The MIT Stephen A. Schwarzman College of Computing has awarded seed grants to seven projects which can be exploring how artificial intelligence and human-computer interaction might be leveraged to boost modern work spaces to realize higher management and better productivity.
Funded by Andrew W. Houston ’05 and Dropbox Inc., the projects are intended to be interdisciplinary and convey together researchers from computing, social sciences, and management.
The seed grants can enable the project teams to conduct research that leads to larger endeavors on this rapidly evolving area, in addition to construct community around questions related to AI-augmented management.
The seven chosen projects and research leads include:
“LLMex: Implementing Vannevar Bush’s Vision of the Memex Using Large Language Models,” led by Patti Maes of the Media Lab and David Karger of the Department of Electrical Engineering and Computer Science (EECS) and the Computer Science and Artificial Intelligence Laboratory (CSAIL). Inspired by Vannevar Bush’s Memex, this project proposes to design, implement, and test the concept of memory prosthetics using large language models (LLMs). The AI-based system will intelligently help a person keep track of vast amounts of knowledge, speed up productivity, and reduce errors by robotically recording their work actions and meetings, supporting retrieval based on metadata and vague descriptions, and suggesting relevant, personalized information proactively based on the user’s current focus and context.
“Using AI Agents to Simulate Social Scenarios,” led by John Horton of the MIT Sloan School of Management and Jacob Andreas of EECS and CSAIL. This project imagines the power to simply simulate policies, organizational arrangements, and communication tools with AI agents before implementation. Tapping into the capabilities of contemporary LLMs to function a computational model of humans makes this vision of social simulation more realistic, and potentially more predictive.
“Human Expertise within the Age of AI: Can We Have Our Cake and Eat it Too?” led by Manish Raghavan of MIT Sloan and EECS, and Devavrat Shah of EECS and the Laboratory for Information and Decision Systems. Progress in machine learning, AI, and in algorithmic decision aids has raised the prospect that algorithms may complement human decision-making in a wide selection of settings. Somewhat than replacing human professionals, this project sees a future where AI and algorithmic decision aids play a job that’s complementary to human expertise.
“Implementing Generative AI in U.S. Hospitals,” led by Julie Shah of the Department of Aeronautics and Astronautics and CSAIL, Retsef Levi of MIT Sloan and the Operations Research Center, Kate Kellog of MIT Sloan, and Ben Armstrong of the Industrial Performance Center. Lately, studies have linked an increase in burnout from doctors and nurses in the USA with increased administrative burdens related to electronic health records and other technologies. This project goals to develop a holistic framework to review how generative AI technologies can each increase productivity for organizations and improve job quality for staff in health care settings.
“Generative AI Augmented Software Tools to Democratize Programming,” led by Harold Abelson of EECS and CSAIL, Cynthia Breazeal of the Media Lab, and Eric Klopfer of the Comparative Media Studies/Writing. Progress in generative AI over the past yr is fomenting an upheaval in assumptions about future careers in software and deprecating the role of coding. This project will stimulate an identical transformation in computing education for individuals who haven’t any prior technical training by making a software tool that would eliminate much of the necessity for learners to take care of code when creating applications.
“Acquiring Expertise and Societal Productivity in a World of Artificial Intelligence,” led by David Atkin and Martin Beraja of the Department of Economics, and Danielle Li of MIT Sloan. Generative AI is believed to enhance the capabilities of staff performing cognitive tasks. This project seeks to raised understand how the arrival of AI technologies may impact skill acquisition and productivity, and to explore complementary policy interventions that may allow society to maximise the gains from such technologies.
“AI Augmented Onboarding and Support,” led by Tim Kraska of EECS and CSAIL, and Christoph Paus of the Department of Physics. While LLMs have made enormous leaps forward lately and are poised to fundamentally change the way in which students and professionals find out about latest tools and systems, there is usually a steep learning curve which individuals need to climb with the intention to make full use of the resource. To assist mitigate the difficulty, this project proposes the event of recent LLM-powered onboarding and support systems that may positively impact the way in which support teams operate and improve the user experience.