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 Inference and Graph Structures
This chapter explores the complexities of causal inference and the impact of interventions on graph structures. It introduces a novel method for integrating unknown interventions into unsupervised training and discusses the challenges of identifying target nodes in networks. The conversation shifts focus to a supervised learning approach in causal discovery, assessing data biases and utilizing transformer architectures for improved predictions.
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