Panel on AI and Autonomous Systems (AI*AS)

The panel on “AI and Autonomous Systems (AI*AS)” is designed to address the latest advances of AI and the cutting-edge challenges to Autonomous Systems (AS) in symbiotic human and robot environment for real-time intelligence generation beyond learning. It aims to enrich the coverage of IEEE SMC’23 program and to enable audience, particularly young researchers, to interact with leading scientists and experts in our fields. Participants may be enlightened by the panelists’ vision and thought to emerging AI technologies and their Intelligence Science (IS) foundations towards autonomous systems. Brain-inspired systems (BIS) and training-free Autonomous AI (AAI) will be explored, underpinned by IS foundations and contemporary Intelligent Mathematics (IM). This spectrum of basic research will enable the next-generation of Cognitive Computers (kC) powered by non-pretrained and non-preprogramed methodologies towards the ultimate aim of autonomous real-time intelligence generation.

The theme of the AI*AS panel is on the challenges and constraints of training-based AI towards autonomous systems. A recent discovery in intelligence science reveals that abstract intelligence sharable by humans and machines is not simply aggregated from data, information, and knowledge. Instead, it is generated by inductive reasoning. Therefore, a fundamental question is that if AS’ represented by the brain is merely training-based and language driven? How real-time intelligence generation may be implemented beyond classical pretrained (neural networks) and preprogrammed (von Neumann computers)? Therefore, SMC theories play an irreplaceable role for synergizing symbiotic human-machine systems as a generic platform powered by IS and IM. This leads to the contemporary brain-centered Natural Intelligence (NI) embodied by AAI for revealing the fundamental constraints of and potential theoretical approaches to classical training-based AI.

Interactive discussions between the panelists and audience will provide a unique opportunity for young scientists to learn from the outstanding scholars and multidisciplinary experts.

Chair and Panel Moderator

Yingxu Wang

Univ. if Calgary, Canada, FIEEE, FBCS, FI2CICC, FAAIA, FWIF, P.Eng


Fundamental Challenges to AI and Autonomous Systems  – How SMCS may Lead the Autonomous AI (AI*) Wave?     


Edward Tunstel

Motiv Robotics, FIEEE


Next-Level Robotic Intelligence and Autonomy

Vladik Kreinovich

Univ. of Texas at El Paso, FIFSA, FSMIA, FRFSSC


Who’s Afraid of the Big Bad AI: The Robot and the Human Should be Friends

Saeid Nahavandi

Swinburne University of Technology, Australia, FIEEE, FTSE, FIEAust, FIET, CPEng, CEng


Autonomous Vehicles (AVs): Self-actuated, Safe, and Trustworthy Manoeuvres in Autonomous Driving

Robert Kozma

University of Memphis, TN, USA, FIEEE


What do Brains Teach us about Intelligence and Intelligent Systems: How I Learned to Stop Worrying  about AI Taking over Human Intelligence?

Ming Hou

DRDC, Canada


Trust Understanding, Modeling, and Management for a Reliable Human-AI Symbiotic Partnership

Prof. Fred Y. Hadaegh



System-level Autonomy: The Technological Challenges Facing Autonomous Systems for Deep Space Missions

Ljiljana Trajkovic

FIEEE, Simon Fraser University, Canada


Improving Cybersecurity using AI and Machine Learning Models