DARPA awards UB engineers $1 million to ‘teach’ physics to AI systems

The research could improve unmanned aerial vehicles and other complex defense systems

The goal is to provide AI systems, which work within specific frameworks and lack tools to explain their reasoning process, with a broader foundation of knowledge through physics. In theory, this will allow for more streamlined, efficient and adaptable AI systems — ideal traits for defense systems, such as unmanned aerial vehicles (UAVs), which operate in uncontrolled environments.

To address this issue, University at Buffalo engineers have been awarded a $1 million Defense Advanced Research Projects Agency grant to combine physics-based models with conventional, data-driven AI methods.

Artificial intelligence (AI) systems — the uber-smart machines that many predict will someday outpace human intelligence — struggle with the subject too.

Rahul Rai, PhD, associate professor of mechanical and aerospace engineering in UB’s School of Engineering and Applied Sciences. Credit: Douglas Levere, University at Buffalo.

“Unmanned aerial vehicles are trained in collision avoidance. For example, they spot another UAV or a bird and take an action, such as slowing down, to avoid striking that object,” said the grant’s principal investigator Rahul Rai, PhD, associate professor of mechanical and aerospace engineering in UB’s School of Engineering and Applied Sciences.

He continued: “What we’re proposing would give that UAV an understanding into the physics of things like how birds fly. This information, combined with weather and data that other sensors are processing, will provide the UAV with better collision avoidance mechanisms.”

To make that possible, Rai and his team of researchers will integrate physics-based models — these are math-based formulas that explain the world around us, such as Einstein’s E=MC2 — into the algorithms that guide machine learning, deep learning and other data-driven AI systems.

“In a sense, we’re teaching physics to AI systems,” he said.

Because these combined models will provide AI systems with a greater understanding of their surroundings, Rai said it should reduce the amount of data that purely data-driven AI systems require. In turn, that will lead to more efficient and less costly systems, he said.

“The goal is to create hybrid systems that generalize well, which means they’re good at adapting to foreign environments where data may not be readily available,” he said.

Rai is a member of the University at Buffalo Artificial Intelligence Institute, which was launched last September. The institute, which focuses health, medicine and autonomous systems, advances core AI technologies that optimize human-machine partnerships, and it provides complementary tools and skills to understand the societal impact of these technologies.