Edge Computing has been a heated topic in the last decade with many buzz words such as edge cloud and edge AI. On one hand, artificial intelligence and machine learning continue to enjoy its initial success in many business, science and engineering applications. On the other hand, 5G and fast networks are fueling many initiatives of the 2020, ranging from Internet of Things, self-driving cars, smart cities to smart planet. This panel consists of top notch research experts in cloud computing, edge computing and mobile and wireless communication. They will share their vision and perspectives on how edge AI and edge computing will transform collaboration at edge of the Internet in the presence of 5G, Cloud, Big Data-powered artificial intelligence and machine learning.

Big data powered artificial Intelligence and machine Learning have paved way for accelerating scientific discoveries and innovations and creating next generation smart and intelligent systems and applications. However, as we enjoy more advances in AI and ML, there is a growing concern on whether and to what extent machine learning can be private, secure and trusted. This panel consists of top notch security research experts. They will share their vision and perspectives on the security, privacy and trust issues and challenges and debate on the following research questions: can machine intelligence be learned privately? what types of security guarantee the AI and ML powered intelligent systems should provide? how much can we trust the machine intelligence learned from statistical models and algorithms?

Artificial intelligence and machine learning have enjoyed unprecedented advances in business, science and engineering applications, including self-driving cars, smart cities, and cyber manufacturing. At the same time, there are increasing debates on the relationships between human intelligence and artificial machine intelligence. Some debates argue the importance of putting human in the loop of machine learning to ensure fairness, accountability, and transparency, while other debates conjecture that machine intelligence may supersede human intelligence and will eventually dominate and control the human society. This panel consists of top notch AI, ML and data mining research experts. They will share their vision and perspectives on the intertwined relationships between machine intelligence and human intelligence from multiple data mining and machine learning subareas, including natural language understanding, behavior modeling and reasoning, graph mining, network representation learning, mobile mining, social media and crowd mining.

Federated Learning, Multi-agent systems and Distributed AI are buzz words that have been revised recently with the advances in cloud data centers, machine learning and AI as a service, and AI on the Edge. Federated learning refers to technologies and learning algorithms that can construct cognitive learner models from a collection of independent learners, representing autonomous organizations or agents. Multi-agent systems typically refer to systems with multiple agents interacting in a spatially and/or temporally constrained environment. However, multi-agent systems tend to focus on multiple software agents and their environment, whereas distributed AI consists of multiple types of agents, ranging from robots, software agents, humans or human teams, to any combination of human, robot, software-agent teams.

This panel consists of experts from industry in cloud data centers, cognitive computing, cyber manufacturing, distributed AI, distributed systems, and multi-agent systems. This industrial panel will present industrial perspectives on Federative Learning and Distributed AI, leveraging the decades research on multi-agent systems, including interesting research and development problems, potential killer applications, and open research challenges ahead.