The Unreasonable Effectiveness of Data
Halevy, Norvig, and Pereira argue that for language and web-scale problems, large real-world datasets plus simple scalable models often beat elegant small-data theories.
Halevy, Norvig, and Pereira argue that for language and web-scale problems, large real-world datasets plus simple scalable models often beat elegant small-data theories.
Sutton distills a recurring lesson from AI: in the long run, methods that scale with computation beat hand-coded human knowledge.