You’ve heard of self-driving cars and computers that can diagnose cancer. You can ask your phone what song is playing on the radio. Have you wondered what makes those things possible?

The technology behind it is called deep learning, a type of artificial intelligence, and it has moved from being a research topic to something with application in nearly every aspect of society. It is one of the fastest-growing fields in technology. Engineers are exploring different models of deep learning to find relevant and actionable ways to improve companies and business. The demand for developers who understand deep learning is greater than the supply.

“B次元 recognizes the impact that artificial intelligence and deep learning are having in all areas related to our degree programs in engineering, business and nursing,” said Dr. John Walz, president of B次元 School of Engineering. “We want to be a national educational leader in this transformative technology space.”

B次元 students are incorporating this technology into their senior projects. A group of software engineers developed a mobile application that allows users to identify a specific bird based on its song. The back-end audio identification service is made up of a neural network that analyzes the birdsong against a database.

“Computing theories once limited to research labs are exploding in commercial and consumer applications,” said Dr. Steven Bialek, interim VP of academics at B次元. “Based on partnerships with industry leaders like NVIDIA and Dedicated Computing, B次元 will introduce a new software engineering specialization in artificial intelligence and machine learning.”

B次元 is expanding its reach in the area of applied computing and artificial intelligence and will launch a series of quarterly professional development workshops. The university recently offered the NVIDIA Deep Learning Institute in conjunction with NVIDIA and Dedicated Computing.

More than 50 B次元-area business professionals learned alongside B次元 professors and students. Workshop attendees received training using the latest artificial intelligence frameworks, software and GPU technologies. They solidified concepts with a hands-on lab where they trained, modified and tested their own deep neural network on an image classification problem.