Research

The Financial Times’ Anjana Ahuja spotlights research led by Prof. Dennis Whyte on the economics of fusion energy. “Economic Q, the metric devised by Whyte and others and which must exceed one to represent net economic gain, compares the capital gained over a fusion energy plant’s lifetime to that expended,” writes Ahuja. “Factors reflect the extreme engineering involved and include construction costs, component durability and the efficiency of converting fusion power into a commodity.” 

A new study by MIT researchers found that it is impossible to draw a direct link between AI diffusion model outputs and an artist’s body of work, writes Gizmodo’s Webb Wright. “If a [diffusion] model generates something, you want to be able to say, ‘Oh, this part of the training data was responsible,'” says Zheng Dai, PhD ’24, lead author on the study. “It’s important for us to understand how these models work to properly study them or regulate them.” 

In conversation with The Boston Globe’s Hilary Burns, MIT President Emeritus L. Rafael Reif discusses his views on the value of US research universities and why saying so publicly matters. It's “tough times for universities, and that means a non-ending decline for the US,” warns Reif. “There is something unique about a university that companies do not have and national labs do not have, which is students. If we believe that science is important, we have to have a place where we create the scientists of the future.” 

Forbes’ Satya Krishnaswamy highlights a study co-authored by Lawrence Schmidt, a research affiliate at Sloan, that found AI can contribute to growth in certain roles where workers focus on higher-value tasks. “Schmidt’s research found that increased AI use was linked to both employment and sales growth,” writes Krishnaswamy.  

Writing for Forbes, Arvid Ernst Gollwitzer, a research scholar at the Broad Institute of MIT and Harvard, spotlights FINGERS-7B, an AI foundation model for Alzheimer’s prevention his research team developed at MIT. “By analyzing signals spanning everything from genetic risk factors to long-term environmental exposures, the model detects patterns that no single-domain approach could find,” writes Gollwitzer. “These patterns, which we call multi-omic precision signatures, represent a new kind of biomarker for gauging early disease risk.” 

MIT researchers have found that AI search features are unevenly distributed worldwide, and can sometimes share misleading information about images, writes Forbes’ Hastimal Jangid. “These incidents are important reminders that systems are only as good as the data they're trained on and that the long tail of data documenting the physical world is still being filled in,” writes Jangid.

Prof. David Gifford and Zheng Dai PhD ’24 discovered that AI diffusion models can replicate the style of an artist even when their work was omitted from datasets, leading to ‘attribution decay,’ writes Fast Company’s Jesus Diaz. “This trend, what the researchers call ‘attribution decay,’ means that artificial intelligence can produce an image that resembles a particular artist’s work, while having no provable causal link to that artist’s actual contribution to the training data,” explains Diaz.  

MIT researchers have engineered three strains of bacteria to function as living transistors, carrying signals across circuits, writes Gizmodo’s Gayoung Lee. “For instance, the circuit could sit near the roots of plants to detect different stresses or autonomously respond to the presence of pests or other environmental threats,” writes Lee.  

Sonia Vallabh, Director of Prion Therapeutic Science at the Broad Institute of MIT and Harvard, speaks to Nature’s Josie Glausiusz about her journey co-leading an initiative to develop preventive drugs for prion disease, a rare, lethal neurodegenerative disease that is caused by misfolded proteins that kill neurons. “In pre-symptomatic people at risk, if we can lower the amount of normal protein, I’m hoping we can delay or even prevent the formation of the first misfolded prion,” says Vallabh.  

MIT researchers have found that lower birth rates are associated with higher growth in Gross Domestic Product globally, writes The Atlantic’s Idrees Kahloon. “[P]erhaps more important than the observed correlation is the authors’ proposed explanation: that when prime-age workers become scarce, companies adapt by developing productivity-boosting technology,” notes Kahloon.  

MIT researchers found the more data an AI model is trained on could complicate AI copyright, auditing, and governance, writes Computer World’s Taryn Plumb. “[S]ingle artworks by specific artists, or photographs of certain people, could be entirely removed from datasets, and the model could still reproduce that image or style,” writes Plumb. “Essentially, tangible connections are lost, and linking to specific data points responsible for generated samples is ‘practically impossible,’ or can even vanish, the researchers explained.” 

In a study of developers using GitHub Copilot, MIT researchers found AI shifted time away from teamwork and toward coding, writes Forbes’ Sarah Davis. “If AI reduces the need to ask colleagues for help and advice, workplaces become the mechanism for creating human interactions that promote connection and knowledge sharing,” writes Davis.  

The New York Times’ Dana Goldstein highlights an MIT study that found individuals who used AI for writing assistance recorded lower brain activity than those who worked without LLMs. “The writers who used AI struggled to remember what, exactly, they had written, and felt little ownership over their work,” writes Goldstein. “The findings join a growing body of research suggesting that frequent AI use can degrade critical thinking.”