Patrice Simard is a Distinguished Engineer at Microsoft. He is passionate about finding new ways to combine engineering and science in the field of machine learning. Simard’s research is currently focused on making machine learning widely accessible for replicating tasks easily done by humans.
He received a PhD in Computer Science from the University of Rochester in 1991. Simard then worked at AT&T Bell Laboratories before joining Microsoft Research in 1998. He was Chief Scientist and General Manager of Live Labs Research in 2006 and Chief Scientist of Microsoft’s AdCenter in 2009. In 2012, he returned to Microsoft Research to create the Computer-Human Interactive Learning (CHIL) group. He is now leading the Machine Teaching Innovation Group in Microsoft’s Experience and Devices division.
Episode 78, May 29, 2019- Machine learning is a powerful tool that enables computers to learn by observing the world, recognizing patterns and self-training via experience. Much like humans. But while machines perform well when they can extract knowledge from large amounts of labeled data, their learning outcomes remain vastly inferior to humans when data is limited. That’s why Dr. Patrice Simard, Distinguished Engineer and head of the Machine Teaching group at Microsoft, is using actual teachers to help machines learn, and enable them to extract knowledge from humans rather than just data.
Today, Dr. Simard tells us why he believes any task you can teach to a human, you should be able to teach to a machine; explains how machines can exploit the human ability to decompose and explain concepts to train ML models more efficiently and less expensively; and gives us an innovative vision of how, when a human teacher and a machine learning model work together in a real-time interactive process, domain experts can leverage the power of machine learning without machine learning expertise.