New York, Dec 16: Researchers led by an Indian-origin scientist from Georgia Institute of Technology have discovered how humans can categorise data using less than one percent of the origil information. They validated an algorithm to explain human learning — a method that also can be used for machine learning, data alysis 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, distinguished professor of computer science. “At a fundamental level, how do humans begin to do that? It’s a computatiol problem,” he asked.
Vempala and colleagues presented test subjects with origil, abstract images and then asked whether they could correctly identify that same image when randomly shown just a small portion of it. “We hypothesised that random projection could be one way humans learn,” said Rosa Arriaga, senior research scientist and developmental psychologist
“The prediction was right. Just 0.15 percent of the total data is enough for humans,” she added. Next, researchers tested a computatiol algorithm to allow machines to complete the same tests. (IANS)