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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.
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."