The confusing ways the word “intelligence” belie the differences between human intelligence and machine sophistication
We’ve known, for as long as we’ve had chess-playing programs, that computers do not play chess like we play chess. We’ve lost sight of that difference because recent artificial intelligence (AI) program advances have overcome higher hurdles than previous programs. Computers now win at dynamic strategy games, translate languages, analyze MRIs, and even recognize cats. These advances seem, on the surface, to convey the idea that more is going on than mere programming, that computers are living up to their designation as “intelligent” in the same sense as a human being. But we should know better. And recent research into how the latest advances differ from human mental activity demonstrates that. 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: The Numbers Don’t Speak for Themselves The patterns uncovered by machine learning may reflect a larger reality or just a bias in gathering data 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).
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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