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Takeaways:

• Causal assumptions are crucial for statistical modeling.
• Deep learning can be integrated with causal models.
• Statistical rigor is essential in evaluating LLMs.
• Causal representation learning is a growing field.
• Inductive biases in AI should match key mechanisms.
• Causal AI can improve decision-making processes.
• The future of AI lies in understanding causal relationships.

Chapters:

00:00 Introduction to Causal AI and Its Importance

16:34 The Journey to Writing Causal AI

28:05 Integrating Graphical Causality with Deep Learning

40:10 The Evolution of Probabilistic Machine Learning

44:34 Practical Applications of Causal AI with LLMs

49:48 Exploring Multimodal Models and Causality

56:15 Tools and Frameworks for Causal AI

01:03:19 Statistical Rigor in Evaluating LLMs

01:12:22 Causal Thinking in Real-World Deployments

01:19:52 Trade-offs in Generative Causal Models

01:25:14 Future of Causal Generative Modeling

Thank you to my Patrons for making this episode possible!

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