Causal Bandits Podcast
Causal AI, Modularity & Learning || Andrew Lawrence || Causal Bandits Ep. 002 (2023)
Nov 7, 2023
Andrew Lawrence, Director of Research at causaLens, shares his insights on the fascinating world of causality and modularity in AI. He discusses his journey from academia to industry, the role of Bayesian non-parametrics in understanding causal relationships, and the importance of collaboration in causal discovery. Andrew highlights the challenges of applying generative AI in high-stakes scenarios and underscores how teamwork is vital in translating research into practical applications. He also reflects on his supportive childhood and the significance of mentorship in tech.
01:25:07
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Quick takeaways
- Dr. Andrew Lawrence emphasizes the significance of Bayesian non-parametrics in developing accurate causal models that reflect real-world data dynamics.
- Causalence adopts a modular approach, allowing data scientists to seamlessly integrate diverse tools and customize their workflows for enhanced innovation.
Deep dives
The Journey into Causality
Dr. Andrew Lawrence's path to understanding causality began after his PhD in Bayesian non-parametrics, which provided a solid foundation for tackling causality concepts. His shift from academia to industry occurred after a realization that practical applications excited him more than theoretical research. Working at Causalence, he started learning about causal models in a professional setting, where he has spent the last four years developing expertise in this area. His skill set, blending theoretical knowledge with real-world data applications, has enabled him to approach complex problems effectively.
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