A new report by Principal Research Scientist Andrew McAfee explores the “implications of generative AI in economic growth, looking at everything from its possible effects on job skills and wages to how it may transform entire industries to its potential risks and pitfalls,” reports Sheryl Estrada for Fortune.
Researchers for MIT and Google are providing a free “Generative AI for Educators Course,” with the aim of helping middle and high school teachers use generative AI tools in the classroom. “MIT RAISE believes knowledge of generative AI is a key factor in creating a more equitable future for education,” says Cynthia Breazeal, director of MIT RAISE. “We’re thrilled to collaborate with Google to offer the Generative AI for Educators Course – providing middle and high school teachers with no-cost AI training. This course empowers educators to confidently integrate AI into their teaching, creating richer and more accessible learning experiences for all students.”
ShareAmerica reporter Lauren Monsen spotlights Prof. Dina Katabi for her work in advancing medicine with artificial intelligence. “Katabi develops AI tools to monitor patients’ breathing patterns, hear rate, sleep quality, and movements,” writes Monsen. “This data informs treatment for patients with diseases such as Parkinson’s, Alzheimer’s, Crohn’s, and ALS (amyotrophic lateral sclerosis), as well as Rett syndrome, a rare neurological disorder.”
MIT researchers have developed a machine-learning accelerator chip to make health-monitoring apps more secure, reports Aman Tripathi for Interesting Engineering. “The researchers subjected this new chip to intensive testing, simulating real-world hacking attempts, and the results were impressive,” explains Tripathi. “Even after millions of attempts, they were unable to recover any private information. In contrast, stealing data from an unprotected chip took only a few thousand samples.”
Bloomberg Opinion columnist Parmy Olson spotlights a new study by MIT researchers that finds AI chatbots can be highly persuasive when reinforced with facts and could potentially be used to help tackle conspiracy theories. “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,” Olson writes. “On average, participants subsequently described themselves as 20% less confident in the conspiracy theory; their views remained softened even two months later.”
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.”
MIT and Google are offering a free Generative AI for Educators course “designed to help middle and high school teachers learn how to use generative AI tools to personalize instruction, develop creative lessons and save time on administrative tasks,” reports Jack Kelly for Forbes.
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.
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.
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.”
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."