“The emergence of big data and advances in machine learning have dramatically accelerated some of the key steps in science. However, many key elements of the scientific process remain largely untouched and untamed by artificial intelligence,” said Honavar. “We believe that we can dramatically accelerate scientific progress, potentially by several orders of magnitude, in some disciplines, by effectively addressing these bottlenecks.”
These elements of the scientific process include generating hypotheses; designing, prioritizing and executing experiments; integrating data, models and simulations across different measurement modalities and scales; drawing inferences and constructing explanations; reconciling scientific arguments of varying uncertainty and provenance; and communicating across disciplines, he added.
According to Honavar, the scientific community understands the need for this effort.
"There is a growing awareness within the scientific community and funding agencies that realizing the promise and potential of data and computing to accelerate science presents a grand challenge for AI," said Honavar. "It also requires the development of algorithmic abstractions of scientific domains, scientific artifacts, and key steps in the scientific process. Addressing this grand challenge would unify many of the subfields of AI, yielding fundamental advances across multiple areas of AI. The infusion of cognitive tools that augment and extend human intellect abilities, into AI mediated human-machine systems and infrastructure will revolutionize collaborative team science.”
The center will be a collaborative, interdisciplinary effort that will connect faculty from multiple colleges and institutes at Penn State. At its launch, CENSAI includes faculty affiliates from nine Penn State colleges, 21 academic departments, and two campuses.
“CENSAI brings together the computational power of AI coupled with the creativity and interdisciplinary collaboration of Penn State researchers,” said Jenni Evans, professor of meteorology and atmospheric science and ICDS director. “CENSAI is a natural fit with the ICDS mission for advancing computational and data intensive methods and infrastructure for science. It represents a revolutionary shift in how we can use AI to realize the potential of the scientific process.”
According to Honavar, Penn State has substantial strengths in AI- and machine learning-related areas, including deep learning, information retrieval and text analytics, knowledge representation, advanced data, and computational infrastructure. The University also has a strong research presence in several scientific disciplines that could potentially benefit from an infusion of AI methods and tools, namely in the life sciences, materials sciences, agriculture, health sciences, cognitive and brain sciences, physical sciences, environmental sciences, and social sciences.
"There are many opportunities for AI researchers to collaborate with biomedical and clinical researchers on developing evidence-based approaches to improving health care, for example, by early identification of individuals at risk, and by designing personalized interventions," said Jennifer Kraschnewski, professor of medicine, public health sciences and pediatrics at Penn State College of Medicine. "I look forward to working with CENSAI to increase collaboration at the interface of AI and biomedical and clinical research at Penn State.”
Anyone interested can learn more about CENSAI here.