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

Web3, as a decentralized, open, and transparent new internet model, has a natural opportunity for integration with artificial intelligence. Under traditional centralized architecture, AI's computing and data resources are strictly controlled, facing numerous challenges such as computational bottlenecks, privacy breaches, and algorithmic black boxes. In contrast, Web3, based on distributed technology, can inject new momentum into AI development through shared computing networks, open data markets, and privacy computation. At the same time, AI can also empower Web3 in various ways, such as optimizing smart contracts and anti-cheating algorithms, aiding its ecosystem development. Therefore, exploring the integration of Web3 and AI is of great significance for building the next-generation internet infrastructure and unlocking the value of data and computing power.

Data-Driven: The Cornerstone of AI and Web3

Data is the core driving force behind the development of AI. AI models need to digest vast amounts 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 islands.
• Personal data privacy is at risk of leakage and abuse

Web3 can address these pain points through a new decentralized data paradigm:

• Users can sell idle networks to AI companies to decentralize the collection of network data, which after cleaning and conversion provides 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 and open 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 become 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 already shown mature application potential.

Privacy Protection: The Application of FHE in Web3

In the data-driven era, privacy protection has become a global focus. Regulations such as the European Union's General Data Protection Regulation reflect 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 direct computation on encrypted data without decrypting it, and the computation result is consistent with the result of performing the same computation on plaintext data.

FHE provides solid protection for AI privacy computing, allowing 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, enabling them to securely 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 strengthens data privacy 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 emphasizes computing on encrypted data to maintain data privacy.

Computing Power Revolution: AI Computing in Decentralized Networks

The current AI system's computational complexity doubles every three months, leading to a surge in computing power demand that far exceeds the existing supply of computational resources. For example, training the GPT-3 model requires enormous computing 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 unattainable for most researchers and developers.

At the same time, global GPU utilization is less than 40%, coupled with the slowdown in the performance improvement of microprocessors and the chip shortage caused by supply chain and geopolitical factors, which has exacerbated the issue of computing power supply. AI practitioners are caught in a dilemma: either purchase hardware or rent cloud resources, and they urgently need a demand-based, cost-effective computing service model.

A decentralized AI computing power network aggregates idle GPU resources from around the world, providing an economical and user-friendly computing power marketplace for AI companies. Demand-side users can publish computing tasks on the network, and smart contracts assign these tasks to miner nodes that contribute computing power. Miners execute the tasks and submit results, receiving points as rewards after verification. This solution improves resource utilization efficiency and helps address the computing power bottleneck in fields such as AI.

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

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DePIN: Web3 Empowering Edge AI

Imagine a scenario where your phone, smart watch, and even smart devices in your home have the capability to run AI – this is the charm of Edge AI. It 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 areas such as autonomous driving.

In the Web3 field, we have a more familiar name - DePIN. Web3 emphasizes decentralization and user data sovereignty. DePIN can enhance user privacy protection and reduce the risk of data breaches by processing data locally. The native token economic mechanism of Web3 can incentivize DePIN nodes to provide computing resources, building a sustainable ecosystem.

Currently, DePIN is developing rapidly within 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. At present, 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, which tokenizes AI models.

In traditional models, due to the lack of revenue-sharing mechanisms, AI model developers find it difficult to obtain ongoing revenue from the subsequent use of the models, especially when the models are integrated into other products and services. The original creators struggle to track usage and receive earnings. In addition, the performance and effectiveness of AI models often lack transparency, making it difficult for potential investors and users to assess their true value, thus limiting the market recognition and commercial potential of the models.

IMO provides a new funding support and value-sharing method for open-source AI models, where investors can purchase IMO tokens to share in the profits generated by the model in the future. A certain protocol uses a specific ERC standard, combined with 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 into the sustainable development of AI technology. The IMO is currently in the early experimental 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, engage in independent thinking, and take corresponding actions to achieve set goals. Supported by large language models, AI agents not only understand natural language but also plan decisions and execute complex tasks. They can act 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 set of creation 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, empowering individuals to become super creators using generative AI technology. The platform has trained a specialized large language model, making role-playing more human-like; voice cloning technology can accelerate personalized interactions of AI products, reducing voice synthesis costs by 99%, with voice cloning achievable in just 1 minute. With the customized AI Agent provided by this platform, it can currently be applied in various fields such as video chatting, language learning, and image generation.

Currently, the integration of Web3 and AI is more focused on exploring the infrastructure layer, including how to obtain high-quality data, protect data privacy, how to host models on-chain, how to efficiently utilize decentralized computing power, and how to validate large language models. As these infrastructures gradually improve, the integration of Web3 and AI is expected to give birth to a series of innovative business models and services.

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