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Interview: New Book Outlines the Perils of Big (Meaningless) Data

Gary Smith, co-author with Jay Cordes of Phantom Patterns, shows why human wisdom and common sense are more important than ever now

Economist Gary Smith and statistician Jay Cordes have a new book out, The Phantom Pattern Problem: The mirage of big data, on why we should not trust Big Data over common sense. In their view, it’s a dangerous mix: Humans naturally assume that all patterns are significant. But AI cannot grasp the meaning of any pattern, significant or not. Thus, from massive number crunches, we may “learn” (if that’s the right word) that Stock prices can be predicted from Google searches for the word debt. Stock prices can be predicted from the number of Twitter tweets that use “calm” words. An unborn baby’s sex can be predicted by the amount of breakfast cereal the mother eats. Bitcoin prices can be…

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Quantity vs Quality: Can AI Help Scientists Produce Better Papers?

What happens when scientists simply can't read all their peers' papers and still find time for original research?
Quantity is definitely a solved problem. STM, the “voice of scholarly publishing” estimated in 2015 that roughly 2.5 million science papers are published each year. Some are, admittedly, in predatory or fake journals. But over 2800 journals are assumed to be genuine. Read More ›