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 discovered that when individual images are removed from large AI diffusion models, the models are still able to produce like images, writes Digital Trends’ Varun Mirchandani. “The more interesting takeaway is that this connection becomes increasingly difficult to trace as datasets grow,” writes Mirchandani. “An AI-generated image may draw on patterns learned from an enormous pool of material without having a clear, identifiable source image behind it.”
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.
The Register’s Thomas Claburn highlights a new study by MIT researchers that found large AI models can still reproduce an artist’s style, even when the artist’s work is removed from its training data, making attribution more difficult. “[G]iven the contemporary adoption of these models for creative and commercial purposes, attributability also carries ethical, policy, financial, and legal implications,” the researchers note.
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.”
By analyzing brain scans from 772 people, MIT researchers have identified 17 additional language-responsive regions in the brain, reports The Dallas Express (DX). “For decades, scientists have focused on regions such as Broca’s area and areas of the left frontal and temporal lobes as the brain’s primary language-processing centers,” writes DX. “The new study found that language-responsive regions are also located in other parts of the brain, including the cerebellum, hippocampus, amygdala, medial frontal cortex, and temporal lobe.”
VICE’s Luis Prada spotlights Prof. Tristan Brown’s discovery that a Chinese tea label on display as a Boston Tea Party artifact at Boston’s Old South Meeting House was actually created during the 19th century. After Brown translated the label from Cantonese, it “revealed an American name, 'Smith, Archer,' as it identified Smith, Archer and Co., a New York trading firm active in East Asia during the 1860s and 1870s,” writes Prada.
MIT researchers found that contrary to popular assumptions, lower birth rates have raised rather than lowered the Gross Domestic Product globally, writes The Dispatch’s Eli Kronenberg. “Across the board, we found that these lower birth rates were leading to a faster pace of technological adoption and progress,” says graduate student Keelan Beirne, a co-author on the paper.