In July, MIT President Sally Kornbluth and Dean Cynthia Barnhart issued a call for papers to “articulate effective roadmaps, policy recommendations, and calls to action in the broad field of generative AI “.
Over the next month, they received an influx of responses from all MIT schools offering to explore the potential applications and impact of generative AI in areas ranging from climate and environment to education , health care, companionship, music and literature.
Today, 27 proposals have been selected to receive exploratory funding. Co-authored by interdisciplinary teams of faculty and researchers affiliated with the Institute’s five schools and the MIT Schwarzman College of Computing, the proposals represent a broad range of perspectives for exploring the transformative potential of generative AI, in directions both positive and negative for society.
“Over the past year, generative AI has captured the public imagination and raised countless questions about how this rapidly evolving technology will affect our world,” Kornbluth says. “This summer, to help shed light on these questions, we offered our faculty seed grants for the most promising “impact papers” – essentially, proposals to pursue intensive research into some aspect of how generative AI will shape people’s lives and work. I am delighted to report that we have received 75 proposals in a short time, across a wide range of fields and very often from interdisciplinary teams. With seed grants now awarded, I look forward to seeing how our faculty will expand our understanding and shine a light on the potential impacts of generative AI.
Each selected research group will receive between $50,000 and $70,000 to create impactful 10-page articles due by December 15. These articles will be shared widely through a publishing venue managed and hosted by MIT Press and the MIT Libraries.
The articles were reviewed by a committee of 19 professors representing a dozen departments. Reflecting the far-reaching impact of generative AI beyond the technological sphere, 11 of the selected proposals have at least one author from the School of Humanities, Arts and Social Sciences. All submissions were initially reviewed by three committee members, with Professors Caspar Hare, Dan Huttenlocher, Asu Ozdaglar and Ron Rivest making final recommendations.
“It was exciting to see the broad and diverse response generated by the call for papers,” said Ozdaglar, who is also assistant dean of the MIT Schwarzman College of Computing and head of the Department of Electrical Engineering and Computer Science. “Our teachers brought truly innovative ideas. We hope to capitalize on the current momentum around this topic and help our faculty transform these summaries into impact that is accessible to a broad audience beyond academia and that can help inform public debate in this important area.
This robust response has already spurred new collaborations, and an additional call for proposals will be issued later this semester to further expand the scope of generative AI research on campus. Many of the selected proposals serve as roadmaps for broad areas of research at the intersection of generative AI and other fields. Indeed, committee members called these papers the beginning of much more in-depth research.
“Our goal with this call was to launch other exciting work to think about the implications of new AI technologies and how to best develop and use them,” says Dan Huttenlocher, dean of the MIT Schwarzman College of Computing. “We also wanted to encourage new avenues of collaboration and information exchange within MIT.”
Thomas Tull, a member of the MIT School of Engineering Dean’s Advisory Council and former innovation researcher at the School of Engineering, contributed to this effort.
“While there is no doubt that the long-term implications of AI will be enormous, as it is still in its infancy, it has been the subject of endless speculation and countless articles – both positive and negative,” says Tull. “As such, I felt strongly about funding an effort involving some of the best minds in the country to facilitate meaningful public discourse on this topic and, ideally, help shape the way we think and best use what is probably the greatest technological innovation in our lives.”
The selected articles are:
- “Can Generative AI Provide Reliable Financial Advice? directed by Andrew Lo and Jillian Ross;
- “Assessing AI Identification Effectiveness 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 and Equitable AI Education,” led by Cynthia Breazeal, Antonio Torralba, Kate Darling, Asu Ozdaglar, George Westerman, Aikaterini Bagiati, and Andres Salazar Gomez;
- “How to Label Generative AI-Produced Content,” 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, Quotation, and the Right to One’s Own Voice in the Age of AI,” edited by Joshua Brandon Bennett;
- “The Implications of Generative AI for Climate and Sustainability,” led by Elsa Olivetti, Vivienne Sze, Mohammad Alizadeh, Priya Donti and Anantha Chandrakasan;
- “From Automation to Augmentation: Redefining Engineering Design and Manufacturing in the NextGen AI Era,” 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 Generative AI Era,” 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;
- “Labour Market Adequacy,” led by John Horton and Manish Raghavan;
- “Towards Robust, End-to-End Explainable, Lifelong Learnable Generative AI with Large-Population Models,” led by Josh Tenenbaum and Vikash Mansinghka;
- “Implementing Generative AI in US 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 Design of Bio-Inspired Materials,” led by Markus Buehler;
- “Generative AI to Support Young People in Creative Learning Experiences,” led by Mitchel Resnick;
- “Employer Implementation of Generative AI for the Future of Inequality,” led by Nathan Wilmers;
- “The pocket calculator, Google Translate and Chat-GPT: from disruptive technologies to curricular innovation”, directed by Per Urlaub and Eva Dessein;
- “Bridging the Execution Gap in Generative AI for Chemicals and Materials: Highways or Guarantees,” 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 in the Age of Alternative “Facts,” led by Saadia Gabriel, Marzyeh Ghassemi, Jacob Andreas, and Asu Ozdaglar;
- “Who do we become when we talk to machines? Reflecting on Generative AI and Artificial Intimacy, the New AI,” led by Sherry Turkle;
- “Elevating Worker Voices in 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 effect of CoPilot on software developer productivity,” led by Tobias Salz and Mert Demirer;
- “AI for Music Discovery,” led by Tod Machover; And
- “Great Language Models for Design and Manufacturing,” led by Wojciech Matusik.