Home Artificial Intelligence College students pitch transformative concepts in generative AI at MIT Ignite competitors | MIT Information

College students pitch transformative concepts in generative AI at MIT Ignite competitors | MIT Information

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College students pitch transformative concepts in generative AI at MIT Ignite competitors | MIT Information

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This semester, college students and postdocs throughout MIT have been invited to submit concepts for the first-ever MIT Ignite: Generative AI Entrepreneurship Competitors. Over 100 groups submitted proposals for startups that make the most of generative synthetic intelligence applied sciences to develop options throughout a various vary of disciplines together with human well being, local weather change, training, and workforce dynamics.

On Oct. 30, 12 finalists pitched their concepts in entrance of a panel of professional judges and a packed room in Samberg Convention Heart.

“MIT has a duty to assist form a way forward for AI innovation that’s broadly helpful — and to do this, we’d like plenty of nice concepts. So, we turned to a reasonably dependable supply of nice concepts: MIT’s extremely entrepreneurial college students and postdocs,” mentioned MIT President Sally Kornbluth in her opening remarks on the occasion. 

The MIT Ignite occasion is a part of a broader deal with generative AI at MIT put forth by Kornbluth. This fall, throughout the Institute, researchers and college students are exploring alternatives to contribute their data on generative AI, figuring out new functions, minimizing dangers, and using it for the advantage of society. This occasion — co-organized by the MIT-IBM Watson AI Lab and the Martin Belief Heart for MIT Entrepreneurship, and supported by MIT’s Faculty of Engineering and the MIT Sloan Faculty of Administration — impressed younger researchers to contribute to the dialogue and innovate in generative AI.

Serving as co-chairs for the occasion have been Aude Oliva, MIT director of the MIT-IBM Watson AI Lab and a principal investigator within the Laptop Science and Synthetic Intelligence Laboratory (CSAIL); Invoice Aulet, the Ethernet Inventors Professor of the Observe on the MIT Sloan Faculty of Administration and director of the Martin Belief Heart; and Dina Katabi, the Thuan (1990) and Nicole Pham Professor within the Division of Electrical Engineering and Laptop Science, director of the Heart for Wi-fi Networks and Cellular Computing, and a CSAIL principal investigator.

Twelve groups of scholars and postdocs have been competing for various prizes, together with 5 MIT Ignite Flagship Prizes of $15,000 every, a particular first-year undergraduate pupil workforce Flagship Prize, and runner-up prizes. All prizes have been supplied by the MIT-IBM AI Watson Lab. Groups have been judged on their mission’s progressive functions of generative AI, feasibility, potential for real-world affect, and the standard of presentation.

After the 12 groups showcased their know-how, its potential to deal with a difficulty, and the workforce’s potential to execute the plan, a panel of judges deliberated. Because the viewers waited for the outcomes, remarks have been made by Mark Gorenberg ’76, chair of the MIT Company; Anantha Chandrakasan, dean of the MIT Faculty of Engineering and the Vannevar Bush Professor of Electrical Engineering and Laptop Science; and David Schmittlein, the John C. Head III Dean and professor of promoting on the MIT Sloan Faculty of Administration. The coed winners included:

MIT Ignite Flagship Prizes

eMote (Philip Cherner, Julia Sebastien, Caroline Lige Zhang, and Daeun Yoo): Typically figuring out and expressing feelings is tough, significantly for these on the alexithymia spectrum; additional, remedy may be costly. eMote’s app permits customers to establish their feelings, visualize them as artwork utilizing the co-creative strategy of generative AI, and mirror on them by journaling, thereby helping college counselors and therapists.

LeGT.ai (Julie Shi, Jessica Yuan, and Yubing Cui): Authorized processes round immigration may be difficult and expensive. LeGT.ai goals to democratize authorized data. Utilizing a platform with a big language mannequin, immediate engineering, and semantic search, the workforce will streamline a chatbot for completion, analysis, and drafting of paperwork for companies, in addition to enhance pre-screening and preliminary 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 scientific notes. To deal with this, Sunona harnesses audio transcription and a big language mannequin to remodel audio from a health care provider’s go to into notes and have extraction, affording suppliers extra time of their day.

