Sara Beery, Marzyeh Ghassemi, and Yoon Kim, EECS faculty and CSAIL principal investigators, were awarded AI2050 Early Career Fellowships earlier this week for their pursuit of “bold and ambitious work on hard problems in AI.” They received this honor from Schmidt Futures, Eric and Wendy Schmidt’s philanthropic initiative that aims to accelerate scientific innovation.
Chatbots can wear a lot of proverbial hats: dictionary, therapist, poet, all-knowing friend. The artificial intelligence models that power these systems appear exceptionally skilled and efficient at providing answers, clarifying concepts, and distilling information. But to establish trustworthiness of content generated by such models, how can we really know if a particular statement is factual, a hallucination, or just a plain misunderstanding?
Daniela Rus, Director of CSAIL and MIT EECS Professor, recently received the 2025 Edison Medal from the Institute of Electrical and Electronics Engineers (IEEE). The award recognizes her leadership and pioneering work in modern robotics.
Regina Barzilay, School of Engineering Distinguished Professor for AI and Health at MIT, CSAIL Principal Investigator, and Jameel Clinic AI Faculty Lead, has been awarded the 2025 Frances E. Allen Medal from the Institute of Electrical and Electronics Engineers (IEEE). Barzilay’s award recognizes the impact of her machine-learning algorithms on medicine and natural language processing.
Daniela Rus, Director of CSAIL and MIT EECS Professor, was recently named a co-recipient of the 2024 John Scott Award by the Board of Directors of City Trusts. This prestigious honor, steeped in historical significance, celebrates scientific innovation at the very location where American independence was signed in Philadelphia, a testament to the enduring connection between scientific progress and human potential.
As far as user data is concerned, much is made of the big social media conglomerates like Google and Meta. However, cloud service providers such as Amazon Web Services and Microsoft Azure are the backbone of countless applications, holding the keys to vast amounts of data stored on their servers.
For roboticists, one challenge towers above all others: generalization – the ability to create machines that can adapt to any environment or condition. Since the 1970s, the field has evolved from writing sophisticated programs to using deep learning, teaching robots to learn directly from human behavior. But a critical bottleneck remains: data quality. To improve, robots need to encounter scenarios that push the boundaries of their capabilities, operating at the edge of their mastery.
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.
In the classic cartoon “The Jetsons,” Rosie the robotic maid seamlessly switches from vacuuming the house to cooking dinner to taking out the trash. But in real life, training a general-purpose robot remains a major challenge.
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.
