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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)
A Differential Between Causal Discovery and Cognitive Learning in Machine Learning
In causal representation learning, disentanglement, disentangled representations, is often thinking about the high dimensional data that you have common in machine learning. While causal discovery, traditionally is you're thinking about tabular data. And so I think linking the two and just saying like this is that these are essentially similar things like you're trying to learn some latent variables that are latent causes and you're hoping to learn the causal structure between them. That's a kind of causal representation learning on ordinary tabular data or pixels.
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