Nature reporter Amanda Heidt speaks with postdoctoral researcher Tigist Tamir about her experience using generative AI with attention-deficit hyperactivity disorder. “Whether I’m reading, writing or just making to-do lists, it’s very difficult for me to figure out what I want to say. One thing that helps is to just do a brain dump and use AI to create a boiled-down version,” Tamir explains. She adds, “I feel fortunate that I’m in this era where these tools exist.”
Postdoc Xuhai Xu and his colleagues have developed an AI program that can distribute pop-up reminders to help limit smartphone screen time, reports Jeremy Hsu for New Scientist. Xu explains that “a random notification to stop doomscrolling won’t always tear someone away from their phone. But machine learning can personalize that intervention so it arrives at the moment when it is most likely to work,” writes Hsu.
U.S. News & World Report reporter Cole Claybourn spotlights Amar Gopal Bose '51, SM '52 ScD '56, a former MIT faculty member, as one of fifteen famous Fulbright scholars. Bose, founder of Bose Corporation, “studied at the Massachusetts Institute of Technology on a full scholarship, earning his bachelor’s, master’s and doctoral degrees in electrical engineering,” writes Claybourn.
A new report by researchers from MIT and Boston Consulting Group (BCG) has uncovered “how AI-based machine learning and predictive analytics are super-powering key performance indictors (KPIs),” reports Sheryl Estrada for Fortune. “I definitely see marketing, manufacturing, supply chain, and financial folks using these value-added formats to upgrade their existing KPIs and imagine new ones,” says visiting scholar Michael Schrage.
FutureTech researcher Tamay Besiroglu speaks with New Scientist reporter Chris Stokel-Walker about the rapid rate at which large language models (LLMs) are improving. “While Besiroglu believes that this increase in LLM performance is partly due to more efficient software coding, the researchers were unable to pinpoint precisely how those efficiencies were gained – in part because AI algorithms are often impenetrable black boxes,” writes Stokel-Walker. “He also points out that hardware improvements still play a big role in increased performance.”
Financial Times reporters Andrew Hill and Emma Jacobs spotlight how Prof. Carlo Ratti and his colleagues have found that “people are traveling less far from their homes, consistent with more remote working.” The findings demonstrate a more flexible approach to commuting. “That’s good news [for cities], because the problem is not a problem of capacity, it’s a problem of peaks,” Ratti explains.
In an article for Forbes, Sloan Research Scientist Ranjan Pal and Prof. Bodhibrata Nag of the Indian Institute of Management Calcutta highlight the risks associated with the rise of Internet of things-driven smart cities and homes. “Unlike traditional catastrophic bond markets, where the (natural) catastrophe does not affect financial stability, a cyber-catastrophe can affect financial stability,” they write. “Hence, more information is needed by bond writing parties to screen cyber-risk exposure to guarantee no threat to financial stability.”
Prof. David Autor speaks with Bloomberg’s Odd Lots podcast hosts Joe Weisenthal and Tracy Alloway about how AI could be leveraged to improve inequality, emphasizing the policy choices governments will need to make to ensure the technology is beneficial to humans. “Automation is not the primary source of how innovation improves our lives,” says Autor. “Many of the things we do with new tools is create new capabilities that we didn’t previously have.”
Prof. David Autor and Prof. Daron Acemoglu speak with New York Times columnist Peter Coy about the impact of AI on the workforce. Acemoglu and Autor are “optimistic about a continuing role for people in the labor market,” writes Coy. “An upper bound of the fraction of jobs that would be affected by A.I. and computer vision technologies within the next 10 years is less than 10 percent,” says Acemoglu.
MIT researchers have found that “when an AI tool for radiologists produced a wrong answer, doctors were more likely to come to the wrong conclusion in their diagnoses,” report Daniel Payne, Carmen Paun, Ruth Reader and Erin Schumaker for Politico. “The study explored the findings of 140 radiologists using AI to make diagnoses based on chest X-rays,” they write. “How AI affected care wasn’t dependent on the doctors’ levels of experience, specialty or performance. And lower-performing radiologists didn’t benefit more from AI assistance than their peers.”
Alumnus Benjamin Rapoport co-founded Precision Neuroscience, a brain-computer interface company, that is developing technology that will allow “paralyzed patients the ability to operate a computer with their thoughts,” reports Jo Craven McGinty for The Wall Street Journal. “In order to be a citizen of the world in 2024, to communicate with loved ones, to make a living, the ability to work with a digital system is indispensable,” says Rapoport. “To operate a word processor is totally transformative.”
Washington Post columnist Eduardo Porter spotlights various MIT research efforts aimed at ensuring that AI is used to empower human workers rather than replace them. Prof. David Autor notes: “AI, if used well, can assist with restoring the middle-skill, middle-class heart of the U.S. labor market that has been hollowed out by automation and globalization.”
Harry Rein '15, MEng '16 and Chris Tinsley MBA '20 co-founded ShopMy, a marketing platform designed to connect content creators with brands and monetize their content, reports Laruen Forristal for TechCrunch. “ShopMy’s marketing platform equips creators with the tools they need to earn from their product recommendations, like building digital storefronts, accessing a catalog of millions of products, making commissionable links and chatting directly with companies via mobile app,” explains Forristal.
Prof. Pulkit Agrawal and graduate student Gabriel Margolis speak with The Economist’s Babbage podcast about the simulation research and technology used in developing intelligent machines. “Simulation is a digital twin of reality,” says Agrawal. “But simulation still doesn’t have data, it is a digital twin of the environment. So, what we do is something called reinforcement learning which is learning by trial and error which means that we can try out many different combinations.”