Author: Alex Ouyang
James Collins: Doing Good Science with an Underdog Spirit
James Collins is the Termeer Professor of Medical Engineering & Science and Professor of Biological Engineering at MIT. Jim serves as a director at the MIT Jameel Clinic, a member of the Harvard-MIT Health Sciences & Technology Faculty, a core founding faculty member of the Wyss Institute, and a member of the Broad Institute. Jim is also an elected member of all three national academies. As one of the founders of synthetic biology, Jim has pioneered research by using synthetic biology and AI to develop next-generation diagnostics and therapeutics, particularly for infectious diseases such as Ebola, Zika, COVID-19 and antibiotic-resistant bacteria (superbugs). Jim has received numerous awards and honors including recognition as a Clarivate Citation Laureate. The technologies from his lab have been licensed by over 25 biotech, pharmaceutical, and medical device companies, and he has also co-founded a number of biotech startups. Learn more
On algorithms, life, and learning
From enhancing international business logistics to freeing up more hospital beds to helping farmers, MIT Professor Dimitris Bertsimas SM ’87, PhD ’88 summarized how his work in operations research has helped drive real-world improvements, while delivering the 54th annual James R. Killian Faculty Achievement Award Lecture at MIT on Thursday, March 19.Bertsimas also described how artificial intelligence is now being used in some of his scholarly projects and as a tool in MIT Open Learning efforts, which he currently directs — another facet of a highly productive and lauded career over four decades at the Institute. The Killian Award is the highest prize MIT gives its faculty.
“I have tried to improve the human condition,” Bertsimas said, summarizing the breadth of his work and the many applications to everyday living that he has found for it. Learn more
A better method for identifying overconfident large language models
Large language models (LLMs) can generate credible but inaccurate responses, so researchers have developed uncertainty quantification methods to check the reliability of predictions. One popular method involves submitting the same prompt multiple times to see if the model generates the same answer.But this method measures self-confidence, and even the most impressive LLM might be confidently wrong. Overconfidence can mislead users about the accuracy of a prediction, which might result in devastating consequences in high-stakes settings like health care or finance.
To address this shortcoming, MIT researchers introduced a new method for measuring a different type of uncertainty that more reliably identifies confident but incorrect LLM responses. Learn more
