This semester, students and postdocs across MIT were invited to submit ideas for the first-ever MIT Ignite: Generative AI Entrepreneurship Competition. Over 100 teams submitted proposals for startups that utilize generative artificial intelligence technologies to develop solutions across a various range of disciplines including human health, climate change, education, and workforce dynamics.

On Oct. 30, 12 finalists pitched their ideas in front of a panel of expert judges and a packed room in Samberg Conference Center.

“MIT has a responsibility to assist shape a way forward for AI innovation that’s broadly helpful — and to do this, we want a whole lot of great ideas. So, we turned to a reasonably reliable source of great ideas: MIT’s highly entrepreneurial students and postdocs,” said MIT President Sally Kornbluth in her opening remarks on the event. 

The MIT Ignite event is a component of a broader give attention to generative AI at MIT put forth by Kornbluth. This fall, across the Institute, researchers and students are exploring opportunities to contribute their knowledge on generative AI, identifying latest applications, minimizing risks, and employing it for the good thing about society. This event — co-organized by the MIT-IBM Watson AI Lab and the Martin Trust Center for MIT Entrepreneurship, and supported by MIT’s School of Engineering and the MIT Sloan School of Management — inspired young researchers to contribute to the dialogue and innovate in generative AI.

Serving as co-chairs for the event were Aude Oliva, MIT director of the MIT-IBM Watson AI Lab and a principal investigator within the Computer Science and Artificial Intelligence Laboratory (CSAIL); Bill Aulet, the Ethernet Inventors Professor of the Practice on the MIT Sloan School of Management and director of the Martin Trust Center; and Dina Katabi, the Thuan (1990) and Nicole Pham Professor within the Department of Electrical Engineering and Computer Science, director of the Center for Wireless Networks and Mobile Computing, and a CSAIL principal investigator.

Twelve teams of scholars and postdocs were competing for a variety of prizes, including five MIT Ignite Flagship Prizes of $15,000 each, a special first-year undergraduate student team Flagship Prize, and runner-up prizes. All prizes were provided by the MIT-IBM AI Watson Lab. Teams were judged on their project’s modern applications of generative AI, feasibility, potential for real-world impact, and the standard of presentation.

After the 12 teams showcased their technology, its potential to handle a difficulty, and the team’s ability to execute the plan, a panel of judges deliberated. Because the audience waited for the outcomes, remarks were made by Mark Gorenberg ’76, chair of the MIT Corporation; Anantha Chandrakasan, dean of the MIT School of Engineering and the Vannevar Bush Professor of Electrical Engineering and Computer Science; and David Schmittlein, the John C. Head III Dean and professor of selling on the MIT Sloan School of Management. The coed winners included:

MIT Ignite Flagship Prizes

eMote (Philip Cherner, Julia Sebastien, Caroline Lige Zhang, and Daeun Yoo): Sometimes identifying and expressing emotions is difficult, particularly for those on the alexithymia spectrum; further, therapy could be expensive. eMote’s app allows users to discover their emotions, visualize them as art using the co-creative strategy of generative AI, and reflect on them through journaling, thereby assisting school counselors and therapists.

LeGT.ai (Julie Shi, Jessica Yuan, and Yubing Cui): Legal processes around immigration could be complicated and expensive. LeGT.ai goals to democratize legal knowledge. Using a platform with a big language model, prompt engineering, and semantic search, the team will streamline a chatbot for completion, research, and drafting of documents for firms, in addition to improve pre-screening and initial consultations.

Sunona (Emmi Mills, Selin Kocalar, Srihitha Dasari, and Karun Kaushik): About half of a health care provider’s day is consumed by medical documentation and clinical notes. To handle this, Sunona harnesses audio transcription and a big language model to remodel audio from a health care provider’s visit into notes and have extraction, affording providers more time of their day.

