Research

Postdoctoral Associate Iman YeckehZaare speaks to El País reporter Jordi Pérez Colomé about his research on RentAHuman, a platform that allows AI agents to hire people to do physical labor. “RentAHuman has shown that many people are willing to sign up to be hired by AI agents. What it still hasn’t shown is that many AI agents are doing the hiring,” says YeckehZaare. “Listings posted by agents will increase as AI agents become capable of acting and paying on their own.”  

In The Boston Globe’s “Starting Point Newsletter,” reporter Anna Kusmer features Prof. Sherry Turkle’s insights from her latest book “Artificial Intimacy: Who We Become When We Talk to Machines,” including the risks that AI companionship poses to school-aged children. “Children are bringing the world inside; they’re bringing the people around them inside them to create an inner life,” says Turkle. “And when you talk to a chatbot, there is nothing to bring inside. There is no caring person.” 

In an interview with Wired’s Steven Levy, Prof. Sherry Turkle discusses how she witnessed chatbots with human-like features becoming “dangerously enmeshed” with people, during research for her latest book “Artificial Intimacy: Who We Become When We Talk to Machines.” “In every serious interview I do, people invoke this myth about practice—that you use chatbots to practice for talking to your boss, your lover, your teacher,” says Turkle. “But when you’re bathing in the warm soup of total acceptance, talking to chatbots becomes the endgame.” 

Testing 1,000 synthetically generated client profiles against AI chatbots for investment advice, Prof. Andrew Lo and Research Affiliate Jillian Ross identified “heuristic collapse,” where chatbots based their recommendations almost entirely on self-reported risk tolerance, while ignoring income and other variables, writes Damilola Esebame for The Street. “Lo has recommended that investors use chatbots for orientation, leveraging them to frame questions, map out available options, and prepare for conversations with a human advisor rather than treating the output as a finished plan,” notes Esebame. 

Maxime Cohen PhD ‘15 speaks to Forbes’ Karl Moore about his latest book “Pricing in the Age of AI,” that explores how AI is transforming pricing decisions for businesses, consumers and society. “We may be entering an era of agent-to-agent commerce, in which AI systems negotiate directly with one another on behalf of consumers and businesses,” says Cohen. “This could fundamentally reshape how prices are determined and how transactions take place.” 

Washington Post reporter Ariana Eunjung Cha spotlights Prof. Pattie Maes’ research at the intersection of AI and memory, including her wearable AI devices designed to assist older adults that “capture pieces of everyday experience and make them retrievable later.” Maes’ research “takes on particular urgency for people with Alzheimer’s disease and other forms of dementia, diseases defined by memory loss that can rob a person’s ability to navigate everyday life independently,” writes Eunjung Cha.  

Using satellite data, graduate student Caroline Needell and her colleagues found that the risk of runaway collapse in tall ice cliffs “depends on two main factors: how steeply the sea floor beneath the glacial ice deepens, and how quickly the ice flows seawards,” writes Marissa Grunes for New Scientist. “This idea has been suggested by idealized mathematical models, but had never been systematically measured in real glaciers,” notes Grunes. “The key question, says Needell, is whether thinning [from runaway collapse] can outcompete rapid ice loss, restabilizing the glacier. Her study suggests that it might.”  

Prof. Sherry Turkle joins The Boston Globe’s Anna Kusmer, host of the “Say More” podcast, to discuss how AI chatbots can significantly deskill school-aged children, as detailed in her latest book, “Artificial Intimacy: Who We Become When We Talk to Machines.” “People in the thick of this know it is not good for children, it’s a deskilling,” says Turkle. “If your chatbot reads for you, you don’t learn to read, you learn to listen to a podcast about a book, if your chatbot writes for you, you don’t learn to think, because thinking is writing, you learn to sort of edit your chatbots ideas: you learn to prompt.”  

At the STAT Summit, President Sally Kornbluth joined Holden Thorp, editor-in-chief of the Science family of journals, for a panel moderated by STAT’s Anil Oza. During the conversation, Kornbluth emphasized the importance of curiosity-driven research and education at institutions like MIT, highlighting MIT for America, a new initiative aimed at ensuring learners and educators from Kindergarten through community college across the country have access to STEM education. “We want to both meet people where they are, give their kids the opportunity, and also make them understand us as people who care about their communities,” says Kornbluth.

MIT researchers have developed the Probabilistic Bias Correction (PBC) tool, which “uses machine learning to identify systematic bias in a model and make corrections” to improve weather forecasts, reports The Verge’s Justine Calma. “The researchers found that PBC improved the accuracy of a leading physics-based weather model by the European Centre for Medium-Range Weather Forecasts (ECMWF) for 98 percent of precipitation and 92 percent of temperature targets,” notes Calma. “The machine learning framework also doubled the accuracy of ECMWF’s new AI forecasting system in the study’s analysis.” 

Gizmodo’s Ellyn Lapointe spotlights the Probabilistic Bias Correction (PBC) tool developed by MIT researchers to “correct systemic errors in forecast models and improve the accuracy of their subseasonal outlooks.” “Extreme weather has the largest impact on our society and improved forecasts of these events can yield large societal benefits,” says Research Scientist Judah Cohen. “Interestingly, previous bias correction schemes actually made forecasts of extreme weather worse, and our model showed clear improvement of extreme weather forecasts.”  

On The New York Times’ “Ezra Klein Show,” Prof. Sherry Turkle shares her insights on humans seeking companionship with AI chatbots, detailed in her latest book, “Artificial Intimacy: Who We Become When We Talk to Machines.” In relationships with chatbots, “on the one hand, people start to see the chatbots more like people,” says Turkle. “But I think, maybe more significantly, people start to be disappointed with people when they don’t behave like chatbots. They start to think of themselves as if they were computers.”  

MIT researchers have developed an AI system “that not only minimizes forecasting errors, but learns from its inevitable mistakes through a method called postprocessing,” writes reporter Andrew Paul for Popular Science. “Their AI postprocessing integration blew the existing forecasting models out of the water,” notes Paul. “Such massive improvements could soon help the farmers and energy providers who already rely on subseasonal forecasts, but the benefits may even save lives.” 

Research led by Principal Research Scientist Neil Thompson suggests that AI is likely to be upend theoretical scientific fields within a few years, writes Wired’s Will Knight. “As with math, Thompson says, we should expect advances in other areas to have both positive and negative effects,” writes Knight. “He notes that theoretical advances sometimes drive practical advances in science and suggests that greater use of AI may mean that more progress happens this way.” 

MIT spinout Cambridge Heavy Industries is developing a desktop-sized fusion reactor that can enable 50 to 80 experiments per day, writes TechCrunch’s Tim De Chant. “Cambridge Heavy’s fusion reactor uses lasers to compress a fuel target, similar to the groundbreaking experiment that proved controlled fusion reactions could produce more power than they consume,” notes De Chant. “But unlike other inertial confinement designs, Cambridge Heavy’s minimizes the number of lasers required to spark fusion.”