UltraNeuro (Mahdi Ramadan, Adam Gosztolai, Alaa Khaddaj, and Samara Khater): For about one in seven adults, spinal wire harm, stroke, or illness will induce motor impairment and/or paralysis. UltraNeuro’s neuroprosthetics will assist sufferers to regain a few of their each day skills with out invasive mind implants. Their know-how leverages an electroencephalogram, good sensors, and a multimodal AI system (muscle EMG, pc imaginative and prescient, eye actions) skilled on hundreds of actions to plan exact limb actions.

UrsaTech (Rui Zhou, Jerry Shan, Kate Wang, Alan He, and Rita Zhang): Schooling at present is marked by disparities and overburdened educators. UrsaTech’s platform makes use of a multimodal giant language mannequin and diffusion fashions to create classes, dynamic content material, and assessments to help lecturers and learners. The system additionally has immersive studying with AI brokers for energetic studying for on-line and offline use.

First-Yr Undergraduate Pupil Workforce MIT Ignite Flagship Prize

Alikorn (April Ren and Ayush Nayak): Drug discovery accounts for vital biotech prices. Alikorn’s giant language model-powered platform goals to streamline the method of making and simulating new molecules, utilizing a generative adversarial community, 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 safety audits require experience and are costly. “Fuzzing” code — injecting invalid or surprising inputs to disclose software program vulnerabilities — could make software program considerably safer. Autonomous Cyber’s system leverages giant language fashions to routinely combine “fuzzers” into databases.

Gen EGM (Noah Bagazinski and Kristen Edwards): Making knowledgeable socioeconomic improvement insurance policies requires proof and information. Gen EGM’s giant language mannequin system expedites the method by analyzing and analyzing literature, after which produces an proof hole map (EGM), suggesting potential affect areas.

Mattr AI (Leandra Tejedor, Katie Chen, and Eden Adler): Datasets which are used to coach AI fashions typically have problems with variety, fairness, and completeness. Mattr AI addresses this with generative AI with a big language mannequin and steady diffusion fashions to reinforce datasets.

Neuroscreen (Andrew Lu, Chonghua Xue, and Grant Robinson): Screening sufferers to doubtlessly be a part of a dementia scientific trial is dear, typically takes years, and principally leads to an ineligibility. Neuroscreen employs AI to extra shortly assess sufferers’ dementia causes, resulting in extra profitable enrollment in scientific trials and remedy of circumstances.

The Knowledge Provenance Initiative (Naana Obeng-Marnu, Jad Kabbara, Shayne Longpre, William Brannon, and Robert Mahari): Datasets which are used to coach AI fashions, significantly giant language fashions, typically have lacking or incorrect metadata, inflicting concern for authorized and moral points. The Knowledge Provenance Initiative makes use of AI-assisted annotation to audit datasets, monitoring the lineage and authorized standing of knowledge, bettering information transparency, legality, and moral considerations round information.

Theia (Jenny Yao, Hongze Bo, Jin Li, Ao Qu, and Hugo Huang): Scientific analysis, and on-line dialogue round it, typically happens in silos. Theia’s platform goals to convey these partitions down. Generative AI know-how will summarize papers and assist to information analysis instructions, offering a service for students in addition to the broader scientific group.

After the MIT Ignite competitors, all 12 groups chosen to current have been invited to a networking occasion as a right away first step to creating their concepts and prototypes a actuality. Moreover, they have been invited to additional develop their concepts with the assist of the Martin Belief Heart for MIT Entrepreneurship by StartMIT or MIT Fuse and the MIT-IBM Watson AI Lab.

“Within the months since I’ve arrived [at MIT], I’ve discovered rather a lot about how MIT of us take into consideration entrepreneurship and the way it’s actually constructed into all the things that everybody on the Institute does, from first-year college students to college to alumni — they’re actually motivated to get their concepts out into the world,” mentioned President Kornbluth. “Entrepreneurship is a vital component for our objective of organizing for constructive affect.”

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