An MIT study has found that the wide spread adoption of self-driving cars could lead to increased carbon emissions, reports Rima Sabina Aouf for Dezeen. “The study found that with a mass global take up of autonomous vehicles, the powerful onboard computers needed to run them could generate as many greenhouse gas emissions as all the data centers in operation today,” writes Aouf.
National Geographic reporter Maya Wei-Haas explores how the ancient art of origami is being applied to fields such a robotics, medicine and space exploration. Wei-Haas notes that Prof. Daniela Rus and her team developed a robot that can fold to fit inside a pill capsule, while Prof. Erik Demaine has designed complex, curving fold patterns. “You get these really impressive 3D forms with very simple creasing,” says Demaine.
A new study by MIT researchers finds that “the energy required to run computers in a future global fleet of autonomous vehicles could produce as much greenhouse gas emissions as all the data centers in the world,” reports Sharon Udasin for The Hill. The researchers found that “1 billion such cars, each driving for an hour daily, would use enough energy to generate the same amount of emissions that data centers do today.”
NBC 1st Look host Chelsea Cabarcas visits MIT to learn more about how faculty, researchers and students are “pioneering the world of tomorrow.” Cabarcas meets the MIT Solar Electric Vehicle team and gets a peek at Nimbus, the single-occupant vehicle that team members raced in the American Solar Challenge from Kansas City to New Mexico. Cabarcas also sees the back-flipping MIT mini cheetah that could one day be used in disaster-relief operations.
Prof. Steven Barrett speaks with New York Times reporter Paige McClanahan about the pressing need to make air travel more sustainable and his research exploring the impact of contrails on the planet’s temperature. “Eliminating contrails is quite a big lever on mitigating the climate impact of aviation,” said Barrett.
Politico reporter Derek Robertson writes that a new study by MIT researchers finds the computing power required to replace the world’s auto fleet with AVs would produce about the same amount of greenhouse gas emissions as all the data centers currently operating. Robertson writes that the researchers view the experiment “as an important step in getting auto- and policymakers to pay closer attention to the unexpected ways in which the carbon footprint for new tech can increase.”
Graduate student Soumya Sudhakar speaks with BBC Digital Planet host Gareth Mitchell about her new study showing that hardware efficiency for self-driving cars will need to advance rapidly to avoid generating as many greenhouse gas emissions as all the data centers in the world.
Using statistical modeling, MIT researchers have found that the energy needed to power a fleet of fully autonomous EVs could generate as much carbon emissions as all the world’s data centers combined, reports Andrew Paul for Popular Science.
Washington Post reporter Pranshu Verma writes that a new study by MIT researchers finds the “future energy required to run just the computers on a global fleet of autonomous vehicles could generate as much greenhouse gas emissions as all the data centers in the world today.”
A review led Prof. Marzyeh Ghassemi has found that a major issue in health-related machine learning models “is the relative scarcity of publicly available data sets in medicine,” reports Emily Sohn for Nature.
Researchers from the MIT-IBM Watson AI Lab and the Harvard Natural Language Processing Group developed the Giant Language model Test Room (GLTR), an algorithm that attempts to detect if text was written by a bot, reports Megan Morrone for Fast Company. “Using the ‘it takes one to know one’ method, if the GLTR algorithm can predict the next word in a sentence, then it will assume that sentence has been written by a bot,” explains Morrone.
MIT spinout Gaia A is developing a forest management building tool aimed at providing foresters with the resources to make data-driven decisions, reports Haje Jan Kamps and Brian Heater for TechCrunch. “The company is currently using lidar and computer vision tech to gather data but is ultimately building a data platform to tackle some of the big questions in forestry,” writes Kamps and Heater.
Rosina Samadani ’89, MS ’92 co-developed EyeBox, an algorithm-based non-invasive diagnostic test for concussions, reports Geri Stengel for Forbes. “Patients watch a video, and the device watches their eyes for 220 seconds with a very high-quality, high-frequency infrared camera that measures eye movements and provides a score based on those eye movements,” explains Stengel. “The score is correlated with the absence or presence of a concussion.”
Research affiliate Ramin Hasani speaks with Kimberly Adams of Marketplace about how he and his CSAIL colleagues solved a differential equation dating back to the early 1900s, enabling researchers to create an AI algorithm that can learn on the spot and adapt to evolving patterns. The new algorithm “will enable larger-scale brain simulations,” Hasani explains.
Popular Science reporter Charlotte Hu writes that MIT researchers have developed a new machine learning model that can depict how the sound around a listener changes as they move through a certain space. “We’re mostly modeling the spatial acoustics, so the [focus is on] reverberations,” explains graduate student Yilun Du. “Maybe if you’re in a concert hall, there are a lot of reverberations, maybe if you’re in a cathedral, there are many echoes versus if you’re in a small room, there isn’t really any echo.”