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Phoenix666 [soylentnews.org] writes:

Humans learn to very quickly identify complex objects and variations of them [gatech.edu]. We generally recognize an “A” no matter what the font, texture or background, for example, or the face of a coworker even if she puts on a hat or changes her hairstyle. We also can identify an object when just a portion is visible, such as the corner of a bed or the hinge of a door. But how? Are there simple techniques that humans use across diverse tasks? And can such techniques be computationally replicated to improve computer vision, machine learning or robotic performance?

Researchers at Georgia Tech discovered that humans can categorize data using less than 1 percent of the original information, and validated an algorithm to explain human learning -- a method that also can be used for machine learning, data analysis and computer vision.

“How do we make sense of so much data around us, of so many different types, so quickly and robustly?” said Santosh Vempala [gatech.edu], Distinguished Professor of Computer Science at the Georgia Institute of Technology and one of four researchers on the project. “At a fundamental level, how do humans begin to do that? It’s a computational problem.”
...
“This fascinating paper introduces a localized random projection that compresses images while still making it possible for humans and machines to distinguish broad categories,” said Sanjoy Dasgupta, professor of computer science and engineering at the University of California San Diego and an expert on machine learning and random projection. “It is a creative combination of insights from geometry, neural computation, and machine learning.”

Although researchers cannot definitively claim that the human brain actually engages in random projection, the results support the notion that random projection is a plausible explanation, the authors conclude. In addition, it suggests a very useful technique for machine learning: large data is a formidable challenge today, and random projection is one way to make data manageable without losing essential content, at least for basic tasks such as categorization and decision making.

They were able to replicate human levels of performance.

Original Submission