Web3, as a decentralized, open, and transparent new paradigm of the internet, has an inherent synergy with AI. Under the traditional centralized architecture, AI computing and data resources are subject to strict control, facing numerous challenges such as computational bottlenecks, privacy breaches, and algorithmic black boxes. On the other hand, Web3, built on distributed technologies, can infuse new vitality into AI development through shared computing networks, open data markets, and privacy-preserving computation. Simultaneously, AI can empower Web3 with capabilities like optimizing smart contracts and anti-cheating algorithms, aiding in the ecosystem's construction. Therefore, exploring the convergence of Web3 and AI is crucial for building the next-generation 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 AI development, akin to fuel for an engine. AI models require ingesting vast amounts of high-quality data to gain profound understanding and robust reasoning abilities. Data not only provides the training foundation for machine learning models but also determines their accuracy and reliability.

In the traditional centralized AI data acquisition and utilization model, several key issues arise:

• Data acquisition costs are prohibitively high, making it challenging for small and medium-sized enterprises to participate.
• Data resources are monopolized by tech giants, creating data silos.
• Personal data privacy faces risks of leakage and misuse.

Web3 offers a new decentralized data paradigm to address the pain points of traditional models:

• Through projects like Grass, users can sell their idle network capacity to AI companies, enabling decentralized web data crawling, cleaning, and transformation to provide real, high-quality data for AI model training.
• Public AI adopts a "label to earn" model, incentivizing global workers with tokens to participate in data annotation, aggregating global expertise, and enhancing data analysis capabilities.
• Blockchain data transaction platforms like Ocean Protocol and Streamr provide an open and transparent trading environment for data supply and demand, fostering data innovation and sharing.

Nevertheless, real-world data acquisition also faces challenges such as varying data quality, high processing complexity, and insufficient diversity and representativeness. Synthetic data may be the rising star in the Web3 data realm. Based on generative AI technologies and simulations, synthetic data can mimic the properties of real data, effectively complementing it and improving data usage efficiency. In domains like 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 focal point, as evidenced by the enactment of regulations like the EU's General Data Protection Regulation (GDPR), reflecting the strict guardianship of personal privacy. However, this has also brought challenges: some sensitive data cannot be fully utilized due to privacy risks, undoubtedly limiting the potential and reasoning capabilities of AI models.

FHE, or Fully Homomorphic Encryption, allows direct computation on encrypted data without the need for decryption, while the computation results are consistent with performing the same operations on plaintext data.

FHE provides robust protection for AI privacy computation, enabling GPU computing power to execute model training and inference tasks without accessing the original data. This presents significant advantages for AI companies, as they can 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 data leakages. In this way, FHEML reinforces data privacy and provides a secure computing framework for AI applications.

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

The Computing Revolution: AI Computation in Decentralized Networks

The computational complexity of current AI systems doubles every three months, leading to a surge in computing power demands that far exceeds the supply of existing computing resources. For instance, the training of OpenAI's GPT-3 model required immense computing power, equivalent to 355 years of training time on a single device. Such a shortage of computing power not only limits the progress of AI technology but also renders advanced AI models inaccessible to most researchers and developers.

Additionally, the global GPU utilization rate is below 40%, coupled with the slowdown in microprocessor performance improvements and supply chain and geopolitical factors contributing to chip shortages, exacerbating the computing power supply issue. AI practitioners find themselves in a dilemma: either purchasing hardware or renting cloud resources, desperately needing an on-demand, cost-effective computing service model.

IO.net is a decentralized AI computing power network based on Solana, aggregating idle GPU resources globally to provide AI companies with an economical and accessible computing power market. Entities demanding computing power can publish computation tasks on the network, and smart contracts will allocate tasks to contributing miner nodes. Miners execute the tasks, submit the results, and receive reward points upon successful verification. IO.net's approach improves resource utilization efficiency, helping to alleviate computing power bottlenecks in fields like AI.

