U of T Engineering professors Scott Sanner (MIE) and Vaughn Betz (ECE) are among this year’s recipients of the Google Faculty Research Award. The program supports world-class research in computer science, engineering and related fields, and facilitates collaboration between researchers at Google and universities.
Only 15 per cent of applicants receive funding. This year, Google received more than 900 proposals from 50 countries and more than 330 universities worldwide.
“Given the high selectivity of this program, it is a tremendous accomplishment for professors Sanner and Betz to receive Google Faculty Research Awards,” says Professor Ramin Farnood, Vice-Dean of Research, U of T Engineering. “It is a testament to the calibre of their work that they are being recognized amongst the very best institutions in the world.”
Sanner joins a list of researchers from Stanford University, the Massachusetts Institute of Technology (MIT) and Carnegie Mellon University to be awarded in the Machine Learning category. His team will use the funding to develop more personalized and interactive conversational assistants by leveraging recent advances in deep learning.
Although Siri, Alexa and Google Assistant have become useful tools for consumers, Sanner points out that they currently do not provide highly personalized recommendations for questions such as, “What movie should I see tonight?”
“These systems usually can’t handle rich, natural language interactions like, ‘Can you give me something a little lighter?’ in response to a recommendation to see Goodfellas,” says Sanner.
Though it might seem that voice-based assistants are on the brink of achieving those capabilities, Sanner says it’s more complex than most imagine.
Personalized recommendations pose a style of interaction that is very different from the rule-based template and curated web-search technology that largely powers the existing conversational assistants of today.
Getting Siri or Alexa to understand how natural language in human interactions should influence future personalized recommendations means relying on machine learning and deep learning, as opposed to rules and web search.
“To date, few researchers have investigated how these various technologies can dovetail to power interactive, conversation-based recommendations,” adds Sanner.
This article by Liz Do, “Google recognizes machine learning and computer systems experts with Faculty Research Award,” originally appeared on Engineering News, February 28, 2020 and has been abridged here.