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Forbes selects innovators for the list’s Healthcare & Science category, written by senior contributor Yue Wang. On the list is MIT PhD candidate Yuzhe Yang, who studies AI and machine learning technologies capability to monitor and diagnose illnesses such as Parkinson's disease.
MIT researchers have developed a new procedure that uses game theory to improve the accuracy and consistency of large language models (LLMs), reports Steve Nadis for Quanta Magazine. “The new work, which uses games to improve AI, stands in contrast to past approaches, which measured an AI program’s success via its mastery of games,” explains Nadis.
MIT researchers have used advancements in machine learning and computing to help decode whale vocalizations, reports Sarah Kuta of Smithsonian Magazine. “If researchers knew what sperm whales were saying, they might be able to come up with more targeted approaches to protecting them,” Kuta explains. “In addition, drawing parallels between whales and humans via language might help engage the broader public in conservation efforts.”
A new analysis of years of vocalizations by sperm whales in the eastern Caribbean has provided a fuller understanding of how whales communicate using codas, reports Will Dunham of Reuters. Graduate student Pratyusha Sharma explained that: "The research shows that the expressivity of sperm whale calls is much larger than previously thought."
New Scientist reporter Clare Wilson writes that a new analysis by MIT researchers of thousands of exchanges made by east Caribbean sperm whales demonstrates a communication system more advanced than previously thought. “It’s really extraordinary to see the possibility of another species on this planet having the capacity for communication,” says Prof. Daniela Rus.
Researchers from MIT and elsewhere have uncovered a phonetic alphabet used by sperm whales, which provides “key breakthroughs in our understanding of cetacean communication,” reports Brain Heater for TechCrunch. “This phonetic alphabet makes it possible to systematically explain the observed variability in the coda structure,” says Prof. Daniela Rus, director of CSAIL. “We believe that it’s possible that this is the first instance outside of human language where a communication provides an example of the linguistic concept of duality of patterning. That refers to a set of individually meaningless elements that can be combined to form larger meaningful units, sort of like combining syllables into words.”
Associated Press reporter Maria Cheng spotlights a new study by MIT researchers that identifies a “phonetic alphabet” used by whales when communicating. “It doesn’t appear that they have a fixed set of codas,” says graduate student Pratyusha Sharma. “That gives the whales access to a much larger communication system.”
Using machine learning, MIT researchers have discovered that sperm whales use “a bigger lexicon of sound patterns” that indicates a far more complex communication style than previously thought, reports Lauren Sommers for NPR. “Our results show there is much more complexity than previously believed and this is challenging the current state of the art or state of beliefs about the animal world," says Prof. Daniela Rus, director of CSAIL.
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.