Principal Research Scientist Deepjyoti Deka speaks to MassLive’s Tréa Lavery about the type of information stored by data centers. “Anything, mostly, that you are using the computer for, that you are getting some information from, is housed in a data center,” says Deka. “A lot of it is active data handling, but some of it is also data storage.”
In a conversation with Slate’s Shirin Ali, Charles Stewart III director of MIT’s Election Data and Science Lab, discusses a framework to help voters “see important red flags in the hours immediately following Election Day,” detailed in his new paper “The Day After: Threats and Guardrails in the 2026 Election.” “After Election Day, that’s where we’re thinking about getting the final count in,” says Stewart. “We shift into the formal process of canvassing the vote, certifying it, and making sure that we’ve named the winners.”
An analysis by Prof. Charles Stewart III, director of MIT’s Election Data and Science Lab, found that requests for mail-in ballots were down in Georgia, Maine, North Carolina, Pennsylvania and Wisconsin earlier this month, reports Patrick Marley, Leslie Shapiro and Laura Meckler for The Washington Post. “Those five states can be more fully analyzed than others because they provide data showing how voting patterns compare with the same stage of the election cycle in 2022,” note Marley, Shapiro and Meckler. “In data collected four weeks before Election Day, requests were down most sharply in Georgia — by 50 percent compared with the same point in 2022.”
Flapping Airplanes, a startup co-founded by Benjamin Spector ’22, MEng ’22, has developed a framework for AI models to run efficiently while being trained on less data and in a single graphics processing unit (GPU), writes Julie Bort for TechCrunch. The framework “can run a task that spends most of its time waiting for data to be fetched alongside one that spends most of its time doing math, so the chip is rarely sitting idle,” notes Bort. “The premise is that AI has barely scratched the surface of what GPUs can do, and they believe it can create intelligent systems ‘without ingesting half the internet.’”
Principal Research Scientist Deepjyoti Deka speaks to MassLive’s Tréa Lavery about the possibility of large data centers being located in greater Boston. “Anything, mostly, that you are using the computer for, that you are getting some information from, is housed in a data center,” says Deka. “When you talk about data centers in greater Boston, I doubt if we have any hyperscale, because there would need to be a gigantic factory that you could observe from a distance. But there are small ones.”
Fast Company’s Adele Peters highlights Firma Power, a clean energy developer co-founded by MIT alumni aimed at working with data centers to use clean alternatives to gas power. “We started the company with the hypothesis that despite the fact that 98% of what’s in the queue is wind, solar and storage, that’s not getting tapped as the default path to power for large loads—so there’s got to be some market frictions and barriers there,” says Jesse Jenkins SM ’14, PhD ’18.
Prof. Ankur Moitra and Prof. John Urschel share their thoughts with Forbes’ Alex Knapp about OpenAI releasing more than 700 math preprint papers online. “We’re used to landmark results being accompanied by new insights and theories,” writes Moitra. “But these feel different. The claims are spectacular, the papers are incomprehensible and there’s no punchline in sight that explains why the mysteries suddenly unraveled.” Adds Urschel: “It’s obvious that how we do and evaluate mathematics is going to have to change. What I think is constant is the fundamental idea of understanding that underpins our profession. And I’m excited to learn from these breakthroughs.”
Prof. Pat Pataranutaporn and graduate student Manuel Cherep speak to the Scientific American’s Mary Randolph about companies creating AI agents to assist users in personal tasks. “We have this narrative crisis about AI right now. There’s two opposite visions of the future, and while people worry about AI,” says Pataranutaporn, “there’s this cute AI tool that’s being released.”
During Climate Week NYC, Evelyn Wang, MIT’s Vice President for Energy and Climate, emphasized the importance of utilizing AI to help tackle climate change, write Seth Borenstein and Jennifer McDermott for The Associated Press. Data centers adding to climate and water problems ‘is not forever,’ according to Wang, who told The Associated Press that data centers are about five years away from being water neutral and about a decade away from no longer adding to planet-warming emissions.
MIT researchers found that AI assisted work leads to the ‘diversity collapse’ of a brand’s voice, and users that rely on AI to do their thinking lose skills over time, highlights Forbes’ Michelle O'Connor. “Judgment, taste and the instinct to catch when something’s off isn’t a soft skill you can afford to lose,” writes O’Connor. “The MIT data is the first hard evidence of that.”
MIT alumni Tom Brown ‘10, co-founder of Anthropic, and Edwin Chen ‘08, founder of Surge AI, are highlighted among the 10 youngest billionaires on the 2026 “Forbes 400 List,” by reporter Alicia Park. Brown “was lead author of the 2020 paper introducing GPT-3, the language model that demonstrated such systems could write fluently and triggered the current investment boom in artificial intelligence,” writes Park. Chen’s Surge AI, “employs people to label data, as well as rate and correct AI-generated answers to train models.”
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.”
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.