On May 7th, Bithumb added Korean won trading pairs for two AI projects, AIOZ and NEAR. NEAR, being a well-established L1 protocol, needs no introduction. AIOZ Network, on the other hand, might be less familiar. Formerly focused on storage and streaming, AIOZ Network is now leveraging its accumulated advantages to move towards AI-as-a-Service gradually and shared computing power. Recently, it released the whitepaper for its decentralized AI project, W3AI.

As the AI landscape becomes more crowded, what new strategies can established projects offer to secure a position in a market where liquidity and attention are both scarce?

Due to the complexity of the whitepaper, TechFlow conducted a thorough analysis to help readers quickly understand the technical features and implementation of the AIOZ W3AI project.

Amidst the Aave, AIOZ’s Opportunity to Enter the AI ​​Market

Although not a new project, AIOZ’s transition to AI seems like a natural progression. Previously, AIOZ Network operated as a Layer-1 network with interoperability between Ethereum and Cosmos. It utilized the AIOZ DePIN, driven by over 120,000 global nodes, to provide computing resources. This infrastructure supports AI processing speed, rapid iteration, scalability, and network security, serving as a crucial resource for the project’s narrative transition.

Moreover, the development of AI faces challenges with centralized cloud computing solutions struggling to handle large datasets, leading to scalability limitations and high costs. Additionally, data privacy and security concerns arise when data control rests with centralized providers rather than users.

Furthermore, the high barriers to accessing top-tier AI resources limit the participation of many small businesses and individuals, hindering innovation. Edge computing offers a solution by providing near-end services for data sources. Applications initiate from the edge, resulting in faster network service responses. Since data is processed locally at nodes, there is no need for long-distance transmission to central servers, naturally reducing the risk of data leakage. With AIOZ DePIN’s globally distributed edge computing nodes, AIOZ gains the confidence to make a large-scale entry into the AI domain.

Current node data operated by AIOZ Network.

W3AI: “Dual-layer architecture” of DePIN+AI as a service

As AIOZ ventures into the AI arena, a key move is the introduction of W3AI—a dual-layer architecture encompassing infrastructure and applications.

The dual-layer architecture is central to the AIOZ W3AI project, offering an innovative approach to address fundamental issues in AI computing such as scalability, cost efficiency, and user privacy protection.

This architectural design divides the operation of the entire network into two main layers: the infrastructure layer (W3AI Infrastructure) and the application layer (W3AI Application). Each layer has its unique functions and roles, collectively supporting the efficient operation of the entire network.

The infrastructure layer (W3AI Infrastructure) serves as the network’s foundation.

1.AIOZ DePIN’s artificial nodes all over the world

The foundation of AIOZ W3AI lies in its huge distributed artificial edge computing nodes, which contribute its computing resources including storage, CPU and GPU globally to form a decentralized power source. Multigraph topology ensures efficient communication lines between AIOZ DePIN, thereby minimizing communication costs and increasing processing speed. These nodes work together through distributed computing methods to jointly train and execute AI models. In this way, the AIOZ W3AI platform effectively utilizes distributed computing resources to reduce costs and increase efficiency for AI applications and enhance data privacy protection. This decentralized approach greatly reduces the risk of server bottlenecks and enhances user privacy by eliminating a single point of control.

The decentralized computing infrastructure of W3AI, driven by the AIOZ node network.

The purple area indicates the distribution of storage nodes, while the blue area represents the distribution of computing nodes.

2.Data processing and storage

Through AIOZ W3S, data is securely stored across multiple globally dispersed nodes, enhancing data security while improving data processing response times.

Distributed file systems like AIOZ IPFS and crypto technologies protect data stored on nodes, preventing unauthorized access and data breaches.

Flexible application layer (W3AI Application)

1.The Web 3 AI platform offers AI as a Service (AIaaS).

In simple terms, AI as a Service is a model where AI technology is provided to users as an online service, allowing businesses or individuals to enjoy the convenience of AI technology without the need for expensive investments.

Imagine an e-commerce merchant wanting to understand user purchase history and analyze consumer behavior to provide personalized shopping recommendations. AI as a Service can be used to collect and analyze user data, generating corresponding sales strategies. This is an example of AI as a Service applied in e-commerce.

In terms of product form, W3AI offers a simplified AI training workflow and intuitive UI/UX, providing users with interfaces and APIs for easy access to W3AI services, development, and deployment of AI models, among other functionalities. This layer design focuses on user experience and service accessibility. Additionally, the platform integrates various AI as a Service offerings, including machine learning, deep learning, and neural networks, allowing users to choose different services and tools according to their needs.

2.Model Training and Inference

The W3AI platform supports model training and inference in a decentralized environment. W3AI training (AIOZ W3AI Infrastructure) utilizes techniques such as Decentralized Federated Learning such as homomorphic encryption to enable collaboration among numerous edge computing nodes (DePINs) without the need to share their own data, improving model training performance while also ensuring data privacy. Trained models are deployed on edge AIOZ DePINs, bringing AI closer to the data source. W3AI inference supported by W3S technology (AIOZ W3S Infrastructure) allows users to upload their own datasets for model training or use existing models on the platform for data analysis and prediction.

