In Search of Ground Truth: From Social Media Networks to Deep Neural Networks

 In Search of Ground Truth: From Social Media Networks to Deep Neural Networks

A public lecture by Professor Conrad Tucker, Director, Carnegie Mellon University-Africa (CMU-Africa)

Ascertaining the veracity of data in the information age is a challenge both for humans (e.g., communicating within social media networks) and machines (e.g., training data for artificial neural networks). A lack of data veracity has the potential to “fool” both machines as well as humans into achieving different outcomes/output. From a machine learning perspective, “fooling” a machine has had a positive impact in the development of algorithms such as Generative Neural Networks (GNNs) and has resulted in the ability of machines to generate hyper-realistic data such as images and text. However, adverse effects can be observed in large-scale social media networks, where the veracity of data cannot be quickly ascertained. Misinformation that is spread via social media networks can result in echo-chambers, lone communities that facilitate selective content diffusion as a result of user polarization. Ironically, this misinformation can now be reliably generated using machine learning algorithms such as GNNs. This research explores the future of human-machine learning and the challenges and opportunities that exist in information acquisition, characterization and utilization.

Professor Conrad Tucker is the Director of Carnegie Mellon University-Africa (CMU-Africa) and the Associate Dean for International Programs-Africa. He is a Trustee Professor of Mechanical Engineering at Carnegie Mellon University and holds courtesy appointments in Machine Learning, Robotics, Biomedical Engineering, and CyLab Security and Privacy. His research focuses on employing Machine Learning (ML)/Artificial Intelligence (AI) techniques to enhance the novelty (i.e., generative designs) and efficiency (i.e., functional evaluations) of engineered systems. His research also explores the challenges of bias and exploitability of AI systems and the potential impacts on people and society.

Dr. Tucker has served as PI/Co-PI on federally/non-federally funded grants from the National Science Foundation, the Air Force Office of Scientific Research, the Defense Advanced Research Projects Agency, the Army Research Laboratory, the Bill and Melinda Gates Foundation, among others. In February 2016, he was invited by National Academy of Engineering (NAE) President Dr. Dan Mote, to serve as a member of the Advisory Committee for the NAE Frontiers of Engineering Education Symposium. He recently served as a Commissioner on the U.S. Chamber of Commerce Artificial Intelligence Commission on Competitiveness, Inclusion, and Innovation and currently serves as a member of the Organisation for Economic Co-operation and Development (OECD) Expert Group on AI risk and accountability ONE AI. Dr. Tucker received his Ph.D., M.S. (Industrial Engineering), and MBA degrees from the University of Illinois at Urbana-Champaign, and his B.S. in Mechanical Engineering from Rose-Hulman Institute of Technology.

Professor Tucker is a UWA Institute of Advanced Studies Visiting Fellow.