Professor James Elder, who is a co-author of a study published by York University, says that deep convolutional neural networks (DCNNs) do not perceive objects as humans do, with configural shape perception, which could be risky in real-time AI applications. The study was reported in the iScience -- a Cell Press journal. "Deep Learning Models Are Unsuccessful in Capturing the Configural Manner of Human Shape Perception" is a joint study by Elder, a York Research Chair in Human and Computer Vision and a Co-Director of York's Centre for AI & Society, and Nicholas Baker, an Assistant Psychology Professor at Loyola College in Chicago, a former VISTA postdoctoral fellow at York. To discover how the human brain and DCNNs process complete, configural object properties, the scientists used novel visual stimuli known as "Frankensteins." Frankensteins are simply objects that have been taken apart and put back together the wrong way around.

Brainstorm guest contributor Paul Fraumeni speaks with four York U researchers who are applying artificial intelligence to their research ventures in ways that, ultimately, could lead to profound and positive impacts on health care in this country. Meet four York University researchers: Lauren Sergio and Doug Crawford have academic backgrounds in physiology; Shayna Rosenbaum has a PhD in psychology; Joel Zylberberg has a doctorate in physics. They share two things in common: They focus on neuroscience – the study of the brain and its functions – and they leverage advanced computing technology using artificial intelligence (AI) in their research ventures, the application of which could have a profound and positive impact on health care. In a nondescript room in the Sherman Health Sciences Research Centre, Lauren Sergio sits down and places her right arm in a sleeve on an armrest. It's an odd-looking contraption; the lower part looks like a sling attached to a video game joystick.