AI Trends 2023: Causality and the Impact on Large Language Models with Robert Osazuwa Ness - #616
The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)
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Causal Inductive Bias in Machine Learning
This chapter explores the emerging trend of causal inductive bias in machine learning, emphasizing the use of simulators and process models for causal discovery. It highlights various applications, such as climate modeling with the CLIMAX model, and discusses the integration of causal structures within neural networks to improve interpretability. The conversation also addresses the challenges and advancements in causal representation learning, particularly in high-dimensional settings and the identification of latent variables.
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