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

With the continued rise of AI narratives, more and more attention is being focused on this track. An in-depth analysis of the technical logic, application scenarios, and representative projects in the Web3-AI track has been conducted to comprehensively present the panorama and development trends in this field.

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

1.1 The Integration 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 are not substantially related 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 provide AI products while also utilizing Web3 economic models as tools for production relations, with both complementing each other. We categorize such projects as the Web3-AI track. In order to help readers better understand the Web3-AI track, we will introduce the development processes and challenges of AI, as well as how the combination of Web3 and AI can perfectly solve problems and create 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, expand, 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 usually includes the following key steps: data collection and data preprocessing, model selection and tuning, model training and inference. To give a simple example, if you want to develop a model to classify images of cats and dogs, you need to:

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

2. Model Selection and Tuning: Choose the appropriate model, such as Convolutional Neural Networks ( CNN ), which are more suitable for image classification tasks. Tune the model parameters or architecture based on different requirements; generally, the network depth of the model can be adjusted according to 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 GPUs, TPUs, or high-performance computing clusters, and the training time is influenced by the complexity of the model and the computing power.

4. Model Inference: The files of a well-trained model are usually referred to as model weights. The inference process refers to the procedure of using 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, F1-score, etc., to assess the effectiveness of the model.

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

Trained AI models 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 of non-open source data when acquiring data in specific fields such as medical data (.

Model selection and tuning: For small teams, it is difficult to obtain model resources specific to a certain field or spend a lot of costs on model tuning.

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

AI Asset Income: Data annotators often fail to receive income that matches their efforts, while the research results of AI developers are also difficult to match with buyers in demand.

The challenges existing in centralized AI scenarios can be addressed by integrating with Web3. As a new type of production relationship, Web3 is naturally compatible with AI, which represents a new productive force, thus promoting simultaneous progress in technology and production capacity.

) 1.3 The Synergistic Effect 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 users in the Web2 era into participants, creating AI that everyone can own. At the same time, the integration of the Web3 world with 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 protected, 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 acquired at a lower cost. With the help of decentralized collaborative crowdsourcing mechanisms 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 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 role of an "artist," such as creating their own NFTs using AI technology, but also creates rich and diverse gaming 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 levels. The classification logic for each level is shown in the diagram below, including the infrastructure layer, middle 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 covers the computing resources and technical architecture that support the operation of the entire AI lifecycle, while the middle layer includes data management, model development, and verification reasoning services that connect the infrastructure and applications. The application layer focuses on various applications and solutions that are directly aimed at users.

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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 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 gain profits, represented by projects like IO.NET and Hyperbolic. In addition, some projects have derived new gameplay, such as Compute Labs, which propose tokenization protocols, allowing users to participate in computing power leasing in different ways by purchasing NFTs that represent GPU entities to gain profits.

• AI Chain: Utilizing blockchain as the foundation for the AI lifecycle, enabling seamless interaction of AI resources on and off the chain, and promoting the development of industry ecosystems. The decentralized AI market on the chain allows for the trading of 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 facilitate advancements in AI technology across different fields, such as Bittensor promoting competition among different types of AI subnets through an innovative subnet incentive mechanism.

• Development Platforms: Some projects offer AI agent development platforms that also enable trading with AI agents, such as Fetch.ai and ChainML. One-stop tools help developers more easily create, train, and deploy AI models, represented by projects like Nimble. These infrastructures promote the widespread application of AI technology in the Web3 ecosystem.

Middleware:

This layer involves AI data, models, as well as inference and verification. Utilizing 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, resource utilization can be optimized and data costs reduced through crowdsourced data and collaborative data processing. Users can have autonomy over their data and sell it under privacy protection, avoiding theft and high profits by malicious merchants. For data demanders, these platforms provide a wide range of choices at 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 achieve collaborative crowdsourcing of data preprocessing. For example, the AI market represented by Sahara AI has data tasks from different fields, 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 for image tasks include CNN and GAN, while the Yolo series can be chosen for object detection tasks. For text-related tasks, common models include RNN and Transformer, as well as some specific or general large models. The depth of the model 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 in training models through crowdsourcing, such as Sentient, which allows users to place trusted model data in storage and distribution layers for model optimization through modular design. The development tools provided by Sahara AI are equipped 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 typically accompanied by a verification mechanism to validate whether the source of the inference model is correct and whether there are malicious behaviors, etc. In Web3, inference can usually be integrated into smart contracts, allowing inference to be performed by calling the model. Common verification methods include technologies such as ZKML, OPML, and TEE. Representative projects like the AI oracle on the ORA chain )OAO( have introduced OPML as a verifiable layer for the AI oracle. Their official website also mentions their research regarding the combination of ZKML and opp/ai)ZKML with OPML(.

Application Layer:

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

• AIGC: Through AIGC, it can be expanded to NFT, games, and other tracks in Web3. Users can directly generate text, images, and audio based on the prompt provided by the user ), and even create customized gameplay in games according to 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 artificial intelligence systems that can autonomously execute tasks and make decisions. AI agents typically have the capabilities of perception, reasoning, learning, and action, allowing them to perform complex tasks in various environments. Common AI agents include language translation, language learning,