UltraNeuro (Mahdi Ramadan, Adam Gosztolai, Alaa Khaddaj, and Samara Khater): For about one in seven adults, spinal cord injury, stroke, or disease will induce motor impairment and/or paralysis. UltraNeuro’s neuroprosthetics will help patients to regain a few of their every day abilities without invasive brain implants. Their technology leverages an electroencephalogram, smart sensors, and a multimodal AI system (muscle EMG, computer vision, eye movements) trained on 1000’s of movements to plan precise limb movements.

UrsaTech (Rui Zhou, Jerry Shan, Kate Wang, Alan He, and Rita Zhang): Education today is marked by disparities and overburdened educators. UrsaTech’s platform uses a multimodal large language model and diffusion models to create lessons, dynamic content, and assessments to help teachers and learners. The system also has immersive learning with AI agents for lively learning for online and offline use.

First-Yr Undergraduate Student Team MIT Ignite Flagship Prize

Alikorn (April Ren and Ayush Nayak): Drug discovery accounts for significant biotech costs. Alikorn’s large language model-powered platform goals to streamline the strategy of creating and simulating latest molecules, using a generative adversarial network, a Monte-Carlo algorithm to vet essentially the most promising candidates, and a physics simulation to find out the chemical properties.

Runner-up Prizes

Autonomous Cyber (James “Patrick” O’Brien, Madeline Linde, Rafael Turner, and Bohdan Volyanyuk): Code security audits require expertise and are expensive. “Fuzzing” code — injecting invalid or unexpected inputs to disclose software vulnerabilities — could make software significantly safer. Autonomous Cyber’s system leverages large language models to routinely integrate “fuzzers” into databases.

Gen EGM (Noah Bagazinski and Kristen Edwards): Making informed socioeconomic development policies requires evidence and data. Gen EGM’s large language model system expedites the method by examining and analyzing literature, after which produces an evidence gap map (EGM), suggesting potential impact areas.

Mattr AI (Leandra Tejedor, Katie Chen, and Eden Adler): Datasets which can be used to coach AI models often have problems with diversity, equity, and completeness. Mattr AI addresses this with generative AI with a big language model and stable diffusion models to enhance datasets.

Neuroscreen (Andrew Lu, Chonghua Xue, and Grant Robinson): Screening patients to potentially join a dementia clinical trial is dear, often takes years, and mostly leads to an ineligibility. Neuroscreen employs AI to more quickly assess patients’ dementia causes, resulting in more successful enrollment in clinical trials and treatment of conditions.

The Data Provenance Initiative (Naana Obeng-Marnu, Jad Kabbara, Shayne Longpre, William Brannon, and Robert Mahari): Datasets which can be used to coach AI models, particularly large language models, often have missing or incorrect metadata, causing concern for legal and ethical issues. The Data Provenance Initiative uses AI-assisted annotation to audit datasets, tracking the lineage and legal status of information, improving data transparency, legality, and ethical concerns around data.

Theia (Jenny Yao, Hongze Bo, Jin Li, Ao Qu, and Hugo Huang): Scientific research, and online dialogue around it, often occurs in silos. Theia’s platform goals to bring these partitions down. Generative AI technology will summarize papers and help to guide research directions, providing a service for scholars in addition to the broader scientific community.

After the MIT Ignite competition, all 12 teams chosen to present were invited to a networking event as a direct first step to creating their ideas and prototypes a reality. Moreover, they were invited to further develop their ideas with the support of the Martin Trust Center for MIT Entrepreneurship through StartMIT or MIT Fuse and the MIT-IBM Watson AI Lab.

“Within the months since I’ve arrived [at MIT], I’ve learned quite a bit about how MIT folks take into consideration entrepreneurship and the way it’s really built into all the things that everybody on the Institute does, from first-year students to school to alumni — they’re really motivated to get their ideas out into the world,” said President Kornbluth. “Entrepreneurship is a necessary element for our goal of organizing for positive impact.”