Data

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.  

In a STAT opinion piece, Arya Rao, a candidate in the Harvard/MIT MD-PhD program, and Marc Succi write about how AI impacts clinical reasoning. “[C]linical reasoning and model reasoning are not the same thing,” Rao and Succi explain. “AI not only lacks this hidden framework and data repository for learning, but it arrives at conclusions using a fundamentally different method.” 

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 new study by MIT FutureTech, 272 researchers ranked the top 24 AI risks, write Axios reporters Jim VandeHei and Mike Allen. The researchers “assessed a 12% chance that AI's dangerous capabilities produce a catastrophic outcome by 2030, and another 12% chance of AI-enabled weapons and mass-harm capabilities [with mitigation efforts to reduce risk],” VandeHei and Allen explain.  

Prof. Tom Leighton, CEO of Akamai, speaks to Fast Company reporter Victor Dey about his vision for a faster and cheaper AI grid that could serve as an alternative to large data centers, and more effectively route inference workloads. “Many inference tasks do not need the largest model or the largest cluster or the most expensive compute,” says Leighton. “What they need is the ability to marry the right model, in the right place, data and moment, at the most effective cost.” 

Prof. Alessandro Acquisti speaks with NPR’s Scott Neuman about privacy issues related to surveillance by autonomous vehicle (AV) companies. “There already exist laws that govern duty to report or even duty to protect [for AV companies],” Acquisti says. “The privacy problems arise when and if driverless carrier companies used such laws or ethical obligations as a pretext for blanket, indiscriminate accumulation of identifiable data for unspecified future purposes.” 

The Economist’s “Bartleby Newsletter” spotlights a survey led by Prof. Danielle Li that found American employees were less likely to opt into training AI after learning how their data could be used. "In an experiment, the researchers offered to buy survey data from participants; those who had been shown a video on how data could be used to train AI were less willing to sell,” writes Andrew Palmer. 

Writing for Forbes about efforts to improve air travel safety, Tanya Eves highlights the Air-Guardian system, an eye-tracking monitor for pilots developed by CSAIL researchers that assists when attention wavers. “In tests, it reduced flight risk and improved navigation success rates,” writes Eves. “It's a model for how the virtual co-pilot relationship should work: not replacement, but a seamless, intelligent partnership that understands when to act and when to stay silent.”

NPR reporter Jeff Brady spotlights a study by Prof. Jessika Trancik and Marco Miotti PhD ’20  that found “across most of the U.S., electric vehicles are cost-competitive with their gas counterparts. And it found that in most locations, EVs also reduce emissions between 40% and 60%.” 

Prof. Marzyeh Ghassemi speaks with Mark Herz, host of GBH Morning Edition, about the potential benefits and issues associated with using AI in medicine. “Where I really see a lot of fantastic opportunity is identifying spaces where humans don’t have a fundamental capacity, like early breast cancer detection where it’s a sub-clinical presentation,” says Ghassemi. “These are spaces where humans cannot do or have been proven not to be good at a very specific clinical task. And there, AI can really help close the gap.”