Title: How to Make Machines Understand Math?

Abstract:   Since the breakthrough of Google's AlphaGo program that defeated the world Go champion in March 2016, the Artificial Intelligence (AI) technologies, mainly machine learning, become very popular in the technology world. Most applications using the machine learning technology heavily rely on certain statistics models, which may not be very effective in solving math problems. In this talk, a novel method called experience-based approach (EBA) is developed to solve math problems, which replaces the traditional axiom-based approach (ABA). The main advantage of the EBA method is that the solutions generated are human-readable, while the ABA-generated solutions are only readable by experts with special training. The human-readable requirement is essential in education. Another important consideration in education is that the system should help students understand math, not simply providing them solutions without any math understanding. In order to develop such a system, we first need to make machines understand math, which means that we need to create a mechanism to represent math expressions so that the pattern matching technique can be applied in solving math problem. A demo will be given to show how the system can solve a simple math problem with a sequence of suggestions. The students using the system still need to make their own decisions based on their math understanding.