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2022 Keynote Speakers


Prof. Shuliang Li, University of Westminster, UK & School of Economics & Management, Southwest Jiaotong University, China

PhD, Fellow (Life) of the British Computer Society
Reader in Business Information Management & Systems Westminster Business School, University of Westminster, United Kingdom

Speech Title: TBA

Biography: Prof. Dr Shuliang Li is a Reader in Business Information Management at Westminster Business School, University of Westminster. He acted as BIM&O departmental Research Leader from January 2010 to July 2013. He is a life Fellow of the British Computer Society (FBCS). 
(Official Web page) 

 

Prof. Adrian Hopgood, University of Portsmouth, UK

Speech Title: Artificial intelligence for improved healthcare

Biography: Adrian Hopgood is Professor of Intelligent Systems at the University of Portsmouth, where he is Director of the Future & Emerging Technologies theme and of the South Coast Centre of Excellence in Satellite Applications. He is also a visiting professor at the Open University and at Sheffield Hallam University. He is a Chartered Engineer, Fellow of the BCS (the Chartered Institute for IT), and a committee member for the BCS Specialist Group on Artificial Intelligence.
Professor Hopgood has extensive experience in both academia and industry. He has worked at the level of Dean and Pro Vice-Chancellor in four universities in the UK and overseas, and has enjoyed scientific roles with Systems Designers PLC and the Telstra Research Laboratories in Australia. His main research interests are in artificial intelligence and its practical applications. He has supervised 20 PhD projects to completion and published more than 100 research articles. His textbook “Intelligent Systems for Engineers and Scientists: A Practical Guide to Artificial Intelligence” is ranked as a bestseller and its fourth edition was published in 2022.

Abstract: Artificial intelligence (AI) has been the subject of research for 70 years. Some of the earliest applications were in the medical domain, where knowledge-based AI aimed to capture clinical knowledge in a flexible and adaptable computer model. In contrast, the recent surge in interest in AI has been largely driven by machine learning, which involves the recognition of patterns in large datasets. These two styles of AI are complementary and offer the possibility of significant improvements in healthcare. This talk will introduce several new medical applications of AI, including the prediction of patient outcomes after surgery, avoidance of intradialytic hypotension, interpretation of X-ray images, and improved electronic heath records.


Prof. Daniel O'Leary, University of Southern California, USA

Speech Title: Massive Data Language Models and Conversational AI: Emerging Issues

Biography: Daniel E. O’Leary is a tenured full professor at the University of Southern California (USC), in the Marshall School of Business. He received his Ph. D. from Case Western Reserve University and his master’s degree in management science and statistics from the University of Michigan. Dan formerly worked with KPMG in their consulting practice. Professor O’Leary recently was named a Fulbright – Hays Scholar recipient and was a co-author on a paper that was named the winner of the 2020 Paul Gray “Thought Provoking” Paper award. Professor O’Leary’s book, Enterprise Resource Planning Systems book, published by Cambridge University Press has been translated into both Russian and Chinese. Professor O’Leary’s research focuses primarily on the use of emerging technologies, big data and artificial intelligence in business.


Abstract:Google’s LaMDA, Open AI’s GPT-3 and Meta’s BlenderBot are artificially intelligent (AI) based chatbots, that have been trained on billions of documents creating the notion of “Massive Data.” These systems use human generated documents to capture words and relationships between words that people use when they communicate. This paper examines some of the similarities of these systems and examines some of the emerging issues regarding these massive data language models, including whether they are sentient, the use and impact of scale, information use and ownership, explanation of discussion and answers and other concerns. This paper also directly investigates some artifacts of Google’s LaMDA, and compares them with Meta’s BlenderBot. Finally, this paper examines some emerging issues and questions deriving from our analysis.

More info. will come soon...