😀Quickstart

What is Aos network?

AOS is a AI inference verification and sampling network for Hetu protocol on Eigenlayer. AOS can bring enhanced security to AI networks, while supporting more efficient and clean verification services for AI networks.

Actors

The AOS network has three roles: staker, operator, and dispatcher

Starkers

A staker is a user who has some funds in the form of LSTs or native ETH. When a staker interacts with AOS on EigenLayer, it does one of 4 things:

• deposit funds

• withdraw funds

• delegate funds to an operator

• undelegate funds from an operator

Dispatcher(AVSs)

An AOS-AVS is a combination of onchain and offchain code.It accepts operators who are interested in performing the AOS service and rewards them for this service AOS-AVS Registration with EigenLayer. it does one of 4 things:

• distribution task

• record task

• act as full node of Aos Netwrok

• record incentive

Operators

An operator is a user who:

• has some type of hardware and

• wants to use this hardware to make money

By registering with AOS on Dispatcher, the operator could service the AI inference verification task.The staker can delegate funds to an operator by Delegation Manager contract.

Services who accept operators require that the operator have some type and amount of bond which can be taken away from the operator in case the operator negatively affects the health of the service.

Use cases

AI-Powered Fraud Detection in Crypto Transactions

• Use Case Description: A blockchain platform employs multiple AI models to detect fraudulent activities in real-time. These models continuously learn from new data and use AI inferences to flag suspicious transactions. An incentive system rewards users who contribute computing power to train and verify the AI models, enhancing the security of the blockchain network.

2. Decentralized AI-Verified Content Moderation

• Use Case Description: To maintain community standards, a decentralized platform uses AI to moderate content. Multiple AI inferences are made to evaluate user-generated content against community guidelines. Users can challenge AI decisions and, upon successful verification of misclassification, earn rewards. This system ensures content remains community-driven while providing incentives for accurate AI moderation.

3. Blockchain-based AI Model Licensing Marketplace

• Use Case Description: AI developers publish their models on a blockchain-based marketplace where users can purchase access. To ensure the integrity of the models, an AI verification process confirms their accuracy and performance. Users who verify and validate the models through a Proof of Clock Sampling process receive tokens as incentives, creating a community-driven verification ecosystem.

4. AI-Enhanced Dynamic Pricing for Digital Goods

• Use Case Description: A digital marketplace uses AI to dynamically price NFTs and other digital goods. Multiple AI models analyze factors like rarity, demand, and market trends to set prices. Users who participate in the verification of these AI inferences through a decentralized process earn rewards, aligning their interests with the accuracy of the pricing algorithm.

5. Incentivized AI-Verified Prediction Markets

• Use Case Description: A prediction market platform leverages AI to forecast outcomes of real-world events. Users can create markets and use AI models to predict results. The platform employs multiple AI inferences to validate the predictions and uses a Proof of Clock Sampling mechanism to select which predictions to verify. Users who contribute to the verification process by challenging and validating predictions are incentivized, ensuring the accuracy and integrity of the platform's forecasts.