In July, MIT President Sally Kornbluth and Provost Cynthia Barnhart issued a call for papers to “articulate effective roadmaps, policy recommendations, and calls for motion across the broad domain of generative AI.”

Over the subsequent month, they received an influx of responses from every school at MIT proposing to explore generative AI’s potential applications and impact across areas starting from climate and the environment to education, health care, companionship, music, and literature.

Now, 27 proposals have been chosen to receive exploratory funding. Co-authored by interdisciplinary teams of school and researchers affiliated with all five of the Institute’s schools and the MIT Schwarzman College of Computing, the proposals represent a sweeping array of perspectives for exploring the transformative potential of generative AI, in each positive and negative directions for society.

“Previously yr, generative AI has captured the general public imagination and raised countless questions on how this rapidly advancing technology will affect our world,” Kornbluth says. “This summer, to assist make clear those questions, we offered our faculty seed grants for essentially the most promising ‘impact papers’ — principally, proposals to pursue intensive research on some aspect of how generative AI will shape people’s life and work. I’m thrilled to report that we received 75 proposals briefly order, across an infinite spectrum of fields and fairly often from interdisciplinary teams. With the seed grants now awarded, I cannot wait to see how our faculty expand our understanding and illuminate the potential impacts of generative AI.”

Each chosen research group will receive between $50,000 and $70,000 to create 10-page impact papers that can be due by Dec. 15. Those papers can be shared widely via a publication venue managed and hosted by the MIT Press and the MIT Libraries.

The papers were reviewed by a committee of 19 faculty representing a dozen departments. Reflecting generative AI’s wide-ranging impact beyond the technology sphere, 11 of the chosen proposals have no less than one writer from the School of Humanities, Arts, and Social Sciences. All submissions were reviewed initially by three members of the committee, with professors Caspar Hare, Dan Huttenlocher, Asu Ozdaglar, and Ron Rivest making final recommendations.

“It was exciting to see the broad and diverse response which the decision for papers generated,” says Ozdaglar, who can also be deputy dean of the MIT Schwarzman College of Computing and the top of the Department of Electrical Engineering and Computer Science. “Our faculty have contributed some truly revolutionary ideas. We hope to capitalize on the present momentum around this topic and to support our faculty in turning these abstracts into impact that’s accessible to broad audiences beyond academia and that will help inform public conversation on this vital area.”

The robust response has already spurred latest collaborations, and an extra call for proposals can be made later this semester to further expand the scope of generative AI research on campus. Most of the chosen proposals act as roadmaps for broad fields of inquiry into the intersection of generative AI and other fields. Indeed, committee members characterised these papers as the start of rather more research.

“Our goal with this call was to spearhead further exciting work for interested by the implications of latest AI technologies and learn how to best develop and use them,” says Dan Huttenlocher, dean of the MIT Schwarzman College of Computing. “We also desired to encourage latest pathways for collaboration and knowledge exchange across MIT.”

Thomas Tull, a member of the MIT School of Engineering Dean’s Advisory Council and a former innovation scholar on the School of Engineering, contributed to the hassle.

“While there isn’t any doubt the long-term implications of AI can be enormous, since it continues to be in its nascent stages, it has been the topic of limitless speculation and countless articles — each positive and negative,” says Tull. “As such, I felt strongly about funding an effort involving a few of the perfect minds within the country to facilitate a meaningful public discourse on this topic and, ideally, help shape how we take into consideration and best use what is probably going the largest technological innovation in our lifetime.”

The chosen papers are:

• “Can Generative AI Provide Trusted Financial Advice?” led by Andrew Lo and Jillian Ross;
• “Evaluating the Effectiveness of AI-Identification in Human-AI Communication,” led by Athulya Aravind and Gabor Brody (Brown University);
• “Generative AI and Research Integrity,” led by Chris Bourg, Sue Kriegsman, Heather Sardis, and Erin Stalberg;
• “Generative AI and Equitable AI Pathway Education,” led by Cynthia Breazeal, Antonio Torralba, Kate Darling, Asu Ozdaglar, George Westerman, Aikaterini Bagiati, and Andres Salazar Gomez;
• “ Label Content Produced by Generative AI,” led by David Rand and Adam Berinsky;
• “Auditing Data Provenance for Large Language Models,” led by Deb Roy and Alex “Sandy” Pentland;
• “Artificial Eloquence: Style, Citation, and the Right to One’s Own Voice within the Age of A.I.,” led by Joshua Brandon Bennett;
• “The Climate and Sustainability Implications of Generative AI,” led by Elsa Olivetti, Vivienne Sze, Mohammad Alizadeh, Priya Donti, and Anantha Chandrakasan;
• “From Automation to Augmentation: Redefining Engineering Design and Manufacturing within the Age of NextGen AI,” led by Faez Ahmed, John Hart, Simon Johnson, and Daron Acemoglu;
• “Advancing Equality: Harnessing Generative AI to Combat Systemic Racism,” led by Fotini Christia, Catherine D’Ignazio, Munzer Dahleh, Marzyeh Ghassemi, Peko Hosoi, and Devavrat Shah;
• “Defining Agency for the Era of Generative AI,” led by Graham M. Jones and Arvind Satyanarayan;
• “Generative AI and K-12 Education,” led by Hal Abelson, Eric Klopfer, Cynthia Breazeal, and Justin Reich;
• “Labor Market Matching,” led by John Horton and Manish Raghavan;
• “Towards Robust, End-to-End Explainable, and Lifelong Learnable Generative AI with Large Population Models,” led by Josh Tenenbaum and Vikash Mansinghka;
• “Implementing Generative AI in U.S. Hospitals,” led by Julie Shah, Retsef Levi, and Kate Kellogg;
• “Direct Democracy and Generative AI,” led by Lily Tsai and Alex “Sandy” Pentland;
• “Learning from Nature to Achieve Material Sustainability: Generative AI for Rigorous Bio-inspired Materials Design,” led by Markus Buehler;
• “Generative AI to Support Young People in Creative Learning Experiences,” led by Mitchel Resnick;
• “Employer Implementation of Generative AI Way forward for Inequality,” led by Nathan Wilmers;
• “The Pocket Calculator, Google Translate, and Chat-GPT: From Disruptive Technologies to Curricular Innovation,” led by Per Urlaub and Eva Dessein;
• “Closing the Execution Gap in Generative AI for Chemicals and Materials: Freeways or Safeguards,” led by Rafael Gomez-Bombarelli, Regina Barzilay, Connor Wilson Coley, Jeffrey Grossman, Tommi Jaakkola, Stefanie Jegelka, Elsa Olivetti, Wojciech Matusik, Mingda Li, and Ju Li;
• “Generative AI within the Era of Alternative ‘Facts,’” led by Saadia Gabriel, Marzyeh Ghassemi, Jacob Andreas, and Asu Ozdaglar;
• “Who Do We Turn into When We Check with Machines? Considering About Generative AI and Artificial Intimacy, the Latest AI,” led by Sherry Turkle;
• “Bringing Employees’ Voices into the Design and Use of Generative AI,” led by Thomas A. Kochan, Julie Shah, Ben Armstrong, Meghan Perdue, and Emilio J. Castilla;
• “Experiment With Microsoft to Understand the Productivity Effect of CoPilot on Software Developers,” led by Tobias Salz and Mert Demirer;
• “AI for Musical Discovery,” led by Tod Machover; and
• “Large Language Models for Design and Manufacturing,” led by Wojciech Matusik.