In addition to general decentralized computing power networks, there are platforms dedicated to AI training, such as Gensyn and Flock.io, as well as specialized computing power networks focused on AI inference, like Ritual and Fetch.ai.

Decentralized computing power networks provide a fair and transparent computing power market, breaking monopolies, lowering application barriers, and improving utilization efficiency. Within the Web3 ecosystem, decentralized computing power networks will play a crucial role, attracting more innovative dApps and jointly driving the development and application of AI technologies.

DePIN: Web3 Empowering Edge AI

Imagine your smartphone, smartwatch, or even smart home devices possessing the ability to run AI – this is the allure of Edge AI. It enables computation to occur at the data source, realizing low latency and real-time processing while protecting user privacy. Edge AI technology has already been applied in critical domains such as autonomous driving.

In the Web3 realm, we have a more familiar name – DePIN. Web3 emphasizes decentralization and user data sovereignty, and DePIN enhances user privacy protection by processing data locally, reducing the risk of data leakages. Web3's native token economy can incentivize DePIN nodes to provide computing resources, building a sustainable ecosystem.

Currently, DePIN is rapidly developing in the Solana ecosystem, becoming one of the preferred public chain platforms for project deployment. Solana's high throughput, low transaction fees, and technological innovations have provided strong support for DePIN projects. At present, the market capitalization of DePIN projects on Solana exceeds $10 billion, with notable projects like Render Network and Helium Network achieving significant progress.

IMO: A New Paradigm for AI Model Publishing

The concept of IMO (Initial Model Offering) was first introduced by the Ora protocol, tokenizing AI models.

In the traditional model, due to the lack of a revenue-sharing mechanism, once an AI model is developed and released to the market, developers often struggle to obtain continuous revenue from the subsequent use of the model. Especially when the model is integrated into other products and services, it becomes challenging for the original creators to track usage and, consequently, generate revenue. Additionally, the performance and effectiveness of AI models often lack transparency, making it difficult for potential investors and users to evaluate their true value, limiting market acceptance and commercial potential.

IMO provides a novel method of funding and value-sharing for open-source AI models. Investors can purchase IMO tokens to share in the subsequent revenue generated by the model. Ora Protocol utilizes the ERC-7641 and ERC-7007 ERC standards, combined with an Onchain AI Oracle and OPML technology, to ensure the authenticity of AI models and enable token holders to share in the revenue.

The IMO model enhances transparency and trust, encourages open-source collaboration, aligns with crypto market trends, and injects momentum into the sustainable development of AI technology. While IMO is still in its early experimental stage, as market acceptance and participation expand, its innovative nature and potential value are worth anticipating.

AI Agents: A New Era of Interactive Experiences

AI Agents can perceive their environment, engage in independent thinking, and take appropriate actions to achieve predefined goals. With the support of large language models, AI Agents not only understand natural language but can also plan, make decisions, and execute complex tasks. They can function as virtual assistants, learning user preferences through interactions and providing personalized solutions. Even without explicit instructions, AI Agents can autonomously solve problems, enhance efficiency, and create new value.

Myshell is an open AI-native application platform offering a comprehensive and user-friendly toolset for configuring bot functionalities, appearances, voices, and connecting to external knowledge bases. It strives to create a fair and open AI content ecosystem, empowering individuals to become super creators by leveraging generative AI technologies. Myshell has trained specialized large language models to make role-playing more humanized. Its voice cloning technology can accelerate personalized AI product interactions, reducing voice synthesis costs by 99%, with voice cloning achievable in just 1 minute. Customized AI Agents created with Myshell can currently be applied to various domains, including video chatting, language learning, and image generation.

In the convergence of Web3 and AI, the current focus is primarily on exploring the infrastructure layer, addressing critical issues such as acquiring high-quality data, protecting data privacy, hosting models on-chain, improving the efficient utilization of decentralized computing power, and verifying large language models. As these infrastructural components gradually mature, we have reason to believe that the fusion of Web3 and AI will give birth to a series of innovative business models and services.