Web3-AI Panorama Report: In-depth Analysis of Technological Integration, Application Scenarios, and Top Projects Depth

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

With the continuous rise of AI narrative, more and more attention is focused on this track. This article conducts an in-depth analysis of the technical logic, application scenarios, and representative projects in the Web3-AI track, presenting a comprehensive panorama and development trends in this field.

1. Web3-AI: Analyzing 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 only use AI in certain parts of their products, and the underlying token economics have no substantial connection to the 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 issues related to production relations and AI to address productivity problems. These projects themselves provide AI products while leveraging the Web3 economic model as a tool for production relations, with both aspects complementing each other. We categorize these projects within the Web3-AI track. To help readers better understand the Web3-AI track, this article 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 technique that allows computers to simulate, extend, and enhance human intelligence. It enables 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 involves the following key steps: data collection and data preprocessing, model selection and tuning, model training and inference. For 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 publicly available datasets or by collecting real data yourself. Then label each image with its category (cat or dog), ensuring the labels are accurate. Convert the images into a format that the model can recognize, and split the dataset into training set, validation set, and test set.

2. Model Selection and Tuning: Choose the 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, the model's network depth 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: You can use GPUs, TPUs, or high-performance computing clusters to train the model, and the training time is affected by the model complexity and computing power.

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

As shown in the figure, after data collection and preprocessing, model selection and tuning, and training, performing inference on the test set with the trained model will yield the predicted values P (probability) for cats and dogs, which indicates the model's inferred probability of being a cat or a dog.

The trained AI model can further be 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 in obtaining specific domain data (such as medical data) due to data not being open source.

Model selection and tuning: For small teams, it is difficult to obtain model resources for specific domains or spend a large amount of cost on model tuning.

Power Acquisition: For individual developers and small teams, the high costs of GPU purchases and cloud computing power leasing can pose a significant financial burden.

AI Asset Income: Data annotation workers often struggle to earn an income that matches their efforts, while AI developers' research outcomes also find it difficult to match with buyers in need.

The challenges present 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 a new form of productive force, thus promoting simultaneous progress in technology and productive 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 that transforms users from AI consumers 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 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, and the data crowdsourcing model promotes the advancement of AI models. Numerous open-source AI resources are available for users, and 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 progress of AI technology.

In the Web3 context, 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 applications, such as market analysis, security detection, social clustering, and more. Generative AI not only allows users to experience the "artist" role, such as creating their own NFTs using AI technology, but also creates diverse gaming scenarios and interesting interactive experiences in GameFi. Rich infrastructure provides a smooth development experience, allowing both AI experts and newcomers wanting to enter the AI field to find a suitable entry point in this world.

2. Interpretation of Web3-AI Ecosystem Project Layout and Architecture

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

The infrastructure layer encompasses the computing resources and technical architecture that support the entire AI lifecycle, the middle layer includes data management, model development, and verification reasoning services that connect the infrastructure and 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. 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 cost-effective 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 propose a tokenization protocol where users can participate in computing power leasing in various ways by purchasing NFTs that represent physical GPUs.

• AI Chain: Utilizes blockchain as the foundation for the AI lifecycle, achieving seamless interaction of AI resources on-chain and off-chain, 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 provides AI development frameworks and supporting development tools, with representative projects like Sahara AI. AI Chain can also promote technological advancement of AI in different fields, such as Bittensor, which facilitates competition among different types of AI subnets through an innovative subnet incentive mechanism.

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

Middleware:

This layer involves AI data, models, as well as reasoning 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, crowdsourced data and collaborative data processing can optimize resource utilization and reduce data costs. Users can have autonomy over their data and sell it under privacy protection to avoid having their data stolen and exploited for high profits by malicious merchants. For data demanders, these platforms offer a wide range of choices and very 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. These tasks may require specialized knowledge for financial and legal data processing. Users can tokenize their skills to enable collaborative crowdsourcing of data preprocessing. Examples include AI markets like Sahara AI, which have data tasks from various fields and can cover multi-domain data scenarios; while AIT Protocol labels data through human-machine collaboration.

• Model: As mentioned earlier in the AI development process, different types of requirements need to match suitable models. Common models used for image tasks include CNN and GAN, while Yolo series can be chosen for object detection tasks. For text-related tasks, common models include RNN and Transformer, and of course, there are also some specific or general large models. The depth of the models required varies with the complexity of the tasks, 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 modular design. The development tools provided by Sahara AI come with advanced AI algorithms and computing frameworks and have the capability for collaborative training.

• Inference and Verification: After the model is trained, it generates model weight files that can be used for direct classification, prediction, or other specific tasks, a process known as inference. The inference process is usually accompanied by a verification mechanism to validate the source of the inference model, checking for correctness and malicious behavior, among other things. Inference in Web3 can typically be integrated into smart contracts, allowing for inference by calling the model. Common verification methods include technologies such as ZKML, OPML, and TEE. Representative projects like the ORA chain AI oracle (OAO) have introduced OPML as a verifiable layer for AI oracles. The ORA official website also mentions their research on ZKML and opp/ai (ZKML combined with OPML).

Application Layer:

This layer is mainly user-facing applications that combine AI with Web3 to create more interesting and innovative gameplay. This article primarily organizes projects in several areas including AIGC (AI-generated content), AI agents, and data analysis.

• AIGC: Through AIGC, it can be extended to NFT, games, and other tracks in Web3, where users can directly generate text, images, and audio through Prompts (the hints provided by users), and even create customized gameplay in games based on their preferences. NFT projects like NFPrompt allow users to generate NFTs through AI for trading in the market; games like Sleepless enable users to shape the personality of virtual companions through dialogue to match their preferences.

• AI Agent: Refers to an artificial intelligence system capable of autonomously executing tasks and making decisions. AI agents typically possess the abilities of perception, reasoning, learning, and action, allowing them to perform complex tasks in various environments. Common AI agents include