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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Bjarne Stroustrup once observed to me that if you can’t explain what a thing is not, you don’t understand that thing. Please tell me, Andrew: what manner of AI is not data centric?
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
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I assume Andrew opposes "data-centric AI" to the trends of 2).
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Anything that works well enough to satisfy customers (instead of investors) is no longer called AI.
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