When Nikola Tesla predicted we’d have handheld phones that could display videos, photographs, and more, his musings seemed like a distant dream. Nearly 100 years later, smartphones are like an extra appendage for many of us.
Research scientist Yosuke Tanigawa and Professor Manolis Kellis at MIT Computer Science and Artificial Intelligence Laboratory (CSAIL) have developed a novel methodology in human genetics to address an often-overlooked problem: how to handle clinical measurements that fall "below the limit of quantification" (BLQ). Recently published in the American Journal of Human Genetics, their new approach, "hypometric genetics," utilizes these typically discarded measurements to enhance genetic discovery, with significant implications for personalized genomic medicine and drug development.
When you think about hands-free devices, you might picture Alexa and other voice-activated in-home assistants, Bluetooth earpieces, or asking Siri to make a phone call in your car. You might not imagine using your mouth to communicate with other devices like a computer or a phone remotely.
Despite their impressive capabilities, large language models are far from perfect. These artificial intelligence models sometimes “hallucinate” by generating incorrect or unsupported information in response to a query.
AI systems are increasingly being deployed in safety-critical health care situations. Yet these models sometimes hallucinate incorrect information, make biased predictions, or fail for unexpected reasons, which could have serious consequences for patients and clinicians.
Ever been asked a question you only knew part of the answer to? To give a more informed response, your best move would be to phone a friend with more knowledge on the subject.
To the untrained eye, a medical image like an MRI or X-ray appears to be a murky collection of black-and-white blobs. It can be a struggle to decipher where one structure (like a tumor) ends and another begins.
This September, MIT Hacking Medicine is hosting the BioxAI Pitch Event. The event will be an opportunity to bring together budding entrepreneurs from various MIT departments, namely PhD students and postdocs, applying ML/AI to biological questions, with a focus on protein biology/drug discovery. For example, early stage founders will pitch for co-founders (max. 2min). Founders and individuals who want to join a team will likewise pitch themselves. This will be an opportunity to learn from guests within and outside MIT, including NSF, CSAIL Alliances, and the Martin Trust Center.
Are you a CSAIL entrepreneur? Are you curious about the resources that CSAIL Alliances, as well as the rest of the MIT Ecosystem can offer you? Sign up for Office Hours using the form to ask Christiana Kalfas, Sr.
On a research cruise around Hawaii in 2018, Yuening Zhang SM ’19, PhD ’24 saw how difficult it was to keep a tight ship. The careful coordination required to map underwater terrain could sometimes led to a stressful environment for team members, who might have different understandings of which tasks must be completed in spontaneously changing conditions. During these trips, Zhang considered how a robotic companion could have helped her and her crewmates achieve their goals more efficiently.
