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Busting the Myth: AI That Needs Perfect Data

In this episode Matt is joined by Gao, a data and AI executive, and Jay to debunk the myth that organizations need perfect data before adopting AI. Throughout the discussion, the team explores why organizations hesitate to start AI projects due to data quality concerns and how they can overcome these fears. They delve into the concept of what constitutes 'perfect data,' the role of dynamic and generative models, and practical examples from the medical and insurance industries. They also discuss tools and methods, like auto-encoders, to handle imperfect data effectively. This session aims to provide a comprehensive understanding of data quality in AI and offers strategies to leverage existing data for successful AI integration. 00:59 Busting the Myth: AI Needs Perfect Data 02:04 Defining Perfect Data 03:33 Challenges with Data Quality in AI Projects 07:28 Real-World Examples and Data Challenges 22:01 Tooling and Approaches for Imperfect Data 34:05 Generative AI and Synthetic Data