Hybrid AI for Complex Applications with Scruff

Editor’s note: Avi Pfeffer is a speaker for ODSC East 2023 this May 9th-11th. Be sure to check out his talk, “Hybrid AI for Complex Applications with Scruff,” there!

In this ODSC East preview, the author describes how the Scruff AI modeling framework enables clear and coherent implementation of multiparadigm AI models.

Many different modeling paradigms are needed to build real-world AI applications. You might need a deep neural network to process image data, combined with a probabilistic model of contextual information, and perhaps a physics-based model to describe how the environment operates dynamically. Not only is combining all these different paradigms usually difficult from an engineering point of view, but it’s quite likely that the resulting combination will be incoherent. For example, imagine a fire modeling application that considers weather conditions in both regional and property-level fire ignition and spread. If you don’t correctly account for the fact that the same weather applies in both cases, you might underestimate the risk of severe weather causing significant damage to your property.

In this workshop, I’ll describe Scruff, our modeling framework that enables clear and coherent implementation of multiparadigm AI models. Scruff, which is an open-source package in Julia, is based on the ideas of probabilistic programming. Like probabilistic programming, a model is organized as a sequence of random variables, where each variable is generated from previous variables according to a random process, which is called a stochastic function or sfunc. However, unlike previous probabilistic programming languages, sfuncs do not need to be specified in explicit probabilistic terms. Instead, Scruff defines a set of operators that can be applied to sfuncs, and algorithms are written using these operators, making algorithm implementation independent of sfunc representation. Furthermore, different kinds of sfunc will support different operators, which enables them to serve different roles in a model. For example, a neural network might be used as a recognizer, while an ODE-based physics model might be used as a simulator. The algorithms know how to make use of components playing these different roles.

One of the appealing features of Scruff is its flexible treatment of time in dynamic models. Most existing AI frameworks model time as proceeding in fixed, discrete time steps. In contrast, Scruff allows asynchronous modeling, where variables are only instantiated when you need them. One benefit of this is that you can postpone running an algorithm until an event calls for it. Another benefit is that you can model fast and slowly evolving variables at their appropriate rates.

In the workshop, I’m going to describe a wildfire risk assessment application and show how it works at multiple temporal and spatial scales, integrating data-driven, physics-based, and probabilistic components. I’ll also explain how it avoids the mistake I described earlier in not accounting correctly for the weather. After presenting this example, I’ll help you get started with Scruff and walk you through some simple, hands-on examples.

About the authors:

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.