Machine learning

Researchers at MIT and Massachusetts General Hospital have developed “Sybil” – an artificial intelligence tool that can predict the risk of a patient developing lung cancer within six years, reports Mallika Marshall for CBS Boston. 

CSAIL researchers are developing a drone that could be used to assist humans in search and rescue operations, reports Mashable. “If you are trying to detect someone in an avalanche and you can maybe just send a drone, and if they can find them out and then fly towards them and send a help signal. It would be probably be something that would be beneficial to people as an immediate application.”

Prof. Daniela Rus, director of CSAIL, speaks with Popular Science reporter Charlotte Hu about the field of artificial intelligence, explaining the difference between AI, robotics and machine learning, and exploring the future of AI. “[AI algorithms] can do really extraordinary things much faster than we can. But the way to think about it is that they’re tools that are supposed to augment and enhance how we operate,” says Rus. “And like any other tools, these solutions are not inherently good or bad. They are what we choose to do with them.”

Researchers at MIT have developed an autonomous vehicle with “mini sensors to allow it to see the world and also with an artificially intelligent computer brain that can allow it to drive,” explains postdoctoral associate Alexander Amini in an interview with Mashable. “Our autonomous vehicles are able to learn directly from humans how to drive a car so they can be deployed and interact in brand new environments that they’ve never seen before,” Amini notes.

MIT researchers have developed a new AI tool called Sybil that could help predict whether a patient will get lung cancer up to six years in advance, reports Pranshu Verma for The Washington Post.  “Much of the technology involves analyzing large troves of medical scans, data sets or images, then feeding them into complex artificial intelligence software,” Verma explains. “From there, computers are trained to spot images of tumors or other abnormalities.”

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.

Prof. Nick Montfort speaks with GBH All Things Considered host Arun Rath about ChatGPT, its potential impact on the future of academia and how instructors could adapt their courses in light of this new technology.

Research fellow Michael Schrage speaks with Fortune reporter Sheryl Estrada about how generative A.I. will impact finance. “I think, increasingly, we’re going to be seeing generative A.I. used for financial forecasts and scenario generation,” says Schrage.

Prof. Daniela Rus, director of CSAIL, discusses the future of artificial intelligence, emphasizing the importance of balancing the development of new technologies with the need to ensure they are deployed in a way that benefits humanity. “We have to advance the science and engineering of autonomy and the science and engineering of intelligence to create the kinds of machines that will be friendly to people, that will be assistive and supportive for people and that will augment people with the tasks that they need help with,” Rus explains.

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 1^st 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.

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.”

Researchers from MIT and Harvard have developed “a new type of electrically conductive hydrogel ‘scaffold’ that could eventually be used to create a soft brain-computer interface (or BCI) that translates neural signals from the brain into machine-readable instructions,” reports Adam Bluestein for Fast Company.

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.

Researchers from MIT and Mass General Hospital have developed “a deep learning model named ‘Sybil’ that can be used to predict lung cancer risk, using data from just a single CT scan,” writes Sai Balasubramanian for Forbes. “Sybil is able to predict a patient’s future lung cancer risk to a certain extent of accuracy, using the data from just one LDCT [low-dose computed tomography scan],” writes Balasubramanian.