And I think that may actually be the key to AI the same way it was the key to human development. Teaching is the part that you have to optimize at a global level, at a societal level. So, from a philosophical standpoint, I believe that machine learning algorithm is almost the easy part, is the part that you can locally optimize. I think teaching is what makes the difference. And I actually don’t believe it’s the learning algorithm. Patrice Simard: A lot of people have thought that the key to AI is the learning algorithm. Microsoft Research Newsletter: Sign up to receive the latest news from Microsoft Research.Android: Subscribe and listen on Android.iTunes: Subscribe and listen to new podcasts each week on iTunes.Microsoft Research Podcast: View more podcasts on.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. 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. 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. Machine learning is a powerful tool that enables computers to learn by observing the world, recognizing patterns and self-training via experience.

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