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Computer Science and Artificial Intelligence Laboratory (CSAIL)

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Displaying 16 - 30 of 668 news clips related to this topic.

Associated Press

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. 

New York Times

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.”

USA Today

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, – 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

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.”

New Scientist

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. 

New Scientist

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.”

Boston Magazine

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.

Boston Magazine

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).”

The Boston Globe

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.”

The Economist

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.”