Accelerating AI, Machine Learning in Research
With a $5.1 million grant from the National Science Foundation (NSF), Case Western Reserve University and partners at the University of Cincinnati (UC) and Ohio Supercomputer Center (OSC) will work to optimize the use of artificial intelligence (AI) and machine learning by making trained experts available to researchers statewide.
The Ohio effort is part of a broader plan by the NSF to bring AI and machine learning to researchers at as many academic institutions as possible—and to ensure the technology is reliable, understandable and valuable.
“Everyone wants to use AI and machine learning right now, but not everyone is an expert or knows what it can actually do for them,” said project leader Vipin Chaudhary, the Kevin J. Kranzusch Professor and chair of the Department of Computer and Data Sciences at the Case School of Engineering.
“We’re going to provide the experts to help researchers from various disciplines understand and integrate the latest AI and machine learning capabilities into their work. Think of them both as evangelists for using the technology and trainers for using it effectively.”
While AI and machine learning are often used interchangeably, AI is the broader category, referring to how computers can emulate human thought and tasks in real-world environments. Machine learning involves the technology that allows systems to identify patterns, make accurate decisions, and improves with experience.
The NSF initiative and financial support illustrates the critical need for both a reliable cyberinfrastructure and an well-trained AI and machine-learning workforce to advise and teach novice AI users, Chaudhary said.
The CWRU-led team will recruit and hire four AI experts—two on staff and on site at CWRU and one each at the other institutions—to provide tailored mentoring and training for AI users. Those users will include researchers at smaller community colleges and Historically Black Colleges and Universities.
These AI experts will conduct research, write technical papers, deliver tutorials at major conferences/workshops and create training materials. Initial projects will focus on materials data science, agricultural data science and biomedical engineering.