I would love your thoughts on this: Data-centric AI is still an emerging field, but what do you think are the key pillars of data-centric AI? E.g., if you were reading a textbook on this nascent field, what are some major topics you’d like the book to include?
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1. AI before the rise of ML, based on rules rather than learning (which is no longer called "AI", for the most part) 2. Current DL research in academia, which is model-centric & architecture-centric, assuming the dataset to be given and immutable
I assume Andrew opposes "data-centric AI" to the trends of 2).
