Download RSS feed: News Articles / In the Media / Audio
MIT researchers have discovered that sperm whales use a “much richer set of sounds than previously known, which they call a ‘sperm whale phonetic alphabet,’” reports Carl Zimmer for The New York Times. “The researchers identified 156 different codas, each with distinct combinations of tempo, rhythm, rubato and ornamentation,” Zimmer explains. “This variation is strikingly similar to the way humans combine movements in our lips and tongue to produce a set of phonetic sounds.”
Prof. Yoon Kim speaks with USA Today reporter Eve Chen about how AI can be used in everyday tasks such as travel planning. “AI is generally everywhere,” says Kim. “For example, when you search for something – let’s say you search for something on TripAdvisor, Hotels.com – there is likely an AI-based system that gives you a list of matches based on your query.”
Researchers from MIT and elsewhere have used an AI model to develop a “new approach to finding money laundering on Bitcoin’s blockchain,” reports Andy Greenberg for Wired. “Rather than try to identify cryptocurrency wallets or clusters of addresses associated with criminal entities such as dark-web black markets, thieves, or scammers, the researchers collected patterns of bitcoin transactions that led from one of those known bad actors to a cryptocurrency exchange where dirty crypto might be cashed out,” explains Greenberg.
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.”
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.”
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.
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."
Boston Magazine spotlights MIT’s leading role in the AI revolution in the Greater Boston area. “With a $2 million grant from the Department of Defense, MIT’s Artificial Intelligence Lab combines with a new research group, Project MAC, to create what’s now known as the Computer Science and Artificial Intelligence Laboratory (CSAIL). Over the next three years, researchers lead groundbreaking machine-learning projects such as the creation of Eliza, a psychotherapy-based computer program that could process languages and establish emotional connections with users (a primordial chatbot, essentially).”
Boston Globe reporter Aaron Pressman spotlights the Perkins School for the Blind Hackathon held at MIT. “The students divided into 10 teams, named after colors, and picked one of eight challenges Perkins had crafted, such as assisting a blind person to navigate an indoor space or to pick up non-verbal cues in video conference conversations,” writes Pressman. “But before writing a line of code, the teams met with people with a disability relevant to the challenge they had selected.”
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.”
Prof. Mike Stonebraker co-founded DBOS, a serverless software platform, that aims to “put a database system at the bottom of the technology stack as close to the bare metal as possible where the operating system usually sits,” reports Ron Miller for TechCrunch. “Bare metal is a term used to describe the pure hardware layer where no software exists. Flipping the OS and the database is a bold and revolutionary idea,” explains Miller.
Prof. Thomas Malone speaks with Poets & Quants for Executives reporter Alison Damast about the executive education course he teaches with Prof. Daniela Rus that aims to provide senior-level managers with a better sense of how AI works. “We are certainly not trying to teach people to understand the details of how to write AI programs, though some of those in the course may know that already,” Malone says. “What we are trying to do is give them a sense of when it is easy and when it is hard to use AI technology at various times for different kinds of business applications.”