Web3-AI Track Overview: Technical Logic, Application Scenarios, and Analysis of Top Projects

Web3-AI Track Panorama Report: Technical Logic, Scenario Applications, and In-Depth Analysis of Top Projects

With the continuous rise of AI narratives, increasing attention is focused on this track. In-depth analysis of the technical logic, application scenarios, and representative projects of the Web3-AI track has been conducted, comprehensively presenting the panorama and development trends of this field.

1. Web3-AI: Analysis of Technical Logic and Emerging Market Opportunities

1.1 The Fusion Logic of Web3 and AI: How to Define the Web-AI Track

In the past year, AI narratives have been exceptionally popular in the Web3 industry, with AI projects emerging like mushrooms after rain. Although many projects involve AI technology, some projects only use AI in certain parts of their products, and the underlying token economics have no substantial connection to AI products. Therefore, such projects are not included in the discussion of Web3-AI projects in this article.

The focus of this article is on projects that use blockchain to solve production relationship issues and AI to address productivity problems. These projects themselves provide AI products while serving as tools for production relationships based on the Web3 economic model, complementing each other. We classify these types of projects as the Web3-AI track. In order for readers to better understand the Web3-AI track, we will elaborate on the development process and challenges of AI, as well as how the combination of Web3 and AI perfectly solves problems and creates new application scenarios.

1.2 The development process and challenges of AI: from data collection to model inference

AI technology is a technology that enables computers to simulate, extend, and enhance human intelligence. It allows computers to perform various complex tasks, from language translation and image classification to facial recognition and autonomous driving applications. AI is changing the way we live and work.

The process of developing artificial intelligence models typically includes the following key steps: data collection and data preprocessing, model selection and tuning, model training and inference. To give a simple example, to develop a model for classifying images of cats and dogs, you need to:

1. Data collection and data preprocessing: Collect an image dataset containing cats and dogs, which can be done using public datasets or by collecting real data yourself. Then label each image with a category (cat or dog), ensuring the labels are accurate. Convert the images into a format that the model can recognize, and divide the dataset into training, validation, and test sets.

2. Model Selection and Tuning: Choose an appropriate model, such as Convolutional Neural Networks (CNN), which are well-suited for image classification tasks. Tune the model parameters or architecture according to different requirements. Generally speaking, the network depth of the model can be adjusted based on the complexity of the AI task. In this simple classification example, a shallower network depth may be sufficient.

3. Model Training: Models can be trained using GPU, TPU, or high-performance computing clusters. The training time is affected by the complexity of the model and the computing power.

4. Model Inference: The file of a trained model is usually referred to as model weights. The inference process refers to the use of the already trained model to predict or classify new data. In this process, a test set or new data can be used to evaluate the classification performance of the model, typically using metrics such as accuracy, recall, and F1-score to assess the effectiveness of the model.

As shown in the figure, after data collection and preprocessing, model selection and tuning, and training, the trained model infers the predicted values P (probability) of cats and dogs on the test set, which is the probability that the model infers it is a cat or a dog.

The trained AI model can be further integrated into various applications to perform different tasks. In this example, the cat and dog classification AI model can be integrated into a mobile application, where users upload pictures of cats or dogs to receive classification results.

However, the centralized AI development process has some issues in the following scenarios:

User Privacy: In centralized scenarios, the development process of AI is often opaque. User data may be stolen and used for AI training without their knowledge.

Data source acquisition: Small teams or individuals may face limitations on non-open source data when acquiring data in specific fields (e.g., medical data).

Model selection and tuning: It is challenging for small teams to access domain-specific model resources or incur significant costs for model tuning.

Obtaining computing power: For individual developers and small teams, the high costs of purchasing GPUs and renting cloud computing power can pose a significant economic burden.

AI Asset Income: Data annotators often struggle to earn an income that matches their contributions, while AI developers find it difficult to match their research results with buyers in need.

The challenges existing in centralized AI scenarios can be addressed by integrating with Web3, which, as a new type of production relationship, naturally adapts to AI that represents new productive forces, thereby promoting simultaneous progress in technology and production capacity.

1.3 The Synergistic Effects of Web3 and AI: Role Transformation and Innovative Applications

The combination of Web3 and AI can enhance user sovereignty, providing users with an open AI collaboration platform, transforming users from AI users in the Web2 era into participants, creating AI that can be owned by everyone. At the same time, the integration of the Web3 world and AI technology can also spark more innovative application scenarios and gameplay.

Based on Web3 technology, the development and application of AI will usher in a brand new collaborative economic system. People's data privacy can be guaranteed, the data crowdsourcing model promotes the advancement of AI models, and numerous open-source AI resources are available for users. Shared computing power can be obtained at a lower cost. With the help of a decentralized collaborative crowdsourcing mechanism and an open AI market, a fair income distribution system can be realized, thereby encouraging more people to drive the advancement of AI technology.

