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.
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’ 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.”
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 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.”
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.”
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.”
Boston.com’s Elizabeth Mehler spotlights an AI-powered flood predictor and disaster response tool built by Arush Shangari, an MIT Beaver Works Research Scholar. “Through MIT Beaver Works and MIT CSAIL, I taught myself artificial intelligence and time-series forecasting,” says Shangari. “There was a lot of that error analysis and just learning in general.”
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.”
Prof. Max Tegmark joins Steve Clemons, host of Al Jazeerah’s, “The Bottom Line,” to discuss regulating AI. “Normally with technology we have a problem we want to solve: cure cancer, be able to get faster from point A to point B, and then we develop technology to meet those needs, to solve those problems—and that’s how we should deal with AI also,” says Tegmark. “We should look at what we would like to have accomplished in our society and then companies can sell products that solve those problems without causing a bunch of harm.”
Prof. Daron Acemoglu discusses how issues with AI and social media inform his new book, “What Happened to Liberal Democracy?” in an article by Fortune’s Nick Lichtenberg. “We live in an environment that’s been partly shaped by social media, and there is a tendency to escalate everything, because that gets attention,” says Acemoglu. “Politics is like that. The other topic like that, unfortunately, is AI.”