Today's guest, Dr. Robert Osazuwa Ness, wrote the popular new book "Causal A.I." so enjoy this episode on what Causal A.I. is and what advantages it has over "normal" (correlation-based) models.
Robert:
• Senior Researcher at "Microsoft Research A.I."
• His research focuses on statistical and causal inference techniques for controllable, human-aligned multimodal models.
• He is also founder of Altdeep.ai, where he teaches professionals advanced topics in machine learning.
• Holds a PhD in Statistics from Purdue University in Indiana.
Today’s episode will resonate most with hands-on practitioners like data scientists, statisticians and A.I. engineers.
In today’s episode, Robert details:
• The three-rung ladder of causation that determines what types of causal questions you can actually answer with your data.
• The surprising connections between Bayesian networks, graphical models and modern causal A.I.
• Why A.I. systems have been dominated by correlation-based learning and what's stopping them from adopting causal reasoning like humans and animals naturally do.
• How tools like PyTorch, Pyro, and DoWhy are revolutionizing causal inference by separating statistical complexity from causal assumptions.
• How large language models like GPT-4o can act as "causal knowledge bases" and outperform traditional causal methods in some scenarios.
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