The rise of artificial intelligence (AI) and a branch of AI called machine learning, which focuses on the use of data and algorithms to imitate the way that humans learn, is rapidly changing the way data-intensive scientific discovery is being done.
Data-intensive science is a modern, exploration-centered style of science that heavily relies on advanced computing capabilities and software tools to manipulate and explore massive data sets. The introduction of new and better machine learning techniques is now being used to assist and automate scientific discovery of increasingly complex problems.
“AI research is making major strides,” said Michael E. Papka, a deputy associate laboratory director and supercomputing facility director at the U.S. Department of Energy’s (DOE) Argonne National Laboratory who is also a professor of computer science at the University of Illinois Chicago (UIC). “We are seeing progress in many areas of AI, made not only by new techniques, but especially by new hardware for running computationally intensive AI models.”