Artificial intelligence technologies are increasingly taking more essential roles in modern society. They are used in web-searches, social and e-commerce websites, cars, and cell phones, etc. More recently, deep-learning methods made important advances in solving a wide range of problems in artificial intelligence. It achieved remarkable results outperforming other machine learning algorithms in fields like image recognition, speech recognition, and natural language understanding. These results sparked a wave of commercial activities and startups.
Deep learning methods are a class of neural networks that allows a machine to be fed with raw data and to automatically discover the necessary formulas needed for classification. After training, the performance of the system is tested on new data sets. Deep neural networks are composed of a multi-layer stack of simple neural networks. Each module in this stack transforms the input using non-linear or linear mathematical operations. Despite the progress enabled by deep-learning methods, limitations of the technology remain to be explored and addressed in order for it to reach its full potential and range of application areas. In this project, we will design and develop novel systems that extends the capabilities of current technology.