UNIVERSITY PARK, Pa. — Researchers from Penn State have received a $900,000 grant to teach computers how to generate original design ideas and then determine if those ideas are technically feasible which, in terms of national security, would give the United States a considerable competitive advantage in technology industries.
The Defense Advanced Research Projects Agency (DARPA) is supporting the 18-month project titled, “Generative Adversarial Networks for Design Exploration and Refinement,” or GANDER, for short.
“We are trying to determine if we can train a computer to do multiple things,” explained Conrad Tucker, associate professor of engineering design and industrial engineering, who is a co-principal investigator (PI) on the project. “First, we want to train a computer to generate novel engineering design ideas, and then we want to train it to determine whether or not those generated ideas make any sense in the real world.”
Michael Yukish, head of the Manufacturing Product and Process Design Department at the Penn State Applied Research Laboratory is the project lead. Tim Simpson, Paul Morrow Professor in Engineering Design and Manufacturing, is a co-PI.
What’s new about this approach to machine learning is that the researchers are moving away from solely teaching a computer how to classify the difference between things in an environment, such as the difference between a car or a lamppost. They are attempting to teach computers to be creative — to have them generate new concepts, which is critical when it comes to engineering design.
The domain of deep learning that the researchers are exploring is called generative adversarial networks (GAN). GANs consist of two competing neural networks: one is generating ideas and the other is discriminating to determine if the idea makes any sense.