In the Web3 scenario, AI can have a positive impact across multiple tracks. For example, AI models can be integrated into smart contracts to enhance work efficiency in various application scenarios, such as market analysis, security detection, social clustering, and many other functions. Generative AI not only allows users to experience the "artist" role, such as creating their own NFTs using AI technology, but also creates rich and diverse game scenarios and interesting interactive experiences in GameFi. A rich infrastructure provides a smooth development experience, allowing both AI experts and newcomers wanting to enter the AI field to find suitable entry points in this world.

2. Interpretation of the Web3-AI Ecosystem Project Map and Architecture

We mainly studied 41 projects in the Web3-AI track and categorized them into different tiers. The logic for dividing each tier is shown in the figure below, which includes the infrastructure layer, intermediate layer, and application layer, with each layer further divided into different sections. In the next chapter, we will conduct a depth analysis of some representative projects.

The infrastructure layer encompasses the computing resources and technological architecture that support the entire AI lifecycle, the middle layer includes data management, model development, and verification inference services that connect the infrastructure with applications, while the application layer focuses on various applications and solutions directly aimed at users.

Infrastructure Layer:

The infrastructure layer is the foundation of the AI lifecycle, and this article classifies computing power, AI Chain, and development platforms as part of the infrastructure layer. It is the support of these infrastructures that enables the training and inference of AI models, presenting powerful and practical AI applications to users.

• Decentralized Computing Network: It can provide distributed computing power for AI model training, ensuring efficient and economical utilization of computing resources. Some projects offer decentralized computing power markets, where users can rent computing power at low costs or share computing power to earn profits, represented by projects such as IO.NET and Hyperbolic. In addition, some projects have derived new gameplay, such as Compute Labs, which proposed a tokenization protocol, allowing users to participate in computing power leasing in various ways by purchasing NFTs that represent physical GPUs.

• AI Chain: Utilizing blockchain as the foundation for the AI lifecycle, achieving seamless interaction of on-chain and off-chain AI resources, and promoting the development of the industry ecosystem. The decentralized AI market on the chain can trade AI assets such as data, models, agents, etc., and provide AI development frameworks and supporting development tools, represented by projects like Sahara AI. AI Chain can also promote technological advancements in AI across different fields, such as Bittensor's innovative subnet incentive mechanism to encourage competition among different types of AI subnets.

• Development Platforms: Some projects offer AI agent development platforms, which can also facilitate the trading of AI agents, such as Fetch.ai and ChainML. One-stop tools help developers more conveniently create, train, and deploy AI models, with representative projects like Nimble. This infrastructure promotes the widespread application of AI technology in the Web3 ecosystem.

Middle Layer:

This layer involves AI data, models, as well as inference and verification, and using Web3 technology can achieve higher work efficiency.

• Data: The quality and quantity of data are key factors affecting the effectiveness of model training. In the Web3 world, data can be optimized and costs reduced through crowdsourced data and collaborative data processing. Users can have autonomy over their data and sell their own data under privacy protection, avoiding theft and high profits by unscrupulous merchants. For data demanders, these platforms offer a wide range of options at extremely low costs. Representative projects like Grass utilize user bandwidth to scrape web data, while xData collects media information through user-friendly plugins and supports users in uploading tweet information.

In addition, some platforms allow domain experts or ordinary users to perform data preprocessing tasks, such as image labeling and data classification, which may require specialized knowledge for financial and legal data processing. Users can tokenize their skills to achieve collaborative crowdsourcing for data preprocessing. For example, the AI marketplace like Sahara AI provides data tasks across different domains, covering multi-domain data scenarios; while AIT Protocol labels data through human-machine collaboration.

• Model: In the AI development process mentioned earlier, different types of requirements need to match suitable models. Common models used for image tasks include CNN and GAN, while for object detection tasks, the Yolo series can be chosen. For text-related tasks, common models include RNN and Transformer, as well as some specific or general large models. The depth of the models required for tasks of different complexities also varies, and sometimes model tuning is necessary.

Some projects support users to provide different types of models or collaborate on model training through crowdsourcing, such as Sentient, which allows users to place trusted model data in the storage layer and distribution layer for model optimization through a modular design. The development tools provided by Sahara AI come with advanced AI algorithms and computing frameworks, and have collaborative training capabilities.

• Inference and Verification: After a model is trained, it generates a model weight file that can be used for direct classification, prediction, or other specific tasks, a process known as inference. The inference process is usually accompanied by verification mechanisms to validate whether the inference model's source is correct and whether there are any malicious behaviors. In Web3, inference can typically be integrated into smart contracts, allowing inference to be performed by calling the model. Common verification methods include technologies like ZKML, OPML, and TEE. Representative projects such as the ORA on-chain AI oracle (OAO) have introduced OPML as a verifiable layer for AI oracles, and their official website also mentions their research on ZKML and opp/ai (ZKML combined with OPML).

Application Layer:

This layer is primarily a user-facing application that combines AI with Web3 to create more interesting and innovative gameplay. This article mainly outlines projects in several areas, including AIGC (AI Generated Content), AI agents, and data analysis.

• AIGC: It can be expanded to through AIGC