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Researchers at the Cheriton School of Computer Science have pioneered a new method that could be used to develop more natural automated virtual assistants to help people suffering from mental illness. 

Called SMERTI (pronounced smarty), the new method enables virtual assistants to use natural language and emotional cues that change depending on the relationship and situations in which they are used. The result allows for the development of virtual assistants that better connect with people they are used to help.

The key to people trusting and co-operating with artificially intelligent agents lies in their ability to display human-like emotions, according to a new study by Postdoctoral Fellow Moojan Ghafurian, Master’s candidate Neil Budnarain and Professor Jesse Hoey at the Cheriton School of Computer Science.

Cheriton School of Computer Science Professor Jesse Hoey has teamed up with Professor Robert Freeland, a sociologist at Wake Forest University, to conduct novel research at the intersection of computer science and social psychology.

Professors Olga Veksler and Yuri Boykov joined the David R. Cheriton School of Computer Science earlier this year. Previously, both were full professors in the Department of Computer Science at Western University, where they were faculty members for 14 years.

Their research interests are in the area of computer vision. In particular, Olga’s interests are in visual correspondence and image segmentation, and Yuri’s also include 3D reconstruction and biomedical image analysis.

The Vector Institute drives excellence and leadership in Canada’s knowledge, creation and use of artificial intelligence to foster economic growth and improve the lives of Canadians. The institute is dedicated to the transformative field of artificial intelligence, excelling in machine and deep learning research.

Professor Jeff Orchard and third-year undergraduate computer science student Louis Castricato received a best paper award at the24thInternational Conference on Neural Informational Processing(ICONIP 2017) for their paper titled “Combating adversarial inputs using a predictive-estimator network.”

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