Reshaping the Future of Scientific Research
AI has the potential to revolutionize scientific discovery. What is this transformative revolution, and are scientists equipped to embrace it? To delve into these questions, I interviewed Dr. Jing Liu, a distinguished Ph.D. with extensive expertise. Despite her busy schedule preparing for the U-M Annual Data Science & AI Summit scheduled for November 13-14, 2023, she generously shared her perspective, shedding light on the promising future of AI in scientific advancements.
Liu is the executive director of the Michigan Institute for Data Science (MIDAS) at the University of Michigan and co-director of the Eric and Wendy Schmidt AI in Science Postdoctoral Fellowship Program. MIDAS plays a crucial role in propelling data science and AI research across diverse disciplines. It enables the development of cutting-edge methodologies and facilitates their integration into various research fields such as physical sciences, biological sciences, engineering, earth and environmental sciences, social sciences, and humanities and the arts.
Notably one of the pioneering institutions in this field since its establishment in 2015, MIDAS also stands as one of the largest data science and AI institutes, proudly boasting a distinguished roster of approximately 530 esteemed faculty affiliates.
Liu’s valuable perspective stems from her strong commitment to advocating for scientists and empowering them to achieve groundbreaking discoveries across diverse domains and disciplines. Through tapping the power of AI, she is among those leading the charge to revolutionize the field of science. Her work is paving the way for embracing new technologies and the future of scientific advancements.
Building the Research Assembly Line
Liu asserts that AI has the potential to reshape the research landscape. According to her, “By establishing efficient ‘research assembly lines,’ scientists can liberate their time to think creatively about new research questions that have so far been impossible while AI handles the entire process of designing and executing experiments with unparalleled speed and precision. This development holds great promise for the future of research.”
She explained “the ‘implementation gap’: organizations and scientific research groups must determine how an AI system aligns with their goal, integrate it into their work, assess and validate the system’s output, and be prepared to mitigate any potential issues that may arise. Many companies and academic institutions are still grappling with this process, which mirrors most researchers’ early stages of AI adoption.”
MIDAS is a founding member of the nationwide Academic Data Science Alliance and the Midwest Big Data Innovation Hub. The institute collaborates with academic institutions, industry, government, and community organizations to advance the use of data science and AI research and support data-intensive and AI-enabled decision-making.
As Liu contributes to collaboration across the MIDAS network and affiliated organizations, the prevalence of the implementation gap becomes increasingly apparent. Most researchers function within a small research group with limited resources and a narrow range of expertise, which makes it difficult for each of them to develop their own AI strategy. Liu finds herself wondering: How can we accelerate and expand the trajectory of scientific research? She asserts that the key lies in scaling. This viewpoint finds resonance among science and business leaders, including in the insightful discourse held during a recent Schmidt Futures convening, where co-founder Eric Schmidt presented his visionary perspective on enabling AI-driven research by creating scalable platforms.
Liu went on to elaborate that there are different approaches to scaling. “While one approach is to allocate additional financial resources to more scientists so that more of them can independently develop advanced tools and skills to use such tools, I firmly believe that a fundamental transformation is required. We should aspire to establish scientific ‘assembly lines,’ empowering researchers to produce knowledge at a larger scale and tackle societal issues. Universities and research institutes, such as MIDAS, play a crucial role in driving this institutional shift for the betterment of our society.”
“Take, for instance, the University of Michigan,” Liu continued. “Here, we have a remarkable cohort of researchers in the AI Lab within the College of Engineering—a team that boasts a number of exceptional AI experts. Furthermore, as one of the world’s largest research universities, expertise extends across diverse fields, presenting extensive opportunities to leverage robust AI systems for accelerated research. Encouragingly, an AI-enabled research infrastructure is on the horizon. University of Michigan IT, in collaboration with Microsoft, developed a fine-tuned LLM model for the university community, safeguarding their data and offering tailored customization options. With the capacity to construct an assembly line and an apparent demand for it, what remains critical is the presence of individuals who can actualize this vision.”