The Integration of Web3 and AI: Data-Driven and Privacy Computing Open a New Era of Computing Power

Web3, as a new paradigm of the internet that emphasizes Decentralization, openness, and transparency, has a natural opportunity for integration with artificial intelligence. Under the traditional centralized architecture, AI computing and data resources are strictly controlled, and there are many challenges such as Computing Power bottlenecks, privacy leaks, and Algorithm opacity. Web3, based on distributed technology, can inject new momentum into AI development through shared Computing Power networks, open data markets, and privacy computing. At the same time, AI can also empower Web3 in many ways, such as optimizing smart contracts and anti-cheating Algorithms, aiding in its ecological construction. Therefore, 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: A Solid Foundation of AI and Web3

Data is the core driving force behind the development of AI, just as fuel is to an engine. AI models need to digest a large amount of high-quality data in order to gain deep understanding and strong reasoning abilities. Data not only provides the training foundation for machine learning models but also determines the accuracy and reliability of the models.

In the traditional centralized AI data acquisition and utilization model, there are several 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 tech giants, creating data silos;
• Personal data privacy is at risk of leakage and abuse.

Web3 can address the pain points of traditional models with a new Decentralization data paradigm.

• Through a decentralized network, users can sell idle networks to AI companies, capture network data, and after cleaning and transformation, provide 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 to enhance data analysis capabilities;
• The blockchain data trading platform provides a public and transparent trading environment for both data supply and demand sides, incentivizing innovation and sharing of data.

Nevertheless, there are some issues with data acquisition in the real world, such as varying data quality, high processing difficulty, and insufficient diversity and representativeness. Synthetic data may be 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 real data and improving data utilization efficiency. In fields such as autonomous driving, financial market trading, and game development, synthetic data has already demonstrated its mature application potential.

Privacy Protection: The Role 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 guard over personal privacy. However, this also brings challenges: some sensitive data cannot be fully utilized due to privacy risks, which undoubtedly limits the potential and reasoning capabilities of AI models.

FHE, or Fully Homomorphic Encryption, allows for the direct computation on encrypted data without the need to decrypt it, and the computation results are consistent with those obtained from performing the same computations 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 without touching the original data. This brings significant advantages to AI companies. They can securely open API services while protecting commercial secrets.

FHEML supports the encryption of data and models throughout the entire machine learning cycle, 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 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, training a certain well-known AI model requires enormous computing power, equivalent to 355 years of training time on a single device. Such a shortage of computing power not only limits the advancement of AI technology but also makes these advanced AI models out of reach for most researchers and developers.

At the same time, the global utilization rate of GPUs is less than 40%, coupled with the slowdown in microprocessor performance improvements and chip shortages caused by supply chain and geopolitical factors, making the supply of computing power even more severe. AI practitioners are caught in a dilemma: either purchase hardware themselves or rent cloud resources; they urgently need a demand-based, cost-effective computing service.

The decentralized AI computing power network aggregates idle GPU resources from around the world, providing AI companies with an economical and easily accessible computing power market. Computing power demanders can publish computing tasks on the network, and smart contracts assign tasks to miner nodes that contribute computing power. Miners execute tasks and submit results, and after verification, they receive points as rewards. 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 also platforms focused on AI training and dedicated computing power networks for AI inference.

Decentralized computing power networks provide a fair and transparent computing power market, breaking monopolies, lowering application thresholds, and improving the utilization efficiency of computing power. In the web3 ecosystem, decentralized computing power networks will play a key role in attracting more innovative dapps to join, collectively promoting the development and application of AI technology.

DePIN: Web3 empowers Edge AI

Imagine that your smartphone, smart watch, and even smart devices at home all have the ability 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 already been applied in key areas such as autonomous driving.

In the Web3 field, we have a more familiar name---DePIN. Web3 emphasizes Decentralization and user data sovereignty. 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 public chain 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 some well-known projects have made significant progress.

IMO: New Paradigm for AI Model Release

The concept of IMO was first proposed by a certain protocol, which tokenizes AI models.

In traditional models, due to the lack of revenue sharing mechanisms, once an AI model is developed and brought to market, developers often find it difficult to obtain ongoing revenue from the subsequent use of the model, especially when the model is integrated into other products and services. The original creators find it hard to track usage, let alone derive revenue from it. Additionally, 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 a new funding support and value sharing method for open-source AI models. Investors can purchase IMO tokens to share in the profits generated by the model in the future. A certain protocol uses two ERC standards, combining 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. Currently, the IMO is still 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, think independently, and take corresponding actions to achieve established 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.

An open AI-native application platform provides a comprehensive and user-friendly toolset for creation, supporting users in configuring robot functions, 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 to make role-playing more human-like; voice cloning technology can accelerate personalized interactions of AI products, reducing voice synthesis costs by 99%, and voice cloning can be achieved in just 1 minute. The customized AI Agent from this platform is currently applicable in various fields such as video chatting, language learning, and image generation.

In the integration of Web3 and AI, the current focus is more on exploring the infrastructure layer, including how to obtain high-quality data, protect data privacy, host models on-chain, improve the efficient use of Decentralization Computing Power, and verify large language models, among other key issues. As these infrastructures gradually improve, we have reason to believe that the integration of Web3 and AI will give birth to a series of innovative business models and services.