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We live in a time of information abundance, which far too many of us see as information overload. With the sum total of human knowledge, past and present, at our fingertips, we’re faced with a crisis of attention: which ideas should we engage with, and why? Big Think is an evolving roadmap to the best thinking on the planet — the ideas that can help you think flexibly and act decisively in a multivariate world.

A word about Big Ideas and Themes — The architecture of Big Think

Big ideas are lenses for envisioning the future. Every article and video on bigthink.com and on our learning platforms is based on an emerging “big idea” that is significant, widely relevant, and actionable. We’re sifting the noise for the questions and insights that have the power to change all of our lives, for decades to come. For example, reverse-engineering is a big idea in that the concept is increasingly useful across multiple disciplines, from education to nanotechnology.

Themes are the seven broad umbrellas under which we organize the hundreds of big ideas that populate Big Think. They include New World Order, Earth and Beyond, 21st Century Living, Going Mental, Extreme Biology, Power and Influence, and Inventing the Future.

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Facing It

December 21, 2009, 6:33 AM
“An engineering team has developed a face recognition system that is remarkably accurate in realistic situations. Unlike existing face recognition programs that try to find ‘optimal’ facial features, the new program uses sparse representation. One of the program’s developers, Yi Ma, an associate professor at the University of Illinois, contends that the choice of features is less important than the number of features used. ‘Face recognition is not new, but new mathematical models have allowed researchers to identify faces so occluded that it was previously thought impossible,’ says Ma. People can learn upwards of tens of thousands of different human faces during their lifetime. Various real-world situations such as lighting, background, pose, expression, and occlusion may complicate human recognition, but are incredibly difficult problems for traditional face recognition algorithms to conquer. Ma’s sparse representation algorithm randomly selects pixels from all over the face, increasing the accuracy of recognition even in cases of disguise, varying expressions, or poor image quality.”

Facing It

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