{"id":6799,"date":"2023-09-18T19:00:00","date_gmt":"2023-09-18T19:00:00","guid":{"rendered":"https:\/\/www.aiproblog.com\/index.php\/2023\/09\/18\/mit-scholars-awarded-seed-grants-to-probe-the-social-implications-of-generative-ai\/"},"modified":"2023-09-18T19:00:00","modified_gmt":"2023-09-18T19:00:00","slug":"mit-scholars-awarded-seed-grants-to-probe-the-social-implications-of-generative-ai","status":"publish","type":"post","link":"https:\/\/www.aiproblog.com\/index.php\/2023\/09\/18\/mit-scholars-awarded-seed-grants-to-probe-the-social-implications-of-generative-ai\/","title":{"rendered":"MIT scholars awarded seed grants to probe the social implications of generative AI"},"content":{"rendered":"<p>Author: MIT News<\/p>\n<div>\n<p>In July, MIT President Sally Kornbluth and Provost Cynthia Barnhart issued a call for papers to \u201carticulate effective roadmaps, policy recommendations, and calls for action across the broad domain of generative AI.\u201d<\/p>\n<p>Over the next month, they received an influx of responses from every school at MIT proposing to explore generative AI\u2019s potential applications and impact across areas ranging from climate and the environment to education, health care, companionship, music, and literature.<\/p>\n<p>Now, 27 proposals have been selected to receive exploratory funding. Co-authored by interdisciplinary teams of faculty and researchers affiliated with all five of the Institute\u2019s 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 both positive and negative directions for society.<\/p>\n<p>\u201cIn the past year, generative AI has captured the public imagination and raised countless questions about how this rapidly advancing technology will affect our world,\u201d Kornbluth says. \u201cThis summer, to help shed light on those questions, we offered our faculty seed grants for the most promising \u2018impact papers\u2019 \u2014 basically, proposals to pursue intensive research on some aspect of how generative AI will shape people\u2019s life and work. I\u2019m thrilled to report that we received 75 proposals in short order, across an enormous spectrum of fields and very 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.\u201d<\/p>\n<p>Each selected research group will receive between $50,000 and $70,000 to create 10-page impact papers that will be due by Dec. 15. Those papers will be shared widely via a publication venue managed and hosted by the MIT Press and the MIT Libraries.<\/p>\n<p>The papers were reviewed by a committee of 19 faculty representing a dozen departments. Reflecting generative AI\u2019s wide-ranging impact beyond the technology sphere, 11 of the selected proposals have at least one author 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.<\/p>\n<p>\u201cIt was exciting to see the broad and diverse response which the call for papers generated,\u201d says Ozdaglar, who is also deputy dean of the MIT Schwarzman College of Computing and the head of the Department of Electrical Engineering and Computer Science. \u201cOur faculty have contributed some truly innovative ideas. We are hoping to capitalize on the current momentum around this topic and to support our faculty in turning these abstracts into impact that is accessible to broad audiences beyond academia and that can help inform public conversation in this important area.\u201d<\/p>\n<p>The robust response has already spurred new collaborations, and an additional call for proposals will be made later this semester to further expand the scope of generative AI research on campus. Many of the selected proposals act as roadmaps for broad fields of inquiry into the intersection of generative AI and other fields. Indeed, committee members characterized these papers as the beginning of much more research.<\/p>\n<p>\u201cOur goal with this call was to spearhead further exciting work for thinking about the implications of new AI technologies and how to best develop and use them,\u201d says Dan Huttenlocher, dean of the MIT Schwarzman College of Computing. \u201cWe also wanted to encourage new pathways for collaboration and information exchange across MIT.\u201d<\/p>\n<p>Thomas Tull, a member of the MIT School of Engineering Dean\u2019s Advisory Council and a former innovation scholar at the School of Engineering, contributed to the effort.<\/p>\n<p>\u201cWhile there is no doubt the long-term implications of AI will be enormous, because it is still in its nascent stages, it has been the subject of endless speculation and countless articles \u2014 both positive and negative,\u201d says Tull. \u201cAs such, I felt strongly about funding an effort involving some of the best minds in the country to facilitate a meaningful public discourse on this topic and, ideally, help shape how we think about and best use what is likely the biggest technological innovation in our lifetime.\u201d<\/p>\n<p>The selected papers are:<\/p>\n<ul>\n<li>\u201cCan Generative AI Provide Trusted Financial Advice?