When Huang Renxun spoke at WGS in Dubai, he proposed the term "sovereign AI". So, which sovereign AI can meet the interests and demands of the Crypto community?
Maybe it needs to be built in the form of Web3 AI. Vitalik described the synergy between AI and Crypto in the article "The promise and challenges of crypto AI applications": The decentralization of Crypto can balance the centralization of AI; AI is opaque, Crypto brings transparency; AI requires data, and blockchain is conducive to data storage and tracking. This kind of collaboration runs through the entire industrial landscape of Web3 AI.
Most Web3 AI projects use blockchain technology to solve the construction problems of infrastructure projects in the AI industry, and a few projects use AI to solve certain problems in Web3 applications.
In the past two years, the computing power used to train large AI models has increased exponentially , basically doubling every quarter, growing at a rate far exceeding Moore's Law. This situation has led to a long-term imbalance in the supply and demand of AI computing power, and the prices of hardware such as GPUs have risen rapidly, thus raising the cost of computing power.
But at the same time, there is also a large amount of idle mid- to low-end computing hardware in the market. It is possible that the single computing power of this part of mid-to-low-end hardware cannot meet high-performance needs. However, if a distributed computing power network is built through Web3 and a decentralized computing resource network is created through computing power leasing and sharing, it can still meet the needs of many AI applications. Because it uses distributed idle computing power, the cost of AI computing power can be significantly reduced.
The computing power layer breakdown includes:
Data is the oil and blood of AI. If you do not rely on Web3, generally only giant companies have a large amount of user data. It is difficult for ordinary startups to obtain extensive data, and the value of user data in the AI industry is not fed back to users. Through Web3 AI, processes such as data collection, data annotation, and distributed data storage can be made cheaper, more transparent, and more beneficial to users.
Collecting high-quality data is a prerequisite for AI model training. Through Web3, distributed networks can be used, combined with appropriate Token incentive mechanisms, and crowdsourcing collection methods. Get high-quality, broad-based data at a lower cost.
Based on the purpose of the project, data projects mainly include the following categories:
Most platform projects will target Hugging Face, focuses on integrating various resources in the AI industry. Establish a platform that aggregates links to various resources and roles such as data, computing power, models, AI developers, and blockchain to more conveniently solve various needs with the platform as the center. For example, Giza, focuses on building a comprehensive zkML operation platform, aims to make Machine learning inference becomes trustworthy and transparent because data and modelsBlack boxes arecurrently in AICommon problems can be solved by Web3using cryptography technologies such as ZK and FHE The reasoning of the verification model is indeed executed correctly, sooner or later will called upon by in the industry.
There are also layer1/layer2 for Focus AI, such as Nuroblocks, Janction, etc. The core narrative connects various computing power, data, models, AI developers, nodes and other resources, and helps Web3 AI applications to achieve rapid construction and development by packaging common components and common SDKs.
There are also platforms like Agent Network. Based on this type of platform, AI Agents can be built for various application scenarios, such asOlas, ChainML, etc.
Platform-type Web3 AI projects mainly use Token to capture the value of the platform and encourage all participants of the platform to build together. It is helpful for start-up projects from 0 to 1, and can reduce the difficulty for project parties to find partners such as computing power, data, AI developer communities, nodes, etc.
Most of the previous infrastructure projects use blockchain technology to solve AI problems Issues in the construction of industry infrastructure projects. Application layer projects are more about using AI to solve problems in Web3 applications.
For example, Vitalik mentioned two directions in the article, which I think is very meaningful.
First, AI serves as a Web3 participant.For example: In Web3 Games, AI can act as a game player, and it canquickly Understand the rules of the game, and complete the game tasks most efficiently;In DEX, AIHasplayed a role in arbitrage trading for many years;Predictionmarkets(Prediction market), AI Agent can widely accept a large amount of data, knowledge base and information to train the analysis and prediction capabilities of its model, And provide it to users as a product to help users make predictions of specific events through model reasoning, Such as sports events, presidential elections, etc..
The second is to create scalable decentralized private AI. Because many users are worried about the black box problem of AI and the system is biased; or they are worried that some dApps use AI technology to deceive users to make profits. Essentially, this is because users do not have review and governance rights over the AI model training and inference process. But if you create a Web3 AI, like the Web3 project, the community has distributed governance rights for this AI, which may be more easily accepted.
As of now, there are no white horse projects with high ceilings in the Web3 AI application layer.
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