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Brendan Dixon: The Numbers Don’t Speak for Themselves

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Because Machine Learning is opaque—even experts cannot clearly explain how a system arrived at a conclusion—we treat it as magic. Therefore, we should mistrust the systems until proven innocent (and correct)

Data analyst Kalev Leetaru—who has written several good pieces on AI at Forbes—also points out that machine learning is “about correlations, not causation.” The fact that two pieces of data may be linked does not mean that one causes the other but nonetheless,

“Developers and data scientists increasingly treat their creations as silicon lifeforms ‘learning’ concrete facts about the world, rather than what they truly are: piles of numbers detached from what they represent, mere statistical patterns encoded into software. We must recognize that those patterns are merely correlations amongst vast reams of data, rather than causative truths or natural laws governing our world. Kalev Leetaru, “A Reminder That Machine Learning Is About Correlations Not Causation” at Forbes

At times, verification and study of these statistical patterns are useful. For example, machine learning is well-suited to assist us in the analysis of complex patterns, such as MRI results. Properly used, it could help save many lives. But when misapplied—for the sake of “efficiency”—to broad swathes of the people using data sets gathered for other purposes, sets that nearly always carry embedded biases, it damages many lives. More.

Brendan Dixon of the Biologic Institute is a Software Architect with experience designing, creating, and managing projects of all sizes. His first foray into Artificial Intelligence was in the 1980s when he built an Expert System to assist in the diagnosis of software problems at IBM. Though he’s spent the majority of his career on other types of software, he’s remained engaged and interested in the field.

Also by Brendan Dixon: Artificial Intelligence Is Actually Superficial Intelligence The confusing ways the word “intelligence” is used belie the differences between human intelligence and machine sophistication

AI Winter Is Coming: Roughly every decade since the late 1960s has experienced a promising wave of AI that later crashed on real-world problems, leading to collapses in research funding.


The “Superintelligent AI” Myth: The problem that even the skeptical Deep Learning researcher left out

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