As a decentralized, open, and transparent new Internet paradigm, Web3 has a natural opportunity to merge with AI. 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 black boxes. Web3, based on distributed technology, can inject new impetus into the development of AI through shared computing power networks, open data markets, and privacy computing. At the same time, AI can also bring many empowerments to Web3, such as smart contract optimization, anti-cheating algorithms, etc., to help its ecological construction. Therefore, exploring the combination of Web3 and AI is crucial to building the next generation of Internet infrastructure and releasing the value of data and computing power.
Data-driven: A solid foundation for 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 to gain in-depth understanding and powerful reasoning capabilities. Data not only provides a training basis 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 problems:
The cost of data acquisition is high and cannot be borne by small and medium-sized enterprises;
Data resources are monopolized by technology giants, forming data islands;
Personal data privacy is at risk of leakage and abuse
Web3 can solve the pain points of the traditional model with a new decentralized data paradigm.
Through Grass, users can sell idle networks to AI companies, decentralizedly capture network data, and provide real, high-quality data for AI model training after cleaning and conversion;
Public AI adopts the "label to earn" model, using tokens to incentivize global workers to participate in data labeling, pooling global expertise and enhancing data analysis capabilities;
Blockchain data trading platforms such as Ocean Protocol and Streamr provide an open and transparent trading environment for both data supply and demand parties, encouraging data innovation and sharing.
Nevertheless, there are some problems in acquiring real-world data, such as inconsistent data quality, difficulty in processing, lack of diversity and representativeness, etc. Synthetic data may be the future star of the Web3 data track. Based on generative AI technology and simulation, synthetic data can simulate the properties of real data, serve as an effective supplement to real data, and improve data utilization efficiency. In the fields of autonomous driving, financial market transactions, game development, etc., synthetic data has shown its mature application potential.
Privacy protection: the role of FHE in Web3
In the data-driven era, privacy protection has become a global focus, and the introduction of laws and regulations such as the EU's General Data Protection Regulation (GDPR) reflects the 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 ability of AI models.
FHE, or fully homomorphic encryption, allows computing operations to be performed directly on encrypted data without decrypting the data, and the computing results are consistent with the results of the same calculations on the plaintext data.
FHE provides solid protection for AI privacy computing, enabling GPU computing to perform model training and reasoning tasks without touching the original data. This brings huge advantages to AI companies. They can open API services securely while protecting business secrets.
FHEML supports encryption of data and models throughout the 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.
Computing 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 OpenAI's GPT-3 model requires enormous computing power, equivalent to 355 years of training time on a single device. This computing power shortage not only limits the progress of AI technology, but also makes those advanced AI models out of reach for most researchers and developers.
At the same time, the global GPU utilization rate is less than 40%, coupled with the slowdown in microprocessor performance improvement, and chip shortages caused by supply chain and geopolitical factors, all of which have made the computing power supply problem more serious. AI practitioners are caught in a dilemma: either buy hardware themselves or rent cloud resources. They urgently need an on-demand, cost-effective computing service method.
IO.net is a decentralized AI computing network based on Solana. By aggregating idle GPU resources worldwide, it provides AI companies with an economical and easily accessible computing market. Those who need computing power can publish computing tasks on the network, and smart contracts assign tasks to miner nodes that contribute computing power. Miners perform tasks and submit results, and receive points after verification. IO.net's solution improves resource utilization efficiency and helps solve computing power bottlenecks in fields such as AI.
In addition to general decentralized computing networks, there are also platforms like Gensyn and Flock.io that focus on AI training, as well as dedicated computing networks like Ritual and Fetch.ai that focus on AI reasoning.
The decentralized computing power network provides a fair and transparent computing power market, breaks the monopoly, lowers the application threshold, and improves the utilization efficiency of computing power. In the web3 ecosystem, the decentralized computing power network will play a key role, attracting more innovative dapps to join, and jointly promote the development and application of AI technology.
DePIN: Web3 empowers Edge AI
Imagine that your mobile phone, smart watch, and even the smart devices in your home are all capable of running AI - this is the charm of Edge AI. It allows computing 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 to key areas such as autonomous driving.
In the Web3 field, we have a more familiar name - DePIN. Web3 emphasizes decentralization and the sovereignty of user data. DePIN can enhance user privacy protection and reduce the risk of data leakage by processing data locally.
The native Token economic mechanism can motivate DePIN nodes to provide computing resources and build a sustainable ecosystem.
Currently, DePIN is developing rapidly in the Solana ecosystem and has become one of the preferred public chain platforms for project deployment. Solana's high TPS, low transaction fees, and technological innovation provide strong support for the DePIN project. Currently, the market value of the DePIN project on Solana exceeds US$10 billion, and well-known projects such as Render Network and Helium Network have made significant progress.
IMO: A new paradigm for AI model publishing
The concept of IMO was first proposed by Ora protocol to tokenize AI models.
In the traditional model, due to the lack of a revenue sharing mechanism, once an AI model is developed and put on the market, it is often difficult for developers to obtain sustained benefits from the subsequent use of the model, especially when the model is integrated into other products and services, it is difficult for the original creator to track the usage, let alone obtain benefits from it. In addition, the performance and effects of AI models often lack transparency, which makes it difficult for potential investors and users to assess their true value, limiting the market recognition and commercial potential of the model.
IMO provides a new way of funding and value sharing for open source AI models. Investors can purchase IMO tokens and share the subsequent profits generated by the model. Ora Protocol uses two ERC standards, ERC-7641 and ERC-7007, combined with AI Oracle (Onchain AI Oracle) and OPML technology to ensure the authenticity of AI models and token holders can share profits.
The IMO model enhances transparency and trust, encourages open source collaboration, adapts to crypto market trends, and injects momentum into the sustainable development of AI technology. IMO is still in its early stages of trial, 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 the environment, think independently, and take corresponding actions to achieve set goals. With the support of large language models, AI Agents can not only understand natural language, but also plan decisions and perform complex tasks. They can act as virtual assistants, learn users' preferences through interaction with them, and provide personalized solutions. In the absence of clear instructions, AI Agents can also solve problems autonomously, improve efficiency, and create new value.
Myshell is an open AI native application platform that provides a comprehensive and easy-to-use creative tool set. It supports users to configure robot functions, appearance, voice, and connect to external knowledge bases. It is committed to creating a fair and open AI content ecosystem, and using generative AI technology to empower individuals to become super creators. Myshell has trained a special large language model to make role-playing more humane; voice cloning technology can accelerate the personalized interaction of AI products. MyShell reduces the cost of voice synthesis by 99%, and voice cloning can be achieved in just 1 minute. The AI Agent customized by Myshell can currently be applied to many fields such as video chat, language learning, and image generation.
In the integration of Web3 and AI, the current focus is more on exploring the infrastructure layer, such as how to obtain high-quality data, protect data privacy, how to host models on the chain, how to improve the efficient use of decentralized computing power, how to verify large language models, etc. 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.
