Machine learning

A new study by researchers at MIT suggests that “the biggest and most computationally intensive AI models may soon offer diminishing returns compared to smaller models,” reports Will Knight for Wired. “By mapping scaling laws against continued improvements in model efficiency, the researchers found that it could become harder to wring leaps in performance from giant models whereas efficiency gains could make models running on more modest hardware increasingly capable over the next decade.” 

Researchers at MIT have developed a new method that can predict how plasma will behave in a tokamak reactor given a set of initial conditions, reports Gayoung Lee for Gizmodo. The findings “may have lowered one of the major barriers to achieving large-scale nuclear fusion,” explains Lee. 

Writing for Forbes, Senior Lecturer Guadalupe Hayes-Mota '08, SM '16, MBA '16 emphasizes the importance of implementing ethical frameworks when developing AI systems designed for use in healthcare. “The future of AI in healthcare not only needs to be intelligent,” writes Hayes-Mota. “It needs to be trusted. And in healthcare, trust is the ultimate competitive edge.” 

Researchers at MIT have studied how chatbots perceived the political environment leading up to the 2024 election and its impact on automatically generated election-related responses, reports Patrick Kulp for Tech Brew. The researchers “fed a dozen leading LLMs 12,000 election-related questions on a nearly daily basis, collecting more than 16 million total responses through the contest in November,” explains Kulp.  

Institute Prof. Daron Acemoglu participated in a “global dialogue on artificial intelligence governance” at the United Nations, reports Steve Lohr for The New York Times. “The AI quest is currently focused on automating a lot of things, sidelining and displacing workers,” says Acemoglu. 

Researchers from MIT and Stanford tracked 11 large language models during the 2024 presidential campaign, and found that “AI models answered differently overtime… [and] they changed in response to events, prompts, and even demographic cues,” reports Ron Schmelzer for Forbes. 

Financial Times reporter Melissa Heikkilä spotlights how MIT researchers have uncovered evidence that increased use of AI tools by medical professionals risks “leading to worse health outcomes for women and ethnic minorities.” One study found that numerous AI models “recommended a much lower level of care for female patients,” writes Heikkilä. “A separate study by the MIT team showed that OpenAI’s GPT-4 and other models also displayed answers that had less compassion towards Black and Asian people seeking support for mental health problems.” 

Prof. Dimitris Bertsimas, vice provost for MIT Open Learning, speaks with Forbes contributor Aviva Legatt about AI usage among university students. “Universities have a responsibility to ensure students, faculty, and staff gain a strong foundation in AI’s concepts, opportunities, and risks so they can help solve society’s biggest challenges,” says Bertsimas.

Edwin Chen '08 speaks with Forbes reporter Pheobe Liu about his journey to founding Surge AI, a startup that “helps tech companies get the high-quality data they need to improve their AI models.” 

Prof. Anant Agarwal speaks with Fortune reporter Nino Paoli about the benefits of a four-year college degree. “In this environment, learning deeply and building real expertise is more important than ever because the AI roles and applications are in the context of these other fields,” says Agarwal. “Degrees also future-proof your career by preparing you for the next big technology, whatever it might be.”

Forbes reporter Martina Castellanos spotlights Edwin Chen '08, founder of Surge AI, as one of the 10 youngest billionaires on the 2025 Forbes 400 list. After working in machine learning, Chen saw “the lack of quality training data for AI,” and “launched Surge AI in 2020 to fix the problem,” writes Castellanos. 

Prof. Dylan Hadfield-Menell speaks with CNN reporter Hadas Gold about the need for AI safeguards and increased education on large language models. “The way these systems are trained is that they are trained in order to give responses that people judge to be good,” explains Hadfield-Menell. 

Researchers at MIT have found that generative AI “not only repeats the same irrational tendencies of humans during the decision making process but also lacks some of the positive traits that humans do possess,” reports Tamsin Gable for Forbes. “This led the researchers to suggest that AI cannot replace many tasks and that human expertise remains important,” adds Gable. 

MIT Dean of Digital Learning Cynthia Breazeal SM ’93, ScD ’00, Profs. Regina Barzilay and Priya Donti, and a number of MIT alumni have been named to Time’s TIME 100 AI 2025 list. The list spotlights “innovators, leaders, and thinkers reshaping our world through groundbreaking advances in artificial intelligence.”

Prof. Marzyeh Ghassemi speaks with Boston Globe reporter Hiawatha Bray about her work uncovering issues with bias and trustworthiness in medical AI systems. “I love developing AI systems,” says Ghassemi. “I’m a professor at MIT for a reason. But it’s clear to me that naive deployments of these systems, that do not recognize the baggage that human data comes with, will lead to harm.”