Prof. Edward Y. Chang
ACM Fellow / IEEE Fellow
Stanford University, USA

Edward Y. Chang is an adjunct professor of Computer Science at Stanford University since 2019, and a visiting chair professor at Asia University. His current research interests include consciousness modeling, generative AI, and healthcare. Chang received his MS in CS and PhD in EE, both from Stanford University. He joined the ECE department of UC Santa Barbara in 1999, where he was tenured in 2003 and promoted to full professor in 2006. From 2006 to 2012, Chang served at Google as a director of research, leading research and development in areas such as scalable machine learning, indoor localization, Google QA, and recommendation systems. In subsequent years, Chang served as the president of HTC Healthcare (2012-2021) and a visiting professor at UC Berkeley AR/VR center (2017-2021), working on healthcare projects including VR surgery planning, AI-powered medical IoTs, and disease diagnosis. Between 2019 and 2022, Chang also served at SmartNews, a Tokyo-based unicorn, as its chief NLP advisor. Chang is an ACM fellow and IEEE fellow for his contributions to scalable machine learning and healthcare.

Speech Title: The Future of AI: From Unconscious Models to Conscious Reasoning and Emotional Intelligence
Abstract: In this presentation, I first highlight the advancements in foundation models in the past decade, and present their impact on various domains. I then explore some known issues such as a lack of robustness, interpretability, and generalization. It's argued that AI models have successfully modeled human unconsciousness to a certain extent. However, to enable AI to think and plan, a proposition is made [1] to model consciousness on top of unconsciousness. The discussion begins with the definition of consciousness, the exploration of the transitional mechanisms between consciousness and unconsciousness, theories of consciousness, and the differentiation between attention and consciousness. A functionalist approach is adopted to formulate a model of consciousness that encompasses perception, awareness, attention, critical thinking, creative thinking, and emotional intelligence. An illustration is provided on how these consciousness capabilities can be developed in foundation models through the careful design of prompt templates. Specifically, how the Socratic method [2], along with inductive, deductive, and abductive reasoning, can facilitate critical thinking and reading via prompt-template engineering is demonstrated. The integration of emotional intelligence to guide exploratory thinking, with ethical safeguards that respect individual and cultural values, is also discussed.

[1] CoCoMo: Computational Consciousness Modeling for Generative and Ethical AI, Edward Y. Chang, ArXiv:2304.02438 e-print, February, 2023, [PDF].
[2] Prompting Large Language Models With the Socratic Method, Edward Y. Chang, IEEE 13th Annual Computing and Communication Workshop and Conference (CCWC), March 2023 [PDF].

Prof. Fakhri Karray
IEEE Fellow
University of Waterloo, Canada

Fakhri Karray is the founding co-director of the University of Waterloo Artificial Intelligence Institute and is the Loblaws Research Chair in Artificial Intelligence in the Department of electrical and computer engineering at the University of Waterloo, Canada. He is also a Professor of Machine Learning and the former Provost at the Mohamed bin Zayed University of Artificial Intelligence (MBZUAI), a graduate-level, research-based artificial intelligence (AI) university, in Abu Dhabi, UAE. Fakhri’s research interests are in the areas of operational AI, cognitive machines, natural human-machine interaction, and autonomous and intelligent systems. Applications of his research include virtual care systems, cognitive and self-aware machines/robots/vehicles, predictive analytics in supply chain management and intelligent transportation systems. He serves as Associate Editor and member of the editorial board of major publications in smart systems and information fusion.

