Interview
summary
Claims that large-data prediction and ranking problems are largely solved by current machine learning stacks and cites machine learning subfields like natural language processing and computer vision as the working tools, while identifying sparse-data settings and the need for semi-supervised or unsupervised methods as the real technical gaps. Focuses on applying these techniques outside Silicon Valley to business domains that lack large labeled datasets, and reports that practical deployments succeed only when data scale or clever transfer learning fills the gap.
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