SCenE - Self-Assessment and Continual Learning on Edge Devices

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Artificial intelligence (AI) systems and machine learning algorithms lay at the heart of modern autonomy in theory but have experienced a bottleneck in expansion into real-world systems. Typically, in the case of real-world environments, AI systems are considered untrustworthy and are lacking the ability to adapt to an ever-changing environment, requiring continuous maintenance and tuning to stay relevant. Most current AI systems are constrained by their knowledge gathered during training and development. In order for a system to be truly intelligent, they must incorporate learning frameworks that are aware of their own limitations, have an expandable knowledge base in case of failure after deployment, and have the capabilities to operate within available energy budgets in a continuous and dynamic real-world environment. The goal for this project is to develop a rigorous and scalable learning framework that will enable the development of data-driven algorithms that can self-assess their performance and continually expand upon their prior knowledge while operating in real-time on a limited energy budget. This work will equally impact academic research and economic development through collaborations with industrial partners as well as local, regional and federal government agencies. The case study examples include healthcare, intelligent transportation systems, surveillance, severe weather and flood monitoring, aviation and rotorcraft safety, agriculture, vegetation, and endangered species monitoring, and smart and connected campus and communities. Collaboration with the Atlantic Cape Community College will serve as a basis to disseminate the research contributions to the next generation of STEM students. The developed algorithms, source code, and hardware configurations will be made available to the public through open-source data-sharing platforms. We aim to tackle the limitations of the current AI systems and learning algorithms, which are based on deterministic and over-confident deep neural networks. The learned parameters of these models are frozen after training and deployed on possibly energy-constrained edge platforms. These models cannot adapt to non-stationary environments resulting in failures in continuously changing environments. The objective of this project is to develop a rigorous, scalable, and open-source learning framework that would facilitate the development and deployment of data-driven algorithms, which can self-assess performance and continually adapt to streaming datasets while operating in real-time on a limited energy budget. We propose a new fundamental approach to machine learning systems that will: (1) provide a theoretical foundation for self-assessment of modern learning algorithms via quantifying confidence in network decisions through the propagation of distribution moments over unknown network parameters, (2) spur the development of self-assessment methods through the monitoring of variance-covariance parameters of the estimated predictive distribution, (3) derive new training methods that allow for algorithms to operate within a given power budget while achieving continual adaptation from streaming datasets through leveraging metrics of kernel importance based on variance-covariance information, and (4) assess the validity of the mathematical derivations and subsequently developed algorithms using benchmark public datasets and real-world applications with our government, industry and academic collaborators.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.