This semester, MIT students and postdocs are invited to submit ideas for the inaugural MIT Ignite: Generative Artificial Intelligence Entrepreneurship Competition. More than 100 teams submitted proposals to startups leveraging generative AI technologies to develop solutions across multiple disciplines, including human health, climate change, education and workforce dynamics.
The 12 finalists presented their ideas on October 30 before a panel of expert judges and a packed room at the Samberg Convention Center.
“MIT has a responsibility to help shape a future of broadly beneficial AI innovation—and to do that, we need a lot of great ideas. So we turned to a very reliable source of great ideas: MIT’s highly entrepreneurial students and postdoc,” MIT President Sally Kornbluth said in opening remarks at the event.
The MIT Ignite event is part of Kornbluth’s broader focus on generative artificial intelligence. This fall, researchers and students across the Institute are exploring opportunities to contribute their knowledge of generative artificial intelligence, identify new applications, minimize risks, and use it to benefit society. The event, co-hosted by the MIT-IBM Watson AI Lab and the MIT Martin Trust Center for Entrepreneurship, with support from the MIT School of Engineering and the MIT Sloan School of Management, inspires young researchers to contribute to dialogue and innovation in the generative field AI.
Co-chairs of this event are Aude Oliva, director of the MIT-IBM Watson AI Laboratory and principal researcher of the Computer Science and Artificial Intelligence Laboratory (CSAIL); Bill Aulet, Ethernet Inventor Professor of the Practice and Professor of Practice at the MIT Sloan School of Management Martin Trust Center Director; Dina Katabi, Thuan (1990) Department of Electrical Engineering and Computer Science; and Nicole Pham Professor, Director of the Center for Wireless Networks and Mobile Computing and CSAIL Principal Investigator.
Twelve teams of students and postdocs competed for multiple awards, including five MIT Ignite Flagship Awards ($15,000 each), a special first-year undergraduate team Flagship Award, and a runner-up award. All awards are provided by the MIT-IBM AI Watson Lab. Teams will be judged on their project’s innovative application of generative AI, feasibility, potential for real-world impact, and quality of demonstration.
The jury deliberated after 12 teams demonstrated their technology, problem-solving potential and the team’s ability to execute their plans. As the audience awaited the results, remarks were made by MIT Corporation Chairman Mark Gorenberg ’76; Anantha Chandrakasan, dean of MIT’s School of Engineering and Vannevar Bush Professor of Electrical Engineering and Computer Science; and MIT Sloan School of Management David Schmittlein, John C. Head III Dean and Professor of Marketing. Winning students include:
MIT Ignite Flagship Award
eMote (Philip Cherner, Julia Sebastien, Caroline Lige Zhang, and Daeun Yoo): Recognizing and expressing emotions is sometimes difficult, especially for those with alexithymia; furthermore, treatment can be expensive. eMote’s app helps school counselors and therapists by allowing users to identify their emotions, visualize them as art using a co-creation process that generates artificial intelligence, and reflect through journaling.
LeGT.ai (Julie Shi, Jessica Yuan and Yubing Cui): The legal process surrounding immigration can be complex and costly. LeGT.ai aims to democratize legal knowledge. The team will streamline chatbots for completing, researching and drafting corporate documents and improve pre-screening and initial consultations using a platform with large language models, rapid engineering and semantic search.
Sunona (Emmi Mills, Selin Kocalar, Srihitha Dasari and Karun Kaushik): Physicians spend about half of their day on medical documentation and clinical notes. To solve this problem, Sunona leverages audio transcription and large language models to convert audio of doctor visits into notes and feature extraction, giving providers more time.
super nervous (Mahdi Ramadan, Adam Gosztolai, Alaa Khaddaj and Samara Khater): For about one in seven adults, spinal cord injury, stroke or disease can cause movement disorders and/or paralysis. UltraNeuro’s neuroprosthetics will help patients regain some daily abilities without invasive brain implants. Their technology utilizes electroencephalography, smart sensors, and multimodal artificial intelligence systems (muscle electromyography, computer vision, eye movement) trained on thousands of movements to plan precise body movements.
Xiong Da Technology (Rui Zhou, Jerry Shan, Kate Wang, Alan He and Rita Zhang): Today’s education is characterized by inequality and overburdened educators. UrsaTech’s platform uses multimodal large language models and diffusion models to create courses, dynamic content, and assessments to help teachers and learners. The system also features immersive learning capabilities with artificial intelligence agents that can use active learning both online and offline.
First-Year Undergraduate Team MIT Ignite Flagship Award
Arican (April Ren and Ayush Nayak): Drug discovery accounts for a huge portion of biotech costs. Alikorn’s large-scale language model-driven platform is designed to simplify the process of creating and simulating new molecules, using generative adversarial networks, Monte Carlo algorithms to vet the most promising candidates, and physical simulations to determine chemical properties.
Runner up prize
autonomous network (James “Patrick” O’Brien, Madeline Linde, Rafael Turner, and Bohdan Volyanyuk): Code security audits require specialized knowledge and are expensive. “Fuzz testing” code – injecting invalid or unexpected input to reveal software vulnerabilities – can make software more secure. Autonomous Cyber’s system leverages large language models to automatically integrate “fuzzers” into databases.
GM Extraordinary Meeting of Shareholders (Noah Bagazinski and Christine Edwards): Making smart socioeconomic development policies requires evidence and data. Gen EGM’s large-scale language modeling system speeds up this process by examining and analyzing the literature, then generating Evidence Gap Maps (EGMs) that indicate potential areas of impact.
Matt artificial intelligence (Leandra Tejedor, Katie Chen, and Eden Adler): Datasets used to train AI models often suffer from diversity, fairness, and completeness issues. Mattr AI solves this problem with generative AI that features large language models and robust diffusion models to enhance data sets.
neurological screening (Andrew Lu, Chonghua Xu and Grant Robinson): Screening patients for possible participation in dementia clinical trials is expensive, often takes years, and often results in ineligibility. Neuroscreen uses artificial intelligence to more quickly assess the cause of dementia in patients, allowing for more successful participation in clinical trials and treatments.
data provenance initiative (Naana Obeng-Marnu, Jad Kabbara, Shayne Longpre, William Brannon, and Robert Mahari): Datasets used to train AI models, especially large language models, often have missing or incorrect metadata, raising legal and ethical issues Worry. The data provenance program uses AI-assisted annotation to audit data sets, track the lineage and legal status of data, and improve data transparency, legality, and ethical issues surrounding data.
Theia (Jenny Yao, Hongze Bo, Jin Li, Ao Qu, and Hugo Huang): Scientific research, and the online conversations around it, often occur in silos. Theia’s platform aims to tear down these walls. Generative AI techniques will summarize the paper and help guide research directions, serving scholars as well as the broader scientific community.
After the MIT Ignite competition, all 12 selected teams were invited to a networking event, the first step in turning their ideas and prototypes into reality. In addition, they are invited to further develop their ideas with support from the Martin Trust Center for MIT Entrepreneurship through StartMIT or MIT Fuse and the MIT-IBM Watson AI Lab.
“In the months since I arrived [at MIT], I learned a lot about how people at MIT view entrepreneurship and how it’s really integrated into everything that everyone at the institute does, from first-year students to faculty to alumni—they’re really motivated to put themselves ideas to the world,” said President Kornbluth. “Entrepreneurship is a fundamental element of our organization’s goal of making a positive impact. “