Academic Talk: Edge AI Services and Foundation Models for Internet of Things Applications

Abstract

Advances in self-supervised AI revolutionized modern machine intelligence, but important challenges remain when applying these solutions in IoT contexts - specifically, on lower-end distributed embedded devices with multimodal specialized sensors, where ample training data are not readily available. The talk discusses challenges in offering self-supervised machine intelligence services to support distributed embedded sensing applications. The intersection of IoT applications, real-time requirements, distribution challenges, and self-supervised AI motivates several important research directions. For example, how to adapt self-supervised training pipelines to the embedded sensing domain? Can one develop foundation models for IoT that offer extended inference capabilities from multimodal time-series data? How to endow these models with an understanding of space (and spatial signal propagation) in order for them to reconstruct the state of the physical environment from multiple distributed sensor observations? How to overcome the challenge of data scarcity when it comes to training such AI models with specialized sensor data that are not as widely available as text and images? The paper addresses the above questions and presents initial empirical results on using the answers to train small foundation models for embedded sensor data.

Biography

Tarek Abdelzaher received his Ph.D. in Computer Science from the University of Michigan in 1999. He is currently a Sohaib and Sara Abbasi Professor and Willett Faculty Scholar at the Department of Computer Science, the University of Illinois at Urbana Champaign. He has authored/coauthored more than 300 refereed publications in edge AI, IoT, real-time computing, sensor networks, and control. He served as an Editor-in-Chief of the Journal of Real-Time Systems, and has served as Associate Editor of the IEEE Transactions on Mobile Computing, IEEE Transactions on Parallel and Distributed Systems, IEEE Embedded Systems Letters, the ACM Transaction on Sensor Networks, and the Ad Hoc Networks Journal, among others. Abdelzaher's research interests lie broadly in understanding and influencing performance and temporal properties of networked embedded, social and software systems in the face of increasing complexity, distribution, and degree of interaction with an external physical environment. Tarek Abdelzaher is a recipient of the IEEE Outstanding Technical Achievement and Leadership Award in Real-time Systems (2012), the Xerox Award for Faculty Research (2011), as well as over a dozen best paper awards. He is a fellow of IEEE and ACM.