Artificial intelligence

MIT researchers found that users preferred to consult professionals over AI chatbots, except when they are feeling embarrassment or shame, writes Forbes’ Joe McKendrick. “When we seek advice from other people, we’re often reluctant to share embarrassing details because we have a fear of being judged,” the researchers write. “People are more likely to turn to AI models to get professional advice when they’re embarrassed, because the model doesn’t judge them.” 

Prof. Daron Acemoglu discusses the impact of AI models on jobs in an article by CNBC’s Trevor Laurence Jockims. “If AI continues to be developed as an automation technology, there will be more impacts. Right now, we don’t have many easy-to-use applications relevant for a large number of tasks/industries,” says Acemoglu. “Once these are developed, the labor market effects will be multiplied.”  

President Emeritus L. Rafael Reif joins Yahoo Finance’s Julie Hyman to discuss how new policies regarding student visas, coupled with cuts to federal funding for universities, are severely disrupting America’s innovation economy. “The whole innovation ecosystem we have in America has been the best since World War II and has created this tremendous economy for the country. A great deal of that economy has to do with federal funding of research, doing it in a nonpoliticized way, and having the best people come from other countries to America to study with us, and to learn with us,” says Reif. “All of that ecosystem is being affected, and that makes it very uncomfortable for the experts who came here, to be here, to study here, and to stay here.” 

Prof. Max Tegmark joins David Gura and Christina Ruffini, hosts of “Bloomberg This Weekend,” to discuss briefing the U.S Senate on regulating AI, in wake of recent reports of rogue agents. “I want to emphasize for anyone who gets stressed by this, that this dystopian future where we build smarter than human AIs and robots, super intelligence, and then they take over Earth, is not at all inevitable,” says Tegmark.  

Prof. Justin Solomon joins GBH’s “The Curiosity Desk" to explain the Navier-Stokes problem and the significance of this longstanding challenge being solved by an AI platform. “There are concerns and there’s excitement,” says Solomon. “Obviously, working on the Navier-Stokes equations and other open problems shows that these AI tools are capable of assisting with proofs, or at least counter examples in this particular case, of really hard questions that have been open for centuries.” 

Prof. Sherry Turkle speaks to Scientific American’s Brianne Kane about her forthcoming book, “Artificial Intimacy: Who We Become When We Talk to Machines.” “Social media got us into the attention economy, but chatbots are more toxic because they’re coming after the attachment economy,” says Turkle. “They’re coming after who you think you love, who you think you can count on, what you think empathy is.”  

In a Scientific American article by reporter Joseph Howlett, Prof. Naomi Sweeting discusses the potential pitfalls of consulting artificial intelligence in mathematics. “I view math as part of a vast and beautiful human project,” says Sweeting. “It's this huge endeavor that puts us in communication with thousands of years of human thinking.” 

Prof. Sherry Turkle’s latest book “Artificial Intimacy: Who We Become When We Talk to Machines,” is highlighted as one of the best new science books of September 2026 by New Scientist’s Liz Else. In her new book, Turkle explores how machines “are producing a generation more alienated, depressed and lonely than ever – and less equipped to reverse course, as machine relationships offer no practice for getting along with people,” writes Else.  

MIT researchers have developed an AI-powered robotic therapy system that combines wearable force sensors and camera recordings of physical therapists’ movements to provide personalized therapy to patients, writes Newsweek’s Claudia Tanner. “Robotic assistance will allow patients to practice exercises more often while still receiving support tailored to their abilities,” says Johannes Lachner, who completed this work as a postdoctoral fellow at MIT. “Our goal is to take a step toward more accessible and personalized physical rehabilitation, giving more patients the opportunity to regain movement and independence.” 

Writing for The Boston Globe, President Emeritus L. Rafael Reif describes the need to develop a universal “moral floor,” like the Ten Commandments, which provide clear guidelines for the use of AI. “Today, people are creating and developing intelligence that is much more capable, in narrow ways, than our own,” Reif explains. “We owe humanity the same discipline to civilize AI before it is too late: Write down what lines must never be crossed, make the list short enough to remember, and hold every actor building it, and using it, accountable for its actions.”

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.  

MIT Media Lab researchers found that using large language models for writing assistance was associated with weaker recall and lower ownership of the work produced, writes Forbes’ Glenn Llopis. “Notice the pattern: when AI does too much, the person may retain too little,” writes Llopis.  

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.” 

Inside Higher Ed’s Emma Whitford spotlights a new report by MIT’s Ad Hoc Committee on AI Use in Teaching, Learning, and Research Training that offers recommendations for how AI should be used in education. “Learning works when it’s both challenging and social; knowledge is built through cognitive friction, whether that’s disentangling the steps of a mathematical proof with your study group, adjusting an experiment over and over until it works, or having a spirited argument with a peer (rather than getting ‘the’ answer from AI),” the committee writes.