Mind Matters Natural and Artificial Intelligence News and Analysis

TagPhantom Pattern Problem (book)

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Basketball scoring basket at a sports arena

Steph Curry Got Red Hot and Torched the “Hot Hand Fallacy”

Many statisticians dismiss remarkable streaks like his as the Hot Hand Fallacy. Are they right?
Is “Hot Hands” really a fallacy, as some claim? Steph Curry is arguably the greatest shooter in National Basketball Association (NBA) history. In 2020, during practice the day after Christmas, Curry demolished that record by making 105 consecutive 3-point shots. Read More ›
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Digital binary code matrix background in graphic concept

Smith and Cordes’ Phantom Pattern Problem A Top 2020 Book

Published by Oxford in 2020, it deals with the “patterns” Big Data throws up that aren’t really there

David Auerbach has picked The Phantom Pattern Problem (2020) by Gary Smith and Jay Cordes as one of the top books of 2020 in the science and tech category. Auerbach, who describes himself as “a writer and software engineer, trying to bridge the two realms,” is the author of BITWISE: A Life in Code (2018). He has an interesting way of choosing books to recommend: Those that resist the “increasingly desperate and defensive oversimplification” of popular culture: I hesitate to mention too many other books for fear of neglecting the others, but I will say that of the science and technology books, several deal with subjects that are currently inundated with popularizations. In my eye, those below are notably superior…

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Abstract 3d render, geometric composition, yellow background design with cubes

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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White swan mask on black wooden surface. Empty space.

New Book Takes Aim at Phantom Patterns “Detected” by Algorithms

Human common sense is needed now more than ever, says economics professor Gary Smith

Pomona College economics professor Gary Smith, author with Jay Cordes of The Phantom Pattern Problem (Oxford, October 1, 2020), tackles an age-old glitch in human thinking: We tend to assume that if we find a pattern, it is meaningful. Add that to the weaknesses of current artificial intelligence and “Houston, we have a problem,” he warns: The scientific method tests theories with data. Data-mining computer algorithms dispense with theory and search through data for patterns, often aided and abetted by slicing, dicing, and otherwise mangling data to create patterns. Gary Smith, “Phantom patterns: The big data delusion” at IAI News (August 24, 2020) Many of the patterns so detected are obviously spurious, for example: A computer algorithm for evaluating job…