Hybrid AI for Complex Applications with Scruff


Complex AI applications, such as disaster preparation and mitigation or predicting the effects of climate change on species survival, require multiple paradigms, including data-driven predictive models, physics simulations, and probabilistic models. Hybrid AI is an emerging field that integrates model components from different paradigms in a unified model for complex applications like these. However, existing hybrid AI frameworks are usually ad-hoc, specific to certain configurations of models, and lack explainability, which is vital for real-world applications. We have developed a new hybrid AI framework called Scruff, based on probabilistic programming principles, that provides a coherent, general, and explainable way to build multi-paradigm and multiscale models. Scruff is implemented in Julia and available open source on GitHub.

In this workshop, I will explain the core principles of Scruff and the main programming concepts. I will then demonstrate how we used Scruff to create a tool for wildfire risk assessment and mitigation that includes climate models, historical fire data, and fire propagation simulators. Finally, we will work through a hands-on session of getting up and running with Scruff and implementing and running simple models.

Session Outline:

Module 1: introducing hybrid AI and Scruff and its main concepts
Module 2: demonstrating Scruff through a wildfire risk assessment application
Module 3: downloading, installing, and getting up and running with Scruff (Julia installation is a prerequisite)
Module 4: building and running simple models

Background Knowledge:

Julia; probabilistic models such as Bayes nets; familiarity with probabilistic programming helps, but no specific language is required


Ms. Sanja Cvijic is a Senior Scientist at Charles River Analytics who leads our Probabilistic AI Representations and Reasoning Systems group and has pioneered the application of Scruff to real-world problems in ISR and maintenance. Dr. Cvijic’s research activities are centered around applications of probabilistic programming to condition monitoring, fault detection and prediction systems. She developed a prognostic health management tool for assessing health and status of power transformers in Scruff. She also developed a probabilistic tool in Scruff for improved space domain awareness for assessing risks to satellites in space. Previously, she worked as a Director of Software and a Consultant in power industry at New Electricity Transmission Software Solutions. She earned her Doctoral degree in Electrical and Computer Engineering, Power Systems, at Carnegie Mellon University in 2013. She earned her Bachelors in Electrical and Computer Engineering at the University of Belgrade, Serbia, in 2008.

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