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

As AI narratives continue to gain traction, more and more attention is focused on this sector. An in-depth analysis of the technical logic, application scenarios, and representative projects of the Web3-AI sector has been conducted to provide you with a comprehensive overview of the landscape and development trends in 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 narrative has been exceptionally popular in the Web3 industry, with AI projects emerging like mushrooms after a 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 with 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 provide AI products while leveraging the Web3 economic model as a tool for production relationships, with both complementing each other. We categorize these projects as the Web3-AI track. In order to help readers 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 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 allows computers to simulate, extend, and enhance human intelligence. It enables computers to perform a variety of 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. 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 sourced from public datasets or by collecting real data yourself. Then annotate 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 divide the dataset into training, validation, and testing sets.

2. Model Selection and Tuning: Choose the appropriate model, such as Convolutional Neural Network ( CNN ), which is more suitable for image classification tasks. Tune model parameters or architecture based on different needs; 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: You can use GPU, TPU, or high-performance computing clusters to train the model. The training time is affected by the complexity of the model and the computing power.

4. Model Inference: The trained file of the model is usually referred to as model weights. The inference process refers to the process 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 test the classification performance of the model, which is usually evaluated 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 will infer the predicted values of cats and dogs on the test set, resulting in the prediction probability P(probability), which is the probability that the model infers it is a cat or a dog.

Trained AI models can further be integrated into various applications to perform different tasks. In this example, a cat and dog classification AI model can be integrated into a mobile application where users upload images 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 restrictions on 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 acquire model resources specific to a particular domain or to spend a large cost on model tuning.

Hashrate 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 struggle to earn an income that matches their efforts, and the research results of AI developers also have difficulty matching with buyers in need.

The challenges existing in centralized AI scenarios can be addressed by integrating with Web3. As a new type of productive relationship, Web3 is inherently compatible with AI, which represents a new productive force, thereby 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, transforming them from AI users in the Web2 era to participants, creating AI that is owned by everyone. At the same time, the integration of the Web3 world and AI technology can also generate 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 ensured, the data crowdsourcing model promotes the advancement of AI models, and a wealth of 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 incentivizing more people to promote 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 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 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 Ecological Project Landscape 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, 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.

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

Infrastructure Layer:

The infrastructure layer is the foundation of the AI lifecycle. This article categorizes 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, with representative projects such as IO.NET and Hyperbolic. In addition, some projects have developed new gameplay, such as Compute Labs, which proposed a tokenized protocol where users can participate in computing power leasing to earn profits in different ways by purchasing NFTs representing GPU entities.

• AI Chain: Utilizing blockchain as the foundation for the AI lifecycle to achieve seamless interaction between on-chain and off-chain AI resources, promoting the development of the industry ecosystem. The decentralized AI market on the chain allows for the trading of AI assets such as data, models, and agents, 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, which promotes competition among different types of AI subnets through innovative subnet incentive mechanisms.

• 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 reasoning and verification, and adopting 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, selling it under privacy protection to prevent it from being stolen by unscrupulous merchants for high profits. 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 annotation and data classification. These tasks may require specialized knowledge for financial and legal data processing. Users can tokenize their skills to achieve collaborative crowdsourcing for data preprocessing. For example, AI markets like Sahara AI have data tasks from different fields, covering multi-domain data scenarios; while AIT Protocol annotates 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. Common models for text-related tasks include RNN, Transformer, and there are also some specific or general large models. The depth of the models required for tasks of varying complexity also differs, and sometimes model tuning is necessary.

Some projects support users to provide different types of models or collaboratively train models 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 collaborative training capabilities.

• Inference and Validation: 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 validation mechanism to verify the correctness of the inference model's source, and to check for malicious behavior, etc. In Web3, inference can often be integrated into smart contracts, allowing for inference by calling the model, with common validation methods including technologies like ZKML, OPML, and TEE. Representative projects such as the AI oracle on the ORA chain )OAO( have introduced OPML as a verifiable layer for the AI oracle, and their official website also mentions their research on the combination of ZKML and opp/ai)ZKML with OPML(.

Application Layer:

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

• AIGC: With AIGC, it can be extended to NFT, games and other tracks in Web3. Users can directly generate text, images, and audio through the prompts given by Prompt). They can even create customized gameplay in games according to their preferences. NFT projects like NFPrompt allow users to generate NFTs with AI for trading in the market; games like Sleepless enable users to shape the personality of their virtual companions through dialogue to match their preferences.

• AI Agent: Refers to an artificial intelligence system that can autonomously execute tasks and make 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 language translation and language learning.