ADVANCING DATA LINEAGE ACCURACY WITH GENERATIVE AI: NEW TECHNIQUES AND TOOLS
Keywords:
Generative AI, Data Lineage, AIAbstract
The popularity of open-source AI code and models is on the rise, particularly among smaller firms, research institutions, and individual users. This is so even if tech companies are progressively controlling and dominating the growing market for generative AI. Unfortunately, they are unable to make this data publicly available for training purposes due to limited computational resources and concerns about data protection. Unfortunately, they are often unable to disclose high-quality data that could be used for training purposes. One possible solution to these two issues could be to train generative AI using crowd-sourcing principles and federated learning techniques to build a distributed architecture that protects privacy. We address in this paper the ways in which these two important enablers, in conjunction with other new technologies, might be put together to form a community-driven ecosystem for generative AI. In this way, even minor players in the ecosystem will be able to safely provide training data for generative AI models. In addition to outlining future research objectives in AI moderation, the report also discusses relevant non-technical issues, such as community duty and intellectual property rights.
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Copyright (c) 2024 Ankush Reddy Sugureddy (Author)
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