Artificial intelligence

Forbes reporter Joe McKendrick spotlights a study by researchers from the MIT Center for Collective Intelligence evaluating “the performance of humans alone, AI alone, and combinations of both.” The researchers found that “human–AI systems do not necessarily achieve better results than the best of humans or AI alone,” explains graduate student Michelle Vaccaro and her colleagues. “Challenges such as communication barriers, trust issues, ethical concerns and the need for effective coordination between humans and AI systems can hinder the collaborative process.”

Prof. David Autor has been named a Senior Fellow in the Schmidt Sciences AI2050 Fellows program, and Profs. Sara Beery, Gabriele Farina, Marzyeh Ghassemi, and Yoon Kim have been named Early Career AI2050 Fellows, reports Michael T. Nietzel for Forbes. The AI2050 fellowships provide funding and resources, while challenging “researchers to imagine the year 2050, where AI has been extremely beneficial and to conduct research that helps society realize its most beneficial impacts,” explains Nietzel. 

Prof. Daniela Rus, director of CSAIL, speaks with NBC Boston reporter Colton Bradford about her work developing a new AI system aimed at making grocery shopping easier, more personalized and more efficient. “I think there is an important synergy between what people can do and what machines can do,” says Rus. “You can think of it as machines have speed, but people have wisdom. Machines can lift heavy things, but people can reason about what to do with those heavy things.” 

Writing for The New York Times, Prof. Anant Agarwal shares AI’s potential to “revolutionize education by enhancing paths to individual students in ways we never thought possible.” Agarwal emphasizes: “A.I. will never replace the human touch that is so vital to education. No algorithm can replicate the empathy, creativity and passion a teacher brings to the classroom. But A.I. can certainly amplify those qualities. It can be our co-pilot, our chief of staff helping us extend our reach and improve our effectiveness.”

Using a new technique developed to examine the risks of multimodal large language models used in robots, MIT researchers were able to have a “simulated robot arm do unsafe things like knocking items off a table or throwing them by describing actions in ways that the LLM did not recognize as harmful and reject,” writes Will Knight for Wired. “With LLMs a few wrong words don’t matter as much,” explains Prof. Pulkit Agrawal. “In robotics a few wrong actions can compound and result in task failure more easily.”

Researchers from MIT and elsewhere have compared 12 large language models (LLMs) against 925 human forecasters for a three-month forecasting tournament to help predict real-world events, including geopolitical events, reports Tomas Gorny for Forbes. "Our results suggest that LLMs can achieve forecasting accuracy rivaling that of human crowd forecasting tournaments,” the researchers explain.

Forbes reporter John M. Bremen spotlights a new study by MIT researchers that “shows the most skilled scientists and innovations benefitted the most from AI – doubling their productivity – while lower-skilled staff did not experience similar gains.” The study “showed that specialized AI tools foster radical innovation at the technical level within a domain-specific scope, but also risk narrowing human roles and diversity of thought,” writes Bremen. 

Writing for Forbes, Senior Lecturer Guadalupe Hayes-Mota SB '08, MS '16, MBA '16 shares insight into how entrepreneurs can use AI to build successful startups. AI “can be a strategic advantage when implemented wisely and used as a tool to support, rather than replace, the human touch,” writes Hayes-Mota. 

Prof. Armando Solar-Lezama speaks with New York Times reporter Sarah Kessler about the future of coding jobs, noting that AI systems still lack many essential skills. “When you’re talking about more foundational skills, knowing how to reason about a piece of code, knowing how to track down a bug across a large system, those are things that the current models really don’t know how to do,” says Solar-Lezama.

Forbes contributor Michael T. Nietzel spotlights the newest cohort of Rhodes Scholars, which includes Yiming Chen '24, Wilhem Hector, Anushka Nair, and David Oluigbo from MIT. Nietzel notes that Oluigbo has “published numerous peer-reviewed articles and conducts research on applying artificial intelligence to complex medical problems and systemic healthcare challenges.” 

Yiming Chen '24, Wilhem Hector, Anushka Nair, and David Oluigbo have been named 2025 Rhodes Scholars, report Brian P. D. Hannon and John Hanna for the Associated Press. Undergraduate student David Oluigbo, one of the four honorees, has “volunteered at a brain research institute and the National Institutes of Health, researching artificial intelligence in health care while also serving as an emergency medical technician,” write Hannon and Hanna.

Research from the Data Provenance Initiative, led by MIT researchers, has “found that many web sources used for training AI models have restricted their data, leading to a rapid decline in accessible information,” reports Gary Drenik for Forbes. 

Researchers at MIT have developed a new AI model capable of assessing a patient’s risk of pancreatic cancer, reports Erez Meltzer for Forbes. “The model could potentially expand the group of patients who can benefit from early pancreatic cancer screening from 10% to 35%,” explains Meltzer. “These kinds of predictive capabilities open new avenues for preventive care.” 

Arago, an AI startup co-founded by alumnus Nicolas Muller, has been named to the Future 40 list by Station F, which selects “the 40 most promising startups,” reports Romain Dillet for TechCrunch. Arago is “working on new AI-focused chips that use optical technology at the chipset level to speed up operations,” explains Dillet.

Neural Magic, an AI optimization startup co-founded by Prof. Nir Shavit and former Research Scientist Alex Matveev, aims to “process AI workloads on processors and GPUs at speeds equivalent to specialized AI chips,” reports Kyle Wiggers for TechCrunch. “By running models on off-the-shelf processors, which usually have more available memory, the company’s software can realize these performance gains,” explains Wiggers.