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BITESIZE | Practical Applications of Causal AI with LLMs, with Robert Ness
Manage episode 497415128 series 2827094
A tartalmat a Alexandre Andorra biztosítja. Az összes podcast-tartalmat, beleértve az epizódokat, grafikákat és podcast-leírásokat, közvetlenül a Alexandre Andorra vagy a podcast platform partnere tölti fel és biztosítja. Ha úgy gondolja, hogy valaki az Ön engedélye nélkül használja fel a szerzői joggal védett művét, kövesse az itt leírt folyamatot https://hu.player.fm/legal.
Today’s clip is from episode 137 of the podcast, with Robert Ness.
Alex and Robert discuss the intersection of causal inference and deep learning, emphasizing the importance of understanding causal concepts in statistical modeling.
The discussion also covers the evolution of probabilistic machine learning, the role of inductive biases, and the potential of large language models in causal analysis, highlighting their ability to translate natural language into formal causal queries.
Get the full conversation here.
Attend Alex's tutorial at PyData Berlin: A Beginner's Guide to State Space Modeling
- Intro to Bayes Course (first 2 lessons free)
- Advanced Regression Course (first 2 lessons free)
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Transcript
This is an automatic transcript and may therefore contain errors. Please get in touch if you're willing to correct them.
171 epizódok
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