Studies by researchers at MIT have found “that shifting to electric vehicles delivers a 30% to 50% reduction in greenhouse gas emissions over combustion vehicles,” reports Tom Krisher for Associated Press. According to Prof. Jessika Trancik, “electric vehicles are cleaner over their lifetimes, even after taking into account the pollution caused by the mining of metals for batteries,” writes Krisher.
Prof. Daniela Rus, director of CSAIL, writes for Forbes about Prof. Dina Katabi’s work using insights from wireless systems to help glean information about patient health. “Incorporating continuous time data collection in healthcare using ambient WiFi detectable by machine learning promises an era where early and accurate diagnosis becomes the norm rather than the exception,” writes Rus.
Prof. Daniela Rus, director of CSAIL, emphasizes the central role universities play in fostering innovation and the importance of ensuring universities have the computing resources necessary to help tackle major global challenges. Rus writes, “academia needs a large-scale research cloud that allows researchers to efficiently share resources” to address hot-button issues like generative AI. “It would provide an integrated platform for large-scale data management, encourage collaborative studies across research organizations, and offer access to cutting-edge technologies, while ensuring cost efficiency,” Rus explains.
In an article for Forbes, research affiliate John Werner spotlights Prof. Dina Katabi and her work showcasing how AI can boost the capabilities of clinical data. “We are going to collect data, clinical data from patients continuously in their homes, track the symptoms, the evolution of those symptoms, and process this data with machine learning so that we can get insights before problems occur,” says Katabi.
Writing for Times Higher Ed, Prof. Carlo Ratti makes the case that in the wake of the U.S. Supreme Court’s ruling on affirmative action, big data and analytics could “help admissions officers quantitatively capture the kinds of disadvantages applicants face and the kinds of diversity they may represent.”
Neil Thompson, director of the FutureTech research project at MIT CSAIL and a principal investigator MIT’s Initiative on the Digital Economy, speaks with Politico reporter Mohar Chatterjee about generative AI, the pace of computer progress and the need for the U.S. to invest more in developing the future of computing. “We need to make sure we have good secure factories that can produce cutting-edge semiconductors,” says Thompson. “The CHIPS Act covers that. And people are starting to invest in some of these post-CMOS technologies — but it just needs to be much more. These are incredibly important technologies.”
Prof. Marzyeh Ghassemi speaks with NPR host Kate Wells about a decision by the National Eating Disorders Associations to replace their helpline with a chatbot. “I think it's very alienating to have an interactive system present you with irrelevant or what can feel like tangential information,” says Ghassemi.
Researchers from MIT have found that Twitter “bot detection tools can rely on funky, flawed data sets that replicate mistakes made within one another, rather than trying to accurately identify bots,” reports Chris Stokel-Walker for Fast Company. “We realized that this was a systemic issue in the data sets that are commonly used for bot detection,” says postdoc Zachary Schutzman.
Prof. Marzyeh Ghassemi speaks with NPR host Emily Kwong and correspondent Geoff Brumfiel about how artificial intelligence could impact medicine. “When you take state-of-the-art machine-learning methods and systems and then evaluate them on different patient groups, they do not perform equally,” says Ghassemi.
Researchers from the Future of Data Initiative at MIT have published a white paper examining “how to design more accountable and traceable financial data systems,” reports Ben Schreckinger for Politico. Senior research scientist Daniel Weitzner says information accountability “means that uses of personal data should be visible to data subjects. And that the companies or government who use personal data should be accountable for misuse.”
Popular Science reporter Jamie Dickman writes that using liquid neural networks, MIT researchers have “trained a drone to identify and navigate toward objects in varying environments.” Dickman notes that: “These robust networks enable the drone to adapt in real-time, even after initial training, allowing it to identify a target object despite changes in their environment.”
Researchers at MIT have developed a new type of autonomous drone that uses advanced neural networks to fly, reports Tony Ho Tran for The Daily Beast. “The new design allows the drone to make better decisions when flying through completely new environments,” writes Tran, “and could have future applications in self-driving cars, search and rescue operations, wildlife monitoring, or even diagnosing medical issues.”
Prof. Marzyeh Ghassemi speaks with WBUR reporter Geoff Brumfiel about her research studying the use of artificial intelligence in healthcare. “When you take state-of-the-art machine learning methods and systems and then evaluate them on different patient groups, they do not perform equally,” says Ghassemi.
Principal Research Scientist Leo Anthony Celi co-authored a study that found “a lack of racial and gender diversity could be hindering the efforts of researchers working to improve the fairness of artificial intelligence (AI) tools in health care,” reports Carissa Wong for Nature.
MIT researchers have found that by “encouraging strategic EV charging placement, rather than allowing EV chargers to be situated merely due to charging company convenience or preferences” it may be possible to “mitigate or eliminate EV charging problems without the need for advanced technological systems of connected devices and real-time communications, which could add to costs and energy consumption,” reports Carolyn Fortuna for CleanTechnica.