His most recent textbook in foundational machine learning “Elements of Dimensionality Reduction and Manifold Learning” was published by Springer Nature in February 2023. He was honored in 2021 by the IEEE Vehicular Technology Society (VTS) with the IEEE VTS Best Land Transportation Paper Award for his pioneering work on improving traffic flow prediction with weather Information in connected cars using deep learning and AI. His recent work on federated learning in communication systems earned him and his co-authors the 2022 IEEE Communication Society’s MeditCom Conference Best Paper Award. Fakhri is a Fellow of the IEEE, a Fellow of the Canadian Academy of Engineering, a Fellow of the Engineering Institute of Canada. He served as a Distinguished Lecturer for the IEEE and a Kavli Frontiers of Science Fellow. Fakhri received the Ing. Dip degree in electrical engineering from the School of Engineering of the University of Tunis,Tunisia and the Ph.D. degree from the University of Illinois Urbana-Champaign, USA.

Speech Title: Generative vs. Operational Artificial Intelligence: Opportunities and Challenges
Abstract: The talk presents recent trends and major advances accomplished lately in the field of Artificial Intelligence (AI), specifically Operational and Generative Artificial Intelligence (OAI/GAI). As demonstrated by impressive accomplishments made in the field (such as ChatGPT and other generative AI based engines) and due to fundamental advances made in the field of machine learning and artificial intelligence, experts are predicting we are at the cusp of a new technological revolution. It is expected that AI will grow the world GDP by up to 20% by 2025. This amounts to more than 15 Trillion dollars of growth over the next few years. These developments have impacted significantly technological innovations in the field of Internet of Things, self-driving machines, powerful chat bots, virtual assistants, human machine intelligent interface, large language models, real-time translators, cognitive robotics, virtual care systems, eHealth and Fintech, to name a few. Although AI constitutes an umbrella of several interrelated technologies, all of which are aimed at imitating to a certain degree intelligent human behavior or decision making, deep learning algorithms are considered to be the driving force behind the explosive growth of AI and their applications in almost every scientific and technological sector: disease diagnosis, remote health care monitoring, financial market prediction, self-driving vehicles, social robots with cognitive skills, intelligent manufacturing, surveillance, cybersecurity, intelligent transportation systems, to name a few. The talk highlights the milestones that led to the current growth in AI, OAI and GAI, the role of academic institutions and discusses some of the major achievements in the fields. It enumerates as well real challenges when these innovations are mis-used leading to potential negative effects on society and end-users.



Prof. Ling Liu
IEEE Fellow
Georgia Institute of Technology, USA

Ling Liu is a Professor in the School of Computer Science at Georgia Institute of Technology. She directs the research programs in the Distributed Data Intensive Systems Lab (DiSL), examining various aspects of large scale big data-powered artificial intelligence (AI) systems, and machine learning (ML) algorithms and analytics, including performance, availability, privacy, security and trust. Prof. Liu is an elected IEEE Fellow, a recipient of IEEE Computer Society Technical Achievement Award (2012), and a recipient of the best paper award from numerous top venues, including IEEE ICDCS, WWW, ACM/IEEE CCGrid, IEEE Cloud, IEEE ICWS. Prof. Liu served on editorial board of over a dozen international journals, including the editor in chief of IEEE Transactions on Service Computing (2013-2016), and the editor in chief of ACM Transactions on Internet Computing (since 2019). Prof. Liu is a frequent keynote speaker in top-tier venues in Big Data, AI and ML systems and applications, Cloud Computing, Services Computing, Privacy, Security and Trust of data intensive computing systems. Her current research is primarily supported by USA National Science Foundation under CISE programs, IBM and CISCO.

Speech Title: Can Federated Learning be Responsible ?
Abstract: Federated learning (FL) is an emerging distributed collaborative learning paradigm by decoupling the learning task from the centralized server to a decentralized population of edge clients. One of the attractive features of federated learning is its default client privacy, allowing clients to jointly learn a global model while keeping their sensitive training data locally and only share local model updates with the federated server(s). However, recent studies have revealed that such default privacy is insufficient for protecting the confidentiality of client training data and the safety of the global model. This keynote will describe model leakage risks and model poisoning risks in distributed collaborative learning systems, ranging from image understanding, video analytics, to large language models (LLMs), and provide insights for risk mitigation methods and techniques, ensuring responsible Federated Learning.