High Performance Computing vs Quantum Computing for Neural Networks supporting Artificial Intelligence
A personalized learning system that adapts to learners' interests, needs, prior knowledge, and available resources is possible with artificial intelligence (AI) that utilizes natural language processing in neural networks. These deep learning neural networks can run on high performance computers (HPC) or on quantum computers (QC). Both HPC and QC are emergent technologies. Understanding both systems well enough to select which is more effective for a deep learning AI program, and show that understanding through example, is the ultimate goal of this project. The entry to learning technologies such as HPC and QC is narrow at present because it relies on classical education methods and mentoring. The gap between the knowledge workers needed, which is in high demand, and those with the expertise to teach, which is being achieved at a much slower rate, is widening. Here, an AI cognitive agent, trained via deep learning neural networks, can help in emergent technology subjects by assisting the instructor-learner pair with adaptive wisdom. We are building the foundations for this AI cognitive agent in this project.
The role of the student facilitator will involve optimizing a deep learning neural network, comparing and contrasting with the newest technologies, such as a quantum computer (and/or a quantum computer simulator) and a high performance computer and showing the efficiency of the different computing approaches. The student facilitator will perform these tasks at the rate described in the proposal. Milestone work will be displayed and shared publicly via posting to the Jupyter Notebooks on Google Colab and linked to regular Github uploads.
Understanding Covid-19 Pandemic through Social Media Discussion
Dr. Li has been collecting covid-19 tweets since March 2020 and currently has about 1.2 billion tweets. She is still collecting the tweets and expects to have more in the future. This project focuses on the understanding of the impact of covid-19 pandemic through social media discussion on Twitter. The following topics will be explored: 1). What are the top topics discussed regarding covid-19? How has the discussion of the topics changed over time? 2). What is sentiment/emotion of the topic by time, location, and gender? and 3). How to identify misinformation/fake news about covid-19.
The student will work on this project from start to finish using various data analytic methodology including data exploration, topic modelling, natural language processing and machine learning.