet infrastructure and unlocking 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, just like fuel to an engine. AI models need to digest a large amount of high-quality data in order 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 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 tech giants, creating data silos.
• Personal data privacy is at risk of leakage and abuse

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

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

However, there are issues with the acquisition of real-world data, such as inconsistent quality, processing difficulties, and insufficient diversity and representativeness. Synthetic data may be the future star of the Web3 data track. Based on generative AI technology and simulation, 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 already demonstrated mature application potential.

Privacy Protection: The Role of FHE in Web3

In the data-driven era, privacy protection has become a global focal point, with regulations such as the EU General Data Protection Regulation (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 for direct computation operations on encrypted data without the need to decrypt the data, and the computation results are consistent with the results of computations on plaintext data. FHE provides solid protection for AI privacy computing, enabling GPU computing power to perform model training and inference tasks in an environment that does not touch the original data. This brings significant advantages to AI companies, allowing them to securely open API services while protecting business secrets.

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

Computing Power Revolution: AI Computation in Decentralized Networks

The current AI system's computational complexity doubles every three months, leading to a surge in demand for computing power that far exceeds the supply of existing computational resources. For example, training the GPT-3 model requires immense computational power, equivalent to 355 years of training time on a single device. This shortage of computing power not only limits the advancement of AI technology but also makes advanced AI models out of reach for most researchers and developers.

At the same time, the global GPU utilization rate is below 40%, coupled with the slowdown in microprocessor performance improvements, and chip shortages caused by supply chain and geopolitical factors, which exacerbate the computing power supply issue. AI practitioners are caught in a dilemma: either purchase hardware or rent cloud resources, urgently needing on-demand and cost-effective computing services.

A decentralized AI computing power network aggregates idle GPU resources globally to provide an economically accessible computing power market for AI companies. Demand-side users can publish computing tasks on the network, and smart contracts assign the tasks to miner nodes that contribute computing power. Miners execute the tasks and submit results, and after verification, they receive point rewards. 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. The decentralized computing network provides a fair and transparent computing power market, breaking monopolies, lowering application barriers, and improving computing power utilization efficiency. In the web3 ecosystem, decentralized computing networks will play a key role in attracting more innovative dapps to join and jointly promote the development and application of AI technology.

DePIN: Web3 Empowers Edge AI

Imagine your mobile phone, smart watch, and even smart devices at home all having the capability to run AI—this is the charm of Edge AI. It enables 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 key areas such as autonomous driving.

In the Web3 space, we have a more familiar name - DePIN. Web3 emphasizes decentralization and user data sovereignty, while DePIN enhances user privacy protection and reduces the risk of data leakage by processing data locally; the Web3 native token economic mechanism incentivizes DePIN nodes to provide computing resources, building a sustainable ecosystem.

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

IMO: New Paradigm for AI Model Release

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

In traditional models, due to the lack of a revenue-sharing mechanism, AI model developers find it difficult to obtain continuous revenue from subsequent usage, especially when the model is integrated into other products and services. The original creators struggle to track usage and even harder to obtain revenue. Moreover, the performance and effectiveness of AI models often lack transparency, making it difficult for potential investors and users to assess their true value, which limits the market recognition and commercial potential of the models.

IMO provides new funding support and value-sharing methods for open-source AI models, allowing investors to purchase IMO tokens and share in the profits generated by the models in the future. A certain protocol uses two ERC standards, combining AI oracles and OPML technology to ensure the authenticity of the AI models and that token holders can share in the profits.

The IMO model enhances transparency and trust, encourages open-source collaboration, and adapts to trends in the cryptocurrency market, injecting momentum into the sustainable development of AI technology. IMO is currently in the early 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 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 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 easy-to-use set of creative tools, supporting users to configure robot functions, appearance, voice, and connect to external knowledge bases, dedicated to creating a fair and open AI content ecosystem. Utilizing generative AI technology, it empowers individuals to become super creators. The platform has trained a specialized large language model to make role-playing more human-like; voice cloning technology can accelerate personalized interaction of AI products, reducing voice synthesis costs by 99%, with voice cloning achievable in just 1 minute. The AI Agent customized through this platform is currently applicable in various fields such as video chatting, language learning, and image generation.

In the integration of Web3 and AI, current efforts are more focused on exploring the infrastructure layer, including key issues such as how to obtain high-quality data, protect data privacy, host models on the blockchain, improve the efficient use of decentralized computing power, and validate large language models. As these infrastructures gradually improve, we have reason to believe that the integration of Web3 and AI will give rise to a series of innovative business models and services.