TORONTO, Ontario (September 29, 2015) -- When you look at someone, what do you see? The details and nuances of what our brains process help us identify, relate to and store impressions of faces and objects. How we perceive, interpret and remember the world around us is part of what makes us human. But what if computers can be taught to do the same?
On Saturday night, Toronto-based software studio, Architech, will present the art of what’s possible through a computer’s “eyes” at the 10th annual Scotiabank Nuit Blanche. The real-time installation, “The Face of Toronto”, is the first-ever large-scale interactive art exhibit to use deep learning to showcase the future of face-processing technology. Architech’s computer will create an amalgamation of thousands of faces of Nuit Blanche attendees, resulting in a massive digital portrait of what the computer has seen, perceived and interpreted - its “memory” - of “The Face of Toronto”.
“Our team has spent months building out the advanced deep learning models that can now recognize individual keypoints with great speed and robustness in uncontrolled conditions, similar to the human brain,” says David Suydam, Architech’s CEO and Founder. “We’re using deep learning machines to process random individual faces and creating a unified whole. It’s both a fascinating peek at our present reality and a visual imprint of memories from one night in the city.”
About the technology
Deep learning, a subfield of machine learning, teaches computers to learn like humans by simulating the way the neurons of the brain are connected across a neural network. These artificial networks give the machines an exponentially higher capacity to absorb knowledge from data by performing in a fashion analogous to the way the human brain processes, represents, and uses information. The higher the amount of information the better deep learning works, making the system virtually impossible to over-saturate. Perhaps even more impressively, deep learning can model any type of data – like text, video, or voice – across a wide spectrum of fields, allowing the computer to combine and exchange insights from multiple domains and eliminating the need for the trial-and-error results of human guesswork. Recent examples of deep learning in the news include Google’s DeepDream and Facebook’s DeepFace.
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