Knowledge Transfer in Artificial Intelligence Systems

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Continually acquiring and transferring knowledge is a key characteristic of human intelligence, which enables us rapidly to adapt to new tasks and environments as we do not have to learn from scratch. By knowledge transfer, we continually develop a wide variety of complex abilities to deal with diverse tasks and problems. While it is an inherent ability of human intelligence, how to enable artificial intelligence systems to appropriately transfer knowledge remains a largely unsolved problem. On the other hand, the recent success of machine learning techniques largely relies on, in addition to the development of efficient algorithms, the availability of large data sets. However, in many real-world applications (e.g., medicine, neuroscience), collecting data is either expensive or time consuming, which makes “big data” unavailable to build reliable machine learning models. Knowledge transfer would be critical in these applications, as it can help to learn with small data with the help of prior knowledge. To this end, my proposed research program will be focused on the development of a comprehensive approach to general-purpose continual knowledge maintain and transfer (CKMT) systems, which can rapidly learn new tasks and adapt to changing environments, with little training data, by knowledge transfer. More specifically, the research proposed is organized around three main objectives: 1. Creating a new mechanism for safe transfer between machine learning tasks; 2. Modeling the non-stationary environments of tasks; 3. Creating a framework for knowledge transfer across different machine learning paradigms. We will also apply our methods to solve problems with practical impact. In particular, we will apply them to brain signals analysis in various application scenarios, as well as power system operation and smart grid energy management. This project will address some fundamental issues in Artificial Intelligence, including transfer learning, multitask learning, meta-learning and lifelong learning. It will provide novel theoretical insights and justifications of knowledge transfer, and motivate new knowledge transfer algorithms and models. With each objective, I will train one PhD and one MSc student, providing them with highly sought-after and valuable skills in Artificial Intelligence and Machine Learning transferable to many subjects, such as biomedical engineering, neuroscience, power/energy management, and others like finance/banking/insurance, for example.