Universities today face many challenges. With today’s market shifts, universities must adapt. Today’s key issues include economic downtrends hitting endowments, enrollment challenges due to rising tuition, and competition from third-party online education and demographic shifts leading to a shrinking pool of new students. Meanwhile, brain drain sees many top-performing scientists elect to enter the private sector over academia, with long term implications on academia’s ability to drive the next generation of basic discoveries from which applications will rise. As the AI and biomed revolutions continue to advance, these challenges will only grow – especially in the sciences.
One promising approach is to enhance mechanisms for translational science that can be used in real-world innovations and applications. This shift will also inevitably have an impact on the funding and donations that universities count on to make their research possible, as private industry and other organizations with real-world impact are also racing for funding.
Many universities, in fact, have come to this realization, and opened translational research hubs for hard sciences, including current market-leading areas like AI and biomedicine. Rather than simply conducting basic research with the aim of publishing scholarly articles, the approach of translational research hubs is to – at a very early stage – figure out how emerging research can be further developed and used to solve real-life problems. Translational programs often entail the development of interdisciplinary hubs – intra-university collectives that promote relationships and collaborations between academic researchers and with commercial industry players. They also make it possible to utilize the institution’s significant resources, including labs doing basic research, theoretical papers, student projects, lecturers, and government and industry relations in solving those problems. Such programs, specifically for translational AI research, have been established at leading U.S. universities like Stanford University, the University of Pennsylvania, and Northwestern University. They are also popping up globally in hotspots that include the UK, Israel, and Singapore.
Such an approach is particularly important in areas like medicine and healthcare, which are quickly seeking to incorporate AI, Big Data, computational technology, and basic research in a wide variety of fields to better prevent and treat cancer, heart disease, and congenital and infectious diseases, and other global health threats.
In effect, university hubs that embrace this approach, act as very early-stage tech incubators, similar to those run by private sector companies like Microsoft or IBM. The researchers involved in these hubs enjoy unprecedented access to research from other faculties they might not have otherwise connected with, institutional and personal connections with the private sector, and academic and industry mentorship for developing commercial business plans.
In addition to helping attract students and researchers, these hubs also offer donors and universities exciting new funding opportunities in the form of grants and impact investments from private, institutional, or government-issued funds. And when researchers develop practical solutions, new doors open for commercial sales, licensing and acquisitions that are critical to produce and market solutions at scale. There is often also a feedback or trickle-down effect; as the well-documented success of such translational programs in bringing solutions to the real world—and the increased awareness of the crucial role of basic sciences in these applications— subsequently drives more direct funding to basic science. Increasing funding for basic science is also essential to the future of universities, as even as translational programs and applications grow, basic science will remain the main focus and heart of academic science.
This approach does come with challenges. Some administrators and lecturers at universities might want their institutions to think twice before embarking on this path, as it is significantly different from the traditionally-perceived non-commercial role of academia. When implementing translational science programs, universities should ensure academic freedom and room for other additional exploration or research continues to exist. This can often be accomplished by including only specific projects, with a clear and well-defined scope, rather than entire departments or all of an individual’s work, in translational science programs.
There are also practical questions to address, such as: Should the university own the solutions – and the patents – that emerge from this process? How much of a role should private enterprise play in this process? Can, or should, universities work with outside organizations whose worldview may not match that of the institution? These are, of course, all issues that each university needs to address on its own. Yet, there’s no question that universities can use these types of hubs to make themselves more relevant in today’s digital technology era.
Collaboration between universities and private companies, in fact, have been responsible for important life-saving initiatives. Many of the earliest automobile crash tests were conducted at Cornell University, in cooperation with leading insurance companies. The results led to the development of far safer cars, essentially inventing the seat belt, among other features. Those innovations went on to save many lives in subsequent years. In today’s environment, similar programs in the area of healthcare and AI – especially if designed to be cooperative and ongoing – are sure to save many more lives in the future. Universities have the resources to develop those solutions, and the private sector has the resources and engagement to bring them to the public. It’s a synergy that can make an important difference in the lives of many people.
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