The Massachusetts Institute of Technology (MIT) launched a call papers to examine generative AI and formulate suggestions on its applications. The initial call was widely acclaimed and received 75 submissions, 27 of which were selected for seed funding. Seeing the enthusiasm, MIT President Sally Kornbluth and Provost Cynthia Barnhart announced a second call for proposals, resulting in an additional 53 submissions.
The faculty committee reviewing the proposals chose 16 to receive exploratory funding. These proposals came from interdisciplinary teams of faculty and researchers from all five of MIT’s schools and the MIT Schwarzman College of Computing. The proposals offered insights into potential use cases and impacts of generative AI across many fields and disciplines.
Each of the selected teams will be awarded between $50,000 and $70,000, with the funding used to construct 10-page impact papers. These papers will be disseminated through a publication venue, operated and hosted by the MIT Press as part of the MIT Open Publishing Services program.
The successful papers covered wide-ranging topics such as privacy in generative AI, impact on the creative economy, application in live music performance, and support for the aging population. The studies offered important perspectives on what generative AI’s impact could be, and made actionable recommendations on how it can be harnessed effectively.
A member of the MIT School of Engineering Dean’s Advisory Council, Thomas Tull, who is also a former innovation scholar at the School of Engineering, contributed substantial amounts of funding to aid this endeavor. It is clear that the initiative drew significant interest and showed that researchers across disciplines at MIT recognize the potential transformative effects of generative AI in a multitude of sectors.
By allowing researchers to delve deeply into the implications of generative AI across varied contexts, this call for papers represents a dedicated effort from a leading educational institution to shape the path forward for generative AI. It carries significant weight and provides a sound basis for further investigation, development, and, ultimately, constructive deployment of AI technologies.
