Machine learning

Researchers at MIT have found that AI can “be remarkably persuasive when reinforced with facts,” reports Parmy Olson for Bloomberg. “The scientists invited more than 2,000 people who believed in different conspiracy theories to summarize their positions to a chatbot — powered by OpenAI’s latest publicly available language model — and briefly debate them with the bot,” explains Olson. “On average, participants subsequently described themselves as 20% less confident in the conspiracy theory; their views remained softened even two months later.”

Scientific American reporter Riis Williams explores how MIT researchers created “smart gloves” that have tactile sensors woven into the fabric to help teach piano and make other hands-on activities easier. “Hand-based movements like piano playing are normally really subjective and difficult to record and transfer,” explains graduate student Yiyue Luo. “But with these gloves we are actually able to track one person’s touch experience and share it with another person to improve their tactile learning process.”

Boston Globe reporter Malcolm Gay spotlights the “AI: Mind the Gap” exhibition at the MIT Museum, “which explores the social, cultural, and political implications of deepfakes and other forms of generative AI.” The exhibit is “meant to address the idea that technology can manipulate what we perceive as true or false,” said Lindsay Bartholomew, exhibit content and experience developer for the MIT Museum. “But you also need to appreciate what you can do as a human, you have some agency here.”

Prof. David Autor speaks with PBS host Walter Isaacson about the fear surrounding AI’s impact in the workforce and his view that AI could provide new opportunities for middle class workers. “Most of the time, technology is good for the elite and not so good for everybody else,” says Autor. “[AI] is a case where the technology might compete a little bit more with the elite and enable more people to do valuable work,” resulting in higher wages and more job opportunity for the middle class. 

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.

Prof. David Autor speaks with New York Times reporter Steve Lohr about his hope that AI can be harnessed to become “worker complementary technology,” enabling individuals to take on more highly skilled work and find better paying jobs. “I do think there is value in imagining a positive outcome, encouraging debate and preparing for a better future,” Autor explains. “This technology is a tool, and how we decide to use it is up to us.”

Researchers at MIT have found that large language models mimic intelligence using linear functions, reports Kyle Wiggers for TechCrunch. “Even though these models are really complicated, nonlinear functions that are trained on lots of data and are very hard to understand, there are sometimes really simple mechanisms working inside them,” writes Wiggers. 

Forbes reporter Oludolapo Makinde spotlights research by Prof. Daron Acemoglu and Prof. Simon Johnson that explores the impact of AI on the workforce. “Instead of aiming to create artificial superintelligence or AI systems that outperform humans, [Acemoglu and Johnson] propose shifting the focus to supporting workers,” writes Makinde.

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.

Research Scientist Robert Ajemian, graduate student Greta Tuckute and MIT Museum Exhibit Content and Experience Developer Lindsay Bartholomew appear on The Economist’s Babbage podcast to discuss the development of generative AI. “The way that current AI works, whether it is object recognition or large language models, it’s trained on tons and tons and tons of data and what it’s essentially doing is comparing something it’s seen before to something it’s seeing now,” says Ajemian.  

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.”

A number of MIT faculty and alumni – including Prof. Daniela Rus, Prof. Regina Barzilay, Research Affiliate Haddad Habib, Research Scientist Lex Fridman, Marc Raibert PhD '77, former Postdoc Rana El Kaliouby and Ray Kurzweil '70 – have been named key figures “at the forefront of Boston’s AI revolution,” reports Wyndham Lewis for Boston Magazine. These researchers are “driving progress and reshaping the way we live,” writes Lewis.

MIT researchers have developed a new technique that uses a large language model to allow robots to self-correct after making a mistake, reports Brian Heater for TechCrunch. “Researchers behind the study note that while imitation learning (learning to do a task through observation) is popular in the world of home robotics, it often can’t account for the countless small environmental variations that can interfere with regular operation, thus requiring a system to restart from square one,” writes Heater. “The new research addresses this, in part, by breaking demonstrations into smaller subsets." 

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.”