Decentralized Governance to Optimize Human Output Datasets for AI Learning
DOI:
https://doi.org/10.51483/IJAIML.4.2.2024.52-66Keywords:
Artificial intelligence, Large language models, Dataset, Micro task work, Gamification, Quality controls, Decentralized autonomous organization, Crypto currencies, Feedback effects, Token models, Tokens, Blockchain, Distributed ledger technology, Code assurancesAbstract
The evolution of AI depends on upgradable quality datasets. Data is the
foundation on which AI algorithms learn and make predictions. High-quality,
diverse, and labeled datasets are crucial for training AI models effectively. The
availability of quality data plays a significant role in determining the success
and impact of AI in disrupted industries. The AI Learning Ecosystem (ALE)
facilitates a micro task ecosystem for AI learning. ALE uses its proven and
tested decentralized governance ecosystem to provide high-quality diverse
datasets for AI learning via gamified micro-task work. Through its testing
environment in the industry-leading Code Review DAO (CRDAO), ALE
distinguishes itself from competitors through unparalleled decentralized
governance optimization that minimizes micro-task work duplication in
centralized systems and allows gamified micro-task work to scale high-quality
diverse datasets for AI learning.