\u201d led by Andrew Lo and Jillian Ross;<\/li>\n<li>\u201cEvaluating the Effectiveness of AI-Identification in Human-AI Communication,\u201d led by Athulya Aravind and Gabor Brody (Brown University);<\/li>\n<li>\u201cGenerative AI and Research Integrity,\u201d led by Chris Bourg, Sue Kriegsman, Heather Sardis, and Erin Stalberg;<\/li>\n<li>\u201cGenerative AI and Equitable AI Pathway Education,\u201d led by Cynthia Breazeal, Antonio Torralba, Kate Darling, Asu Ozdaglar, George Westerman, Aikaterini Bagiati, and Andres Salazar Gomez;<\/li>\n<li>\u201cHow to Label Content Produced by Generative AI,\u201d led by David Rand and Adam Berinsky;<\/li>\n<li>\u201cAuditing Data Provenance for Large Language Models,\u201d led by Deb Roy and Alex \u201cSandy\u201d Pentland;<\/li>\n<li>\u201cArtificial Eloquence: Style, Citation, and the Right to One\u2019s Own Voice in the Age of A.I.,\u201d led by Joshua Brandon Bennett;<\/li>\n<li>\u201cThe Climate and Sustainability Implications of Generative AI,\u201d led by Elsa Olivetti, Vivienne Sze, Mohammad Alizadeh, Priya Donti, and Anantha Chandrakasan;<\/li>\n<li>\u201cFrom Automation to Augmentation: Redefining Engineering Design and Manufacturing in the Age of NextGen AI,\u201d led by Faez Ahmed, John Hart, Simon Johnson, and Daron Acemoglu;<\/li>\n<li>\u201cAdvancing Equality: Harnessing Generative AI to Combat Systemic Racism,\u201d led by Fotini Christia, Catherine D\u2019Ignazio, Munzer Dahleh, Marzyeh Ghassemi, Peko Hosoi, and Devavrat Shah;<\/li>\n<li>\u201cDefining Agency for the Era of Generative AI,\u201d led by Graham M. Jones and Arvind Satyanarayan;<\/li>\n<li>\u201cGenerative AI and K-12 Education,\u201d led by Hal Abelson, Eric Klopfer, Cynthia Breazeal, and Justin Reich;<\/li>\n<li>\u201cLabor Market Matching,\u201d led by John Horton and Manish Raghavan;<\/li>\n<li>\u201cTowards Robust, End-to-End Explainable, and Lifelong Learnable Generative AI with Large Population Models,\u201d led by Josh Tenenbaum and Vikash Mansinghka;<\/li>\n<li>\u201cImplementing Generative AI in U.S. Hospitals,\u201d led by Julie Shah, Retsef Levi, and Kate Kellogg;<\/li>\n<li>\u201cDirect Democracy and Generative AI,\u201d led by Lily Tsai and Alex \u201cSandy\u201d Pentland;<\/li>\n<li>\u201cLearning from Nature to Achieve Material Sustainability: Generative AI for Rigorous Bio-inspired Materials Design,\u201d led by Markus Buehler;<\/li>\n<li>\u201cGenerative AI to Support Young People in Creative Learning Experiences,\u201d led by Mitchel Resnick;<\/li>\n<li>\u201cEmployer Implementation of Generative AI Future of Inequality,\u201d led by Nathan Wilmers;<\/li>\n<li>&#8220;The Pocket Calculator, Google Translate, and Chat-GPT: From Disruptive Technologies to Curricular Innovation,\u201d led by Per Urlaub and Eva Dessein;<\/li>\n<li>\u201cClosing the Execution Gap in Generative AI for Chemicals and Materials: Freeways or Safeguards,\u201d 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;<\/li>\n<li>\u201cGenerative AI in the Era of Alternative \u2018Facts,\u2019\u201d led by Saadia Gabriel, Marzyeh Ghassemi, Jacob Andreas, and Asu Ozdaglar;<\/li>\n<li>\u201cWho Do We Become When We Talk to Machines? Thinking About Generative AI and Artificial Intimacy, the New AI,\u201d led by Sherry Turkle;<\/li>\n<li>\u201cBringing Workers\u2019 Voices into the Design and Use of Generative AI,\u201d led by Thomas A. Kochan, Julie Shah, Ben Armstrong, Meghan Perdue, and Emilio J. Castilla;<\/li>\n<li>\u201cExperiment With Microsoft to Understand the Productivity Effect of CoPilot on Software Developers,\u201d led by Tobias Salz and Mert Demirer;<\/li>\n<li>\u201cAI for Musical Discovery,\u201d led by Tod Machover; and<\/li>\n<li>\u201cLarge Language Models for Design and Manufacturing,\u201d led by Wojciech Matusik.<\/li>\n<\/ul>\n<\/div>\n<p><a href=\"https:\/\/news.mit.edu\/2023\/mit-scholars-awarded-seed-grants-generative-ai-0918\">Go to Source<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Author: MIT News In July, MIT President Sally Kornbluth and Provost Cynthia Barnhart issued a call for papers to \u201carticulate effective roadmaps, policy recommendations, and [&hellip;] <span class=\"read-more-link\"><a class=\"read-more\" href=\"https:\/\/www.aiproblog.com\/index.php\/2023\/09\/18\/mit-scholars-awarded-seed-grants-to-probe-the-social-implications-of-generative-ai\/\">Read More<\/a><\/span><\/p>\n","protected":false},"author":1,"featured_media":473,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_bbp_topic_count":0,"_bbp_reply_count":0,"_bbp_total_topic_count":0,"_bbp_total_reply_count":0,"_bbp_voice_count":0,"_bbp_anonymous_reply_count":0,"_bbp_topic_count_hidden":0,"_bbp_reply_count_hidden":0,"_bbp_forum_subforum_count":0,"footnotes":""},"categories":[24],"tags":[],"_links":{"self":[{"href":"https:\/\/www.aiproblog.com\/index.php\/wp-json\/wp\/v2\/posts\/6799"}],"collection":[{"href":"https:\/\/www.aiproblog.com\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.aiproblog.com\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.aiproblog.com\/index.php\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.aiproblog.com\/index.php\/wp-json\/wp\/v2\/comments?post=6799"}],"version-history":[{"count":0,"href":"https:\/\/www.aiproblog.com\/index.php\/wp-json\/wp\/v2\/posts\/6799\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.aiproblog.com\/index.php\/wp-json\/wp\/v2\/media\/470"}],"wp:attachment":[{"href":"https:\/\/www.aiproblog.com\/index.php\/wp-json\/wp\/v2\/media?parent=6799"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.aiproblog.com\/index.php\/wp-json\/wp\/v2\/categories?post=6799"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.aiproblog.com\/index.php\/wp-json\/wp\/v2\/tags?post=6799"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}