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AI Trends 2023: Causality and the Impact on Large Language Models with Robert Osazuwa Ness - #616
The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)
Using the Intuition of Causal Invariance in the Machine Learning Loop
I would say it's not the first paper I've seen historically that's kind of using this causal invariance to enforce some kind of modularity on the parameters that you're that are being trained. But it's the first time I've seen it in terms of how fast the model train on the candidate graph adapts to out of distribution data. And you identified some additional papers around this idea of causal discovery and its advancement over the past year. A lot of that work seems to be happening at DeepMind and Mila and in Montreal.
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