Machine learning

Prof. Daniel Huttenlocher, dean of MIT’s Schwarzman College of Computing, joins Fox News’ “Fox and Friends” to discuss his views on the role of human responsibility in AI’s evolution. “With conventional software, when you program something, you give the machine precise instructions of what to do, so the humans developing it have a much more concrete understanding of what it can and can’t do,” says Huttenlocher. “With an AI system instead, it’s learned much more general skills and what you tell it to do is achieve some sort of a goal, but you don’t tell it the specific way it should achieve that goal. We can’t control it [AI] in the same ways that we control traditional software, but that doesn’t mean we shouldn’t control it and that we can’t control it, it’s just different.” 

Prof. Gary Gensler joins CNBC’s “Squawk Box” to discuss the AI race and shares his suggestions for government regulations. “I do feel that there’s a need for more responsible regulation of artificial intelligence,” says Gensler. “These existential risks [conflicts of interest, explainability, and bias], however you measure them, are much harder to control because you’ve got the competition between Chinese model developers, [and] U.S. model developers.”  

Prof. Dylan Hadfield-Menell speaks to The Boston Globe’s Joshua Miller about his concerns with the current state of AI systems and how they are optimized. “The parts [of AI systems] that are imitating the ways that humans use language, those parts of the systems actually are quite correctable. They’re still pretty unpredictable, but they do seem quite flexible,” says Hadfield-Menell. “On the other hand, there’s reinforcement learning, task-focused behavior, that can often be quite sticky. They [AI systems] seem to really want to push towards task completion.” He adds: “If you have a powerful optimization-driven system pointed at some goal, it can often do a lot of unexpected things that can have a lot of impact.” 

Prof. Shafi Goldwasser and Prof. Vinod Vaikuntanathan founded the Institute for Responsible Superintelligence (RESI), “to develop mathematical principles and protocols to ensure AI models behave as intended,” writes Aaron Pressman for The Boston Globe. “You patch them [AI models] up, you test them, you patch them to make sure that they can perform to the best ability, but also not do damage,” says Goldwasser. “That’s probably not enough attention, given the fact that these things are going to become what we call super intelligent.” 

In an article for The Wall Street Journal, Prof. Daniel Huttenlocher, dean of MIT’s Schwarzman College of Computing writes that: “AI is an extremely powerful and beneficial technology. But accountability for its actions remains with the people and institutions that grant it the power to act, and so does everything that follows: the duty to justify the arrangement, to watch it, to correct it and to stop it when necessary.”  

The Boston Globe’s Hiawatha Bray spotlights Prof. Max Tegmark speaking at the “Pro-Human Assembly” in Washington, an event advocating for government regulations on AI, sponsored by Tegmark’s Future of Life Institute. “We are here today because we know that what’s up next is a fork in the road,” said Tegmark at the assembly. “We know that the future of AI is not inevitable. It is for us to decide.”  

Members of the MIT community speak to Boston 25 News reporter Lisa Gresci about how they use AI, and their concerns with regulating the technology. Developers should consider, “how AI can help communities so [they are] not just building things, but are also deploying it [with purpose] out in the world,” says graduate student Anku Rani.  

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.”  

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.  

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.