Recently, the Computing Research Association – Industry (CRA-I) held a dynamic roundtable event on “Generative AI (GenAI) for Research and Science,” bringing together industry leaders, researchers, and experts to delve into the transformative potential of GenAI, a subset of AI that generates new content by learning data patterns, across various scientific disciplines.

CRA-I Council member Elizabeth Bruce from Microsoft moderated the roundtable, and she emphasized its significance in automating creative processes and its broad applicability across sectors. The panelists were Travis Johnson (Director of Bioinformatics at the Indiana BioSciences Research Institute), Jing Liu (Executive Director of the Michigan Institute for Data Science), Vijay Murugesan (Staff Scientist at Pacific Northwest National Lab), and Neil Thompson (Director of the MIT FutureTech Lab). 

Johnson shared insights into how GenAI is revolutionizing drug discovery by generating new molecules based on existing data, expediting the identification of novel drug targets. Liu highlighted the potential of GenAI in facilitating interdisciplinary research and enhancing the scale of scientific endeavors. She also emphasized its role in distinguishing between valuable research and spurious results. Murugesan discussed a collaborative project with Microsoft leveraging GenAI to accelerate materials discovery for battery technology. He outlined how AI-driven screening of millions of materials drastically reduced the time and resources required for research. Thompson provided an economic perspective on the productivity gains enabled by GenAI. He underscored its role in empowering scientists to leverage computation more effectively and its potential to revolutionize scientific understanding through data-driven exploration.

At the end of the roundtable, Bruce asked the panelists what advice they would give young researchers today. Thompson initiated the final segment with sage advice from economist Hal Varian, emphasizing the importance of identifying areas complementary to emerging technologies like GenAI. He encouraged young researchers to focus on domains that can leverage AI advancements effectively, such as material science and biology, and to continuously adapt and evolve their research focus in response to technological advancements. Echoing Thompson’s sentiments, Murugesan underscored the significance of adaptability in the rapidly evolving research landscape. He highlighted the transformative impact of GenAI on traditional research paradigms and emphasized the need for researchers to stay agile and adaptable in their thinking and research focus to remain relevant and impactful. Liu shared a poignant reflection from her postdoctoral mentor, emphasizing the importance of mindful contribution to science amidst technological advancements. She encouraged young researchers to reflect on their research goals and aspirations, prioritizing rigor and meaningful contributions to scientific knowledge while leveraging evolving tools and methodologies. Finally, Johnson concluded the discussion by emphasizing the importance of responsible AI utilization in research. While advocating for the use of GenAI as a powerful tool for hypothesis generation and data analysis, he cautioned against over-reliance on AI-generated insights. Johnson urged young researchers to maintain expertise in their respective fields while leveraging AI as a supplementary tool to enhance research outcomes.

Overall, the CRA-I roundtable served as a forum for thought-provoking dialogue and collaboration, shedding light on the transformative impact of Generative AI in driving scientific innovation. See the full recording here.