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Time Magazine

Prof. Daniela Rus, Director of MIT CSAIL, and Prof. David Autor, department head of Economics, are featured on Time’s “TIME100 AI 2026” list of 100 innovators, leaders, and thinkers reshaping the world through their advances in AI. “Over three decades, [Rus] has conducted pioneering work in robotics, extending the understanding of what form robots can take,” writes Tharin Pillay, while Steven Freiss describes Autor as “one of the nation’s most vocal and prominent economists aiming to ascertain AI’s potential impacts on labor.” 

MassLive

MassLive’s Juliet Schulman-Hall covers a new report from the MIT Ad Hoc Committee on AI Use in Teaching, Learning and Research Training about AI and education at MIT. “It’s rare that a successful institution has to take a fresh look at many aspects of its mission,” said President Sally Kornbluth in a letter to the community. “But I’m convinced that the opportunities and risks generative AI poses for our model of education and research now constitute such a watershed for MIT — and for all of higher education.” 

Forbes

MIT researchers found that purchased AI tools succeed roughly twice as often as comparable in-house builds, Forbes’ Dennis Vorobyov highlights. “[S]o spend the engineering effort where you are genuinely differentiated,” writes Vorobyov. “Budget wise, none of this requires a moonshot, just clarity and sequencing.” 

Forbes

MIT’s Ad Hoc Committee on AI Use in Teaching, Learning, and Research Training has released a report on AI and education and recommendations for the Institute moving forward, writes Forbes’ Ron Schmelzer. “President Sally Kornbluth describes the moment as a ‘watershed’ for MIT and higher education,” writes Schmelzer. “The recommendations include redesigned assessments, renewed attention to hands-on learning, explicit AI rules for courses and new communities of practice for faculty.”

Scientific American

Prof. Emeritus Rodney Brooks speaks to Scientific American’s Mary Randolph about humanoid robots interacting with the real world. “When you see a performance of an [artificial intelligence] system or a robot on one thing,” says Brooks, “that fools us into thinking that it has the same general competence as a human. And that’s a mistake people make.” 

Gizmodo

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.” 

The Washington Post

In a message to campus, President Sally Kornbluth shared a new report by MIT’s Ad Hoc Committee on AI Use that found that “even as AI accelerates and expands discoveries,  it is also driving dramatic changes in campus culture, upending foundational experiences such as study groups, office hours and undergraduate research,” writes the Washington Post’s Susan Svrluga. “For the sake of our students, for the future of MIT and for the shape of our sector and our society,” says Kornbluth, “it’s imperative that we get this right.” 

Forbes

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.  

Forbes

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.” 

Forbes

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.

Financial Times

In a Financial Times opinion piece, Prof. Carlo Ratti discusses alternatives to implementing traditional air conditioning systems, which move rather than reduce heat, that could be used in Europe as temperatures continue to rise. “The best answer, then, is a mixed system, co-ordinated in real time, with sensors to track temperature, occupancy and demand, and algorithms deciding when to produce, store and distribute cooling in step with the electricity grid,” writes Ratti. 

Forbes

Forbes’ Bryan Robinson highlights Prof. Paul Osterman’s new book, “Disposable Workers: The Transformation of Employment,” and identifies what constitutes disposable work. “We’re not becoming a gig economy; we’re becoming a disposable one,” says Osterman. “Marginal workers are employees who have no career prospects at their organizations.” 

Fast Company

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.  

Digital Trends

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.” 

 

STAT

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.”