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Students pitch transformative ideas in generative AI at MIT Ignite competition


  • by YFS TEAM
  • 2023-11-18
  • in Inspiration
startupnews

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 diverse 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 help shape a future of AI innovation that is broadly beneficial — and to do that, we need a lot of great ideas. So, we turned to a pretty reliable source of great ideas: MIT’s highly entrepreneurial students and postdocs,” said MIT President Sally Kornbluth in her opening remarks at the event. 

 

MIT Ignite Flagship PrizeseMote (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 can be expensive. eMote’s app allows users to identify their emotions, visualize them as art using the co-creative process 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 can be complicated and costly. LeGT.ai aims to democratize legal knowledge. Using a platform with a large language model, prompt engineering, and semantic search, the team will streamline a chatbot for completion, research, and drafting of documents for firms, as well as improve pre-screening and initial consultations.

 

Sunona (Emmi Mills, Selin Kocalar, Srihitha Dasari, and Karun Kaushik): About half of a doctor’s day is consumed by medical documentation and clinical notes. To address this, Sunona harnesses audio transcription and a large language model to transform audio from a doctor’s visit into notes and feature extraction, affording providers more time in 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 some of their daily 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 thousands 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 assist teachers and learners. The system also has immersive learning with AI agents for active learning for online and offline use.

 

First-Year Undergraduate Student Team MIT Ignite Flagship PrizeAlikorn (April Ren and Ayush Nayak): Drug discovery accounts for significant biotech costs. Alikorn’s large language model-powered platform aims to streamline the process of creating and simulating new molecules, using a generative adversarial network, a Monte-Carlo algorithm to vet the most promising candidates, and a physics simulation to determine the chemical properties.Runner-up PrizesAutonomous 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 reveal software vulnerabilities — can make software significantly safer. Autonomous Cyber’s system leverages large language models to automatically 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 process by examining and analyzing literature, and then produces an evidence gap map (EGM), suggesting potential impact areas.

 

Mattr AI (Leandra Tejedor, Katie Chen, and Eden Adler): Datasets that are used to train AI models often have issues of diversity, equity, and completeness. Mattr AI addresses this with generative AI with a large language model and stable diffusion models to augment datasets.Neuroscreen (Andrew Lu, Chonghua Xue, and Grant Robinson): Screening patients to potentially join a dementia clinical trial is costly, often takes years, and mostly results in an ineligibility. Neuroscreen employs AI to more quickly assess patients’ dementia causes, leading to 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 that are used to train 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 data, 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 aims to bring these walls down. Generative AI technology will summarize papers and help to guide research directions, providing a service for scholars as well as the broader scientific community.After the MIT Ignite competition, all 12 teams selected to present were invited to a networking event as an immediate first step to making their ideas and prototypes a reality. Additionally, 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.“In the months since I’ve arrived [at MIT], I’ve learned a lot about how MIT folks think about entrepreneurship and how it’s really built into everything that everyone at the Institute does, from first-year students to faculty to alumni — they are really motivated to get their ideas out into the world,” said President Kornbluth. “Entrepreneurship is an essential element for our goal of organizing for positive impact.”