The work of researchers at the University of Wyoming was featured in a video created by the Science Journal’s video team and was the most viewed entry for the entire year. The video accompanied a special package on artificial intelligence and featured Science Journal’s staff writer Paul Voosen.
In 2017, the Science video team created a record 179 videos on various scientific topics.
“Science Magazine is one of the world’s top academic journals, top science professionals from all over the world access this Journal,” PhD graduate in Water Resource Management, Dr. Jagath Vithanage, said.
The basis for the video, named “A.I. detectives are cracking open the black box of deep learning,” came largely from the work of a team of researchers, two of whom are associated with UW’s Department of Computer Science Associate Professor Jeff Clune and graduate student Anh Nguyen, who now is an assistant professor at Auburn University.
The video explores information from another video, titled “Deep Visualization Toolbox” and a research paper titled, “Understanding neural networks through deep visualization,” both include contributions from Clune and Nguyen, along with Cornell University’s Jason Yosinski and Hod Lipson and California Institute of Technology’s Tom Fuchs. The information was presented at the Deep Learning Workshop of the International Conference on Machine Learning in 2015.
“I am delighted that the Science Journal is covering this important research,” Clune said. “Artificial intelligence and, in particular, deep neural networks will lead to dramatic changes in every economic sector, scientific field and in many cultural areas. It is increasingly being deployed throughout society, despite the fact that we do not know exactly how it works, when it is biased and how to prevent it from being easily manipulated.”
Artificial Neural Networks are based on a collection of connected units or nodes called artificial neurons. Each connection between artificial neurons can transmit a signal from one to another. The artificial neuron that receives the signal can process it and then signal artificial neurons connected to it.
“We still don’t have a global understanding of how Neural Networks work. In order to develop artificial intelligent for practical use, we need to understand how these networks think. That’s why we need more research,” Computer Science graduate Krish Krishnamoorthi said.
Clune said, “Our work is part of an effort to better understand this technology and improve it and we are delighted that the scientific community is so interested in such research. The possible benefits from artificial intelligence are enormous, but so are the potential societal harms. It is, thus, critical to improve our understanding of the technology to harness it as best we can while mitigating its downsides.”
