The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence) AI Trends 2023: Causality and the Impact on Large Language Models with Robert Osazuwa Ness - #616
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Feb 14, 2023 Robert Osazuwa Ness, a senior researcher at Microsoft and professor at Northeastern University, dives into exciting trends in causal modeling. He highlights advances in causal discovery and its implications for drug discovery and healthcare. The conversation delves into the significance of causality in large language models, exploring how these models can enhance reasoning about cause and effect. Ness also discusses innovative applications like SayCan, which transforms verbal commands into robotic actions, merging AI with practical tasks.
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Causal Discovery and Graph Size
- Causal discovery learns causal structures from data, like creating a directed acyclic graph (DAG).
- Optimizing for larger graphs in causal discovery may not be practical for downstream tasks.
Gaming Example
- Imagine a gaming company analyzing how guild membership, side quests, and in-game purchases relate.
- An intervention, like forcing side quest engagement, helps determine causal effects, but the target node isn't always known.
Predicting Intervention Targets
- Predicting intervention targets helps determine downstream effects in causal discovery.
- Matching predicted effects with observed data helps learn the underlying causal graph.