912: In Case You Missed It in July 2025
Super Data Science: ML & AI Podcast with Jon Krohn
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Shifting Paradigms in Machine Learning
This chapter explores the transition from traditional to data-centric machine learning, highlighting the importance of dataset quality over model iteration. It covers challenges in benchmarking and evaluations, particularly in the context of large language models, and the implications of human judgment in assessing AI outputs. The discussion emphasizes tailored approaches in sectors like finance, advocating for rigorous evaluation methods to ensure the safety and reliability of AI systems.
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