3.Decentralized W3AI Marketplace and Incentive Mechanisms

The application layer also provides users with decentralized markets such as the AIOZ AI dApp Store and AI Model & Dataset Marketplace. Individuals and organizations can freely contribute, sell artificial intelligence datasets and models, build and deploy innovative AI applications, and convert their contributions into token rewards.

AIOZ W3AI’s two-layer architecture

“AI-powered routing” that Navigates through the “Dual-layer Architecture”

While the architecture is well-structured, managing the logical resources and task data between the operation of the dual-layer architecture is essential. Therefore, W3AI introduces AI-powered routing into the dual-layer architecture to dynamically optimize each task, ensuring higher overall system efficiency.

At the infrastructure layer, AI-powered routing assesses computational demands and the current workload of nodes, dynamically allocating tasks to ensure each node can participate in suitable tasks based on its capabilities and real-time network conditions. It also monitors the health status of nodes, promptly identifying and addressing potential node failures or performance bottlenecks to avoid single-point failures affecting overall efficiency.

At the application layer, intelligent routing enables quick response to user requests, dynamically adjusting data flow and processing strategies in real-time. It can also intelligently allocate the most suitable nodes to users based on their specific geographical locations and requirements. Faced with large-scale high-concurrency tasks, the AI routing architecture intelligently schedules and optimizes tasks to support the application layer in handling complex AI models and big data analysis.

The whitepaper also references numerous complex formula calculations to demonstrate the specific routing implementation. Interested readers can refer to the whitepaper document for more details.

AI-powered Routing determines the transmission path for task allocation among AIOZ DePIN nodes. Green indicates nodes with connections, while blue represents parts skipped due to low confidence.

Workflow: Example of AI Task Execution

With this rich infrastructure, how does W3AI unfold its workflow? From data input to result output, W3AI’s workflow embodies a complete decentralized operation mode: encrypted output → task decomposition and allocation → execution of computing tasks and storage → collection of completed computations in containers → users obtain decrypted output results.

We can refine the above process into simple steps:

1. Firstly, before entering the platform, user-uploaded data is homomorphically encrypted to ensure data security throughout the processing - Data input and encryption;
2. The encrypted data is then split into multiple segments based on task requirements, with each task assigned to the most suitable node for execution - Task decomposition and allocation;
3. Selected nodes execute specific computing tasks, such as AI model training or data analysis, while also responsible for related data storage - Computing and storage execution;
4. Upon task completion, results are re-encrypted and stored in transformed containers, awaiting retrieval by end-users - Result collection and encryption;
5. Only authorized users can access the final results, with results decrypted homomorphically before outputting - Result decryption and output.

Workflow architecture of W3AI

Through this process, W3AI enhances processing efficiency while balancing flexible, scalable characteristics and data security and privacy. It optimizes system resource utilization, reduces manual intervention, and lowers operational costs.

Token Economy Surrounding the Entire Ecosystem

$AIOZ is a crucial element in the entire AIOZ W3AI ecosystem. With the emergence of AI as a Service and shared computing power, its token has gained more use cases and value capture.

Data Trading and Contribution Incentives

$AIOZ is used to reward users who provide computing power and storage resources, ensuring the stable operation of the network. In the platform’s trading market, users can use $AIOZ to purchase various AI-as-a-service offerings or trade AI models and datasets. Additionally, token holders can participate in network governance by voting to decide the ecosystem’s next steps.

Sustaining Ecosystem Operations

A portion of the transaction fees paid in $AIZO is allocated to AIOZ network operations and financial management, ensuring the platform’s continuous maintenance and development. Another portion is burned directly, helping to regulate token supply and mitigate inflation. This carefully designed token circulation cycle incentivizes innovation and rewards participation, and drives the continuous development of the AIOZ W3AI ecosystem.

Token Flow Within the W3AI Ecosystem

Conclusion

As a decentralized project revolutionizing AI, AIOZ W3AI boasts inherent advantages in technical resources and operational mechanisms. W3AI has exhibited significant potential in technology and concepts, promising users safer, more flexible, and more efficient computing services along with intriguing ecological experiences. Nevertheless, it is important to acknowledge that W3AI also confronts challenges such as the market’s incomplete recognition and trust in centralized AI solutions, and the potential high operational costs under the system’s high-standard operation mode.

The current whitepaper resembles more of a blueprint drafted in the project’s early stages, setting the groundwork for the future but yet to be fully implemented and executed. Its usability and any potential safety or technical issues remain untested by the market.

Nonetheless, adapting to the narrative and actively evolving remains a prudent approach for Web3 projects amidst the high relevance of the business landscape, where both new and established projects are involved in an AI saga. Time will naturally reveal whether the crypto users on the stage can justify their worth.

Statement:

1. This article originally titled “AIOZ W3AI Explained: Shared Computing Power and AI-as-a-Service “Dual-Layer Architecture,” What New Gameplay Will Narrative Transition Bring?” is reproduced from [techflow]. All copyrights belong to the original author [深潮 TechFlow ]. If you have any objection to the reprint, please contact the Gate Learn team, the team will handle it as soon as possible.

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