Source: IOBC Capital
Web3, as a decentralized, open, and transparent new paradigm of the internet, has a natural opportunity to integrate with AI. In the traditional centralized architecture, AI computation and data resources are strictly controlled, and there are many challenges such as computational bottlenecks, privacy breaches, and algorithm black boxes. However, Web3, based on distributed technology, can inject new momentum into the development of AI through shared computing networks, open data markets, privacy computation, and other methods. At the same time, AI can also empower Web3 in various ways, such as optimizing smart contracts and anti-cheating algorithms, and helping in its ecosystem 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 for AI and Web3
Data is the core driving force behind AI development, just like fuel to an engine. AI models require a large amount of high-quality data to gain deep understanding and powerful 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 main problems:
– High cost of data acquisition, which is difficult for small and medium-sized enterprises to afford.
– Data resources monopolized by tech giants, resulting in data silos.
– Risks of personal data leakage and misuse.
Web3 can solve the pain points of the traditional model with a new decentralized data paradigm. Grass allows users to sell idle networks to AI companies, enabling decentralized data collection. After cleaning and transformation, high-quality and real data can be provided for AI model training. Public AI adopts the “label to earn” model, incentivizing global workers to participate in data annotation and consolidating global expertise to enhance data analysis capabilities. Blockchain data trading platforms like Ocean Protocol and Streamr provide an open and transparent trading environment for data supply and demand, promoting data innovation and sharing.
However, real-world data acquisition also faces challenges such as varying data quality, difficulty in processing, lack of diversity and representativeness. Synthetic data may become the star of Web3 data track in the future. Based on generative AI technology and simulation, synthetic data can simulate the attributes of real data and serve as an effective supplement, improving the efficiency of data usage. 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 era of data-driven AI, privacy protection has become a global focus. Regulations such as the European Union’s General Data Protection Regulation (GDPR) reflect strict protection of 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 abilities of AI models.
Fully Homomorphic Encryption (FHE) allows direct computation operations on encrypted data without the need for decryption, and the computed results are consistent with those obtained from plaintext data.
FHE provides robust protection for AI privacy computation, allowing GPU computing to perform model training and inference tasks in an environment that doesn’t touch the original data. This brings significant advantages for AI companies. They can safely open API services while protecting trade secrets.
FHEML supports encryption of data and models throughout the entire machine learning cycle, ensuring the security of sensitive information and preventing data leakage risks. In this way, FHEML strengthens data privacy and provides a secure computing framework for AI applications.
FHEML complements ZKML, which proves the correct execution of machine learning, while FHEML emphasizes computing on encrypted data to maintain data privacy.
Computational Revolution: AI Computing in Decentralized Networks
The computational complexity of current AI systems doubles every three months, leading to a surge in computational demands that surpasses the existing supply of computing resources. For example, OpenAI’s GPT-3 model training requires massive computational power equivalent to 355 years on a single device. Such a shortage of computational power not only limits the advancement of AI technology but also makes advanced AI models inaccessible to most researchers and developers.
Additionally, the global GPU utilization rate is less than 40%, coupled with the slowdown in the improvement of microprocessor performance, chip shortages caused by supply chain and geopolitical factors, the problem of computational power supply becomes more severe. AI practitioners are caught in a dilemma: either purchasing hardware or renting cloud resources. They urgently need an on-demand and cost-effective computing service.
IO.net is a decentralized AI computing network based on Solana. It aggregates idle GPU resources globally, providing an economical and accessible computing market for AI companies. Computational demanders can publish computing tasks on the network, and smart contracts assign tasks to contributing mining nodes. Miners execute tasks and submit results, earning rewards after verification. IO.net’s solution improves resource utilization efficiency and helps solve computational bottlenecks in AI and other fields.
In addition to general decentralized computing networks, there are platforms focused on AI training such as Gensyn and Flock.io, as well as dedicated AI inference networks like Ritual and Fetch.ai.
Decentralized computing networks provide a fair and transparent computing market, breaking monopolies, lowering the entry barriers, and improving the utilization efficiency of computational power. In the Web3 ecosystem, decentralized computing networks will play a crucial role in attracting more innovative DApps to promote the development and application of AI technology.
DePIN: Web3 Empowering Edge AI
Imagine your phone, smartwatch, and even smart devices in your home all having the capability to run AI – that’s the charm of Edge AI. It enables computation to happen at the source of data generation, achieving low latency, real-time processing, and protecting user privacy. Edge AI technology has been applied in critical fields such as autonomous driving.
In the Web3 domain, we have a more familiar name – DePIN. Web3 emphasizes decentralization and user data sovereignty. DePIN enhances user privacy protection by processing data locally, reducing the risk of data leakage. Web3’s native token economy mechanism incentivizes DePIN nodes to provide computational resources, building a sustainable ecosystem.
Currently, DePIN is rapidly developing in the Solana ecosystem and has become one of the preferred blockchain platforms for project deployment. Solana’s high TPS, low transaction fees, and technological innovations provide strong support for DePIN projects. Currently, DePIN projects on Solana have a market value exceeding $10 billion, and well-known projects like Render Network and Helium Network have made significant progress.
IMO: A New Paradigm for AI Model Deployment
The concept of IMO was first proposed by Ora Protocol, tokenizing AI models.
In the traditional model, due to the lack of profit-sharing mechanisms, developers often struggle to obtain continuous profits from the subsequent use of AI models, especially when the models are integrated into other products and services. It is difficult for the original creators to track their usage and benefit from them. Furthermore, 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 recognition and commercial potential.
IMO provides a new funding support and value-sharing method for open-source AI models. Investors can purchase IMO tokens and share the profits generated from the models. Ora Protocol uses the ERC-7641 and ERC-7007 standards, combined with Onchain AI Oracle and OPML technology, to ensure the authenticity of AI models and enable token holders to share profits.
The IMO model enhances transparency and trust, encourages open collaboration, adapts to the trends of the crypto market, and injects momentum into the sustainable development of AI technology. IMO is still in the early stages of experimentation, but with increasing market acceptance and expanding participation, its innovation and potential value are worth looking forward to.
AI Agent: A New Era of Interactive Experience
AI Agents can perceive the environment, engage in independent thinking, and take corresponding actions to achieve predefined goals. With the support of 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, learn user preferences through interaction, and provide personalized solutions. In the absence of explicit instructions, AI Agents can autonomously solve problems, improve efficiency, and create new value.
Myshell is an open AI-native application platform that provides a comprehensive and user-friendly set of creative tools. It supports users in configuring robot functions, appearances, sounds, and connecting external knowledge bases. Myshell is committed to building a fair and open AI content ecosystem, empowering individuals to become super creators using generative AI technology. Myshell has trained dedicated large language models to make role-playing more human-like. Voice cloning technology can accelerate personalized interactions in AI products. MyShell reduces the cost of speech synthesis by 99%, and voice cloning can be achieved in just one minute. With the customized AI Agents provided by Myshell, it can currently be applied in various fields such as video chat, language learning, and image generation.
In the integration of Web3 and AI, the current focus is more on the exploration of infrastructure layers, such as obtaining high-quality data, protecting data privacy, hosting models on the blockchain, improving the efficient utilization of decentralized computing power, and verifying large language models. With the gradual improvement of these infrastructures, we have reason to believe that the integration of Web3 and AI will give birth to a series of innovative business models and services.
