By April Hunt
Emory Report
If you’ve ever told Siri to call your friend Bob and she answers with, “Calling cops,” you’ve seen the instability of artificial intelligence (AI) in action.
Those mistakes are the limitation of the AI technology known as deep learning. They arise from the design of the deep neural network, as well as the network’s “training,” which applies mathematical optimization methods to massive amounts of data rather than hand-crafting rules to accomplish a specific task.
Emory mathematician Lars Ruthotto has dedicated his research to modeling and solving such 21st century problems with the innovative use of differential equations that date back to the late 1600s. The National Science Foundation has rewarded his efforts with a CAREER Award, its most prestigious honor for junior faculty.
Put simply, Ruthotto is pioneering a new field — combining applied math, engineering and computer science — that applies the logic of differential equations to refine the chaos of deep learning.
“Focusing on this research question can impact specific areas of deep learning now as well as emerging technology,” says Ruthotto, an assistant professor in mathematics and computer science. “It’s uncharted territory, and my students and I will be at the forefront exploring it.”
The award is recognition of the new knowledge Ruthotto and his students are creating in the emerging field and also a hint of what’s to come, says Vaidy Sunderam, chair of Emory's Department of Math and Computer Science.
“This grant establishes Emory as a research and education pioneer in innovative methods for robust deep learning, a key technology in the coming AI decade,” Sunderam says.
Read more in Emory Report.
Related:
Emory team vies for best social bot via Amazon's Alexa Prize
CDC funds Emory project to automate analysis of mixed strains of antibiotic-resistant bacteria