二十一世纪的计算大会 | Learning to Learn: Exploring Approaches to Help Machine and People Learn

In recent years, there have been much progress in machine learning in the areas of computer vision, speech, natural language processing, and other domains. Yet, there remain many challenging situations where better machine learning algorithms are necessary. There are cases where teaching signals and evaluation metrics are very clear. There are also scenarios where evaluation metrics can be subjective and one would need to rely on real world feedback for better learning. In this talk, I will present some of the recent work in helping machines learn from Microsoft Research Asia, such as dual learning and self-generated data learning. Furthermore, I will highlight some important challenges for machine learning. Lastly, as artificial intelligence makes bigger impact on society, people also need to adapt to enhance their skills. I will talk about some recent work on using machine to help people learn.