Machine learning

Prof. David Autor speaks to Financial Times reporter Christian Davies about the increased use of physical AI systems at U.S. manufacturing companies. “[Autor says] that while individual companies might increase headcount as they grow more productive, it is a fallacy to assume this will be replicated across the manufacturing sector as a whole, noting that previous waves of automation in the US have all driven large-scale declines in blue-collar employment,” writes Davies.  

Prof. Emeritus Kerry Emanuel speaks to The New York Times’ William J. Broad about the use of AI models in predicting hurricanes. “It’s human beings who have to make these calls,” says Emanuel. “[Eventually] AI will be treated as just another form of guidance” along with satellite images and readings from hurricane hunter aircraft that pierce the tempests. 

Prof. Regina Barzilay, graduate student Aziz Ayed and alumna Sydney Pham ‘24 are featured in an article about AI models and the workforce, written by Nature’s Ben Deighton. “Sometimes models can make sense of unexpected observations, which can guide further iterations of studies,” Pham says. “It’s helpful to understand how the models generate their results.” 

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.” 

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.” 

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.” 

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 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.  

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.” 

Forbes’ “50 Over 50” list spotlights four MIT faculty leading innovation in science and technology: Prof. Paula Hammond, dean of the MIT School of Engineering; Prof. Dina Katabi; Prof. Nergis Mavalvala, dean of the MIT School of Science; and Aude Oliva, director of MIT-IBM Computing Research Lab. The 50 “science and technology standouts on this list are advancing how we use AI, fight osteoporosis and deploy clean energy.”  

Prof. Joshua Tenenbaum discusses whether AI systems will perform better if they have increased awareness of the world around them with New Scientist’s Daniel Cossins. “One of the big misconceptions is that intelligence is a single thing, that there is a single world model in the brain,” says Tenenbaum. “What we actually have is the ability to run many different models depending on the context, task and goal.”