Project Goal

To design and develop a decentralized application (dApp) that fosters the creation, deployment, and collaborative improvement of self-teaching AI agents, rewarding users with cryptocurrency for their contributions to the platform's growth and the AI agents' learning processes. This aims to build a community-driven AI ecosystem that is transparent, accessible, and incentivizes collective intelligence.

Problem

Current AI development is often centralized, with data and models held by large corporations. This can lead to limited transparency, potential biases, and restricted access. Furthermore, there's a lack of direct incentivization for individuals to contribute their data, expertise, or computational resources to the development and improvement of AI models in a decentralized manner.

Process

01: research and ideation

02: user flows

03: wireframes

04: conceptual designs

05: final designs

06: implementation and testing

Solution

Designing a dApp with the core functionalities and reward mechanisms. Decentralized methods for verifying the performance and reliability of AI agents, potentially involving community-based evaluation and staking mechanisms. Implementing a staking mechanism where users can stake tokens to participate in the platform's governance, influencing future development and resource allocation.

Responsibilities

As the lead designer for this project, I was responsible for conducting user research to understand the needs of AI developers, data scientists, and enthusiasts; designing the user interface and user experience for all aspects of the platform, including agent creation, marketplace navigation, data contribution, reward tracking, and governance features; creating wireframes, prototypes, and visual designs that are both intuitive and engaging; collaborating closely with blockchain developers and AI researchers to ensure technical feasibility and alignment with the project's goals.

Results

01: Self-Teaching AI Agent Framework; Mechanisms for users to securely contribute data to train and improve the AI agents.

02: Decentralized Data Sharing

03: Verification & Validation Mechanisms