Motherboard reporter Tatyana Woodall writes that a new study co-authored by MIT researchers finds that AI models that can learn to perform new tasks from just a few examples create smaller models inside themselves to achieve these new tasks. “Learning is entangled with [existing] knowledge,” graduate student Ekin Akyürek explains. “We show that it is possible for these models to learn from examples on the fly without any parameter update we apply to the model.”
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.
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.”
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.
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.
Graduate student Matthew Groh discusses Detect Fakes, a research project he co-created aimed at teaching people how to detect deepfakes, with Wall Street Journal reporter Ann-Marie Alcántara. Groh recommends people pay attention to the context of an image or video, noting that people can “pay attention to incentives and what someone is saying and why someone might be saying this.”
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.
Vecna Technologies and Vecna Robotics co-founder Daniel Theobald ’95, MS ’97 speaks with Forbes reporter Heather Wishart-Smith about the future of robotics. “I believe that robotics can be one of the great tools for solving the world’s problems,” says Theobald. “The environment, equality, food scarcity, even happiness in allowing us to focus on being more human than today’s humans working like machines and doing jobs that really should not be done by humans.”
Alumnus Jeremy Bilotti co-founded Rarify, a design furniture retail company, reports Lauren Mowery for Forbes. “Rarify uses the history of design to tell a story, educate our audience about the importance of notable designers, and push toward the future, bringing to light noteworthy manufacturers and designers that aren’t known or recognized to the degree that they deserve,” explains Rarify co-founder David Rosenwasser. Alumnus Jeremy Bilotti co-founded Rarify, a design furniture retail company, reports Lauren Mowery for Forbes. “Rarify uses the history of design to tell a story, educate our audience about the importance of notable designers, and push toward the future, bringing to light noteworthy manufacturers and designers that aren’t known or recognized to the degree that they deserve,” explains Rarify co-founder David Rosenwasser.
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.
Fortune reporter Sydney Lake spotlights MIT’s free online “Introduction to Computer Science and Programming Using Python” course. “The course also covers topics including data structures and simple algorithms,” writes Lake.