AI Learning - Decentralized Governance to Optimize Human Output Datasets for AI Learning
Wulf A. Kaal · 2024
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. Key Words: Artificial Intelligence, Large Language Models, Dataset, Micro Task Work, Gamification, Quality Controls, Decentralized Autonomous Organization, Token Models, Cryptocurrencies, Feedback Effects, Emerging Technology, Tokens, Blockchain, Distributed Ledger Technology, Code Assurances JEL Categories: K20, K23, K32, L43, L5, O31, O32 1The Author is grateful to many members of ALE and the open source community who provided valuable feedback. 1 Electronic copy available at: https://ssrn.com/abstract=4755632
Cite: Wulf A. Kaal, AI Learning - Decentralized Governance to Optimize Human Output Datasets for AI Learning (2024). SSRN: https://ssrn.com/abstract=4755632. https://doi.org/10.2139/ssrn.4755632
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