Machine Learning for Human Learning: Challenges and Opportunities

Hui Lin

Compared to machine learning, human learning is distinguished by the range and complexity of skills that can be learned and the degree of abstraction that can be achieved. Although human is a remarkable learning machine, the need for efficient learning is growing as the world is rapidly evolving around us. How to leverage machine learning to increase the efficiency and effectiveness of human learning has received much attention recently. In this talk, I will first introduce some areas where machine learning, especially speech and natural language processing technology, can play an important role to make learning more personalized and more efficient. I will then share some recent efforts we made to address the challenges arise in these areas, and conclude with a discussion on opportunities ahead.

  Dr. Hui Lin is the co-founder and chief scientist at LAIX Inc. (NYSE:LAIX, “LAIX” or the “Company”). He is also an Adjunct Professor at Shanghai University. Prior to founding LAIX, he was a research scientist at Google in Mountain View, California. His areas of research include speech recognition, natural language processing, machine learning, and data mining. Hui Lin has published over 30 papers in top industry journals. Hui Lin received his Ph.D. in Electrical Engineering from University of Washington, and M.S.E. and B.S.E. in Electronics and Communications Engineering from Tsinghua University. Dr. Hui Lin is responsible for leading the artificial intelligence team of LAIX to carry out technical research and development projects. He is leading AI scientists in Shanghai Headquarters and Silicon Valley AI Lab to explore the frontier areas of AI technologies and education to ensure that the Company's AI technology is always at an advanced level in the world. On September 27, 2018, as the forerunner of “Education 3.0” model, LAIX was officially listed on the New York Stock Exchange and was hailed as “The First Stock of AI+Education” by media.