MIT researchers have developed Robust MADER, an updated version of a previous system developed in 2020 to help drones avoid in-air collisions, reports Brian Heater for TechCrunch. “The new version adds in a delay before setting out on a new trajectory,” explains Heater. “That added time will allow it to receive and process information from fellow drones and adjust as needed.”
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 researchers have developed a new hardware that offers faster computation for artificial intelligence with less energy, reports Kyle Wiggers for TechCrunch. “The researchers’ processor uses ‘protonic programmable resistors’ arranged in an array to ‘learn’ skills” explains Wiggers.
Postdoctoral researcher Murat Onen and his colleagues have created “a nanoscale resistor that transmits protons from one terminal to another,” reports Alex Wilkins for New Scientist. “The resistor uses powerful electric fields to transport protons at very high speeds without damaging or breaking the resistor itself, a problem previous solid-state proton resistors had suffered from,” explains Wilkins.
In an article for The New York Times exploring whether humans are the only species able to comprehend geometry, Siobhan Roberts spotlights Prof. Josh Tenenbaum’s approach to exploring how humans can extract so much information from minimal data, time, and energy. “Instead of being inspired by simple mathematical ideas of what a neuron does, it’s inspired by simple mathematical ideas of what thinking is,” says Tenenbaum.
Wall Street Journal reporter Sara Castellanos spotlights Prof. Markus Buehler’s work combining virtual reality with sound waves to help detect subtle changes in molecular motions. Castellanos notes that Buehler and his team recently found, “coronaviruses can be more lethal or infectious depending on the vibrations within the spike proteins that are found on the surface of the virus.”
Graduate student Shashank Srikant speaks with The Economist about his work developing a new model that can detect computer bugs and vulnerabilities that have been maliciously inserted into computer code.
Mashable spotlights Strolling Cities, a video project from the MIT-IBM Watson AI Lab, which uses AI to allow users to imagine what different words would like as a location. “Unlike other image-generating AI systems, Strolling Cities creates fictional cities every time,” Mashable notes.
Fast Company reporter Mark Wilson spotlights Strolling Cities, a new AI video project developed by researchers from the MIT-IBM Watson AI Lab, which recreates the streets of Italy based on millions of photos and words. “I decided that the beauty and sentiment, the social, historical, and psychological contents of my memories of Italy could become an artistic project, probably a form of emotional consolation,” says Mauro Martino of the MIT-IBM Watson AI Lab. “Something beautiful always comes out of nostalgia.”
Fortune reporter Jonathan Vanian writes that researchers from the MIT-IBM Watson AI Lab and IBM Research have published a paper and data set “intended to help researchers create deep learning systems that can create software code.” Vanian notes that “research like this could pave the way for more efficient methods for companies to test software, analyze code, and potentially build apps.”
Wired reporter Will Knight spotlights how MIT researchers have showed that “an AI program trained to verify that code will run safely can be deceived by making a few careful changes, like substituting certain variables, to create a harmful program.”
Verge reporter James Vincent writes that researchers at the MIT-IBM Watson AI Lab have developed an algorithm that can transform selfies into artistic portraits. The algorithm is “trained on 45,000 classical portraits to render your face in faux oil, watercolor, or ink, “Vincent explains.
Researchers from MIT and a number of other institutions have found that grammar-enriched deep learning models had a better understanding of key linguistic rules, reports Kyle Wiggers for VentureBeat. The researchers found that an AI system provided with knowledge of basic grammar, “consistently performed better than systems trained on little-to-no grammar using a fraction of the data, and that it could comprehend ‘fairly sophisticated’ rules.”
In this video, Reuters explores how MIT researchers have developed a robot that can automatically sort recycling. The robot uses a pressure sensor to squeeze items to determine how they should be sorted.