MIT researchers have developed a new technique that uses deep learning to improve the process of drug discovery, reports Jonathan Vanian for Fortune. “The technique addresses a common problem that researchers face when using A.I. to develop novel molecular structures: life sciences experts can often face challenges synthesizing A.I.-created molecular structures,” writes Vanian.
TechCrunch reporter Brian Heater spotlights new MIT robotics research, including a team of CSAIL researchers “working on a system that utilizes a robotic arm to help people get dressed.” Heater notes that the “issue is one of robotic vision — specifically finding a method to give the system a better view of the human arm it’s working to dress.”
STAT reporter Katie Palmer spotlights Principal Research Scientist Leo Anthony Celi’s research underscoring the importance of improving the diversity of datasets used to design and test clinical AI systems. “The biggest concern now is that the algorithms that we’re building are only going to benefit the population that’s contributing to the dataset,” says Celi. “And none of that will have any value to those who carry the biggest burden of disease in this country, or in the world.”
MIT researchers and two high school seniors have developed DualFair, a new technique for removing bias from a mortgage lending dataset, reports Hiawatha Bray for The Boston Globe. “When a mortgage-lending AI was trained using DualFair and tested on real-world mortgage data from seven US states,” writes Bray, “the system was less likely to reject applications of otherwise qualified borrowers because of their race, sex, or ethnicity.”
CSAIL researchers have developed a new technique that could enable robots to handle squishy objects like pizza dough, reports Brian Heater for TechCrunch. “The system is separated into a two-step process, in which the robot must first determine the task and then execute it using a tool like a rolling pin,” writes Heater. “The system, DiffSkill, involves teaching robots complex tasks in simulations.”
MIT researchers have utilized a new reinforcement learning technique to successfully train their mini cheetah robot into hitting its fastest speed ever, reports Matt Simon for Wired. “Rather than a human prescribing exactly how the robot should walk, the robot learns from a simulator and experience to essentially achieve the ability to run both forward and backward, and turn – very, very quickly,” says PhD student Gabriel Margolis.
Using machine learning techniques, MIT researchers analyzed social media sentiment around the world during the early days of the Covid-19 pandemic and found that the “pandemic precipitated a dramatic drop in happiness,” reports Charlotte Hu for Popular Science. “We wanted to do this global study to compare different countries because they were hit by the pandemic at different times,” explains Prof. Siqi Zheng, “and they have different cultures, different political systems, and different healthcare systems.”
MIT researchers have created a new computer algorithm that has allowed the mini cheetah to maximize its speed across varying types of terrain, reports Shi En Kim for Popular Science. “What we are interested in is, given the robotic hardware, how fast can [a robot] go?” says Prof. Pulkit Agrawal. “We didn’t want to constrain the robot in arbitrary ways.”
MIT researchers have used a new reinforcement learning system to teach robots how to acclimate to complex landscapes at high speeds, reports Emmett Smith for Mashable. “After hours of simulation training, MIT’s mini-cheetah robot broke a record with its fastest run yet,” writes Smith.
CSAIL researchers developed a new machine learning system to teach the MIT mini cheetah to run, reports James Vincent for The Verge. “Using reinforcement learning, they were able to achieve a new top-speed for the robot of 3.9m/s, or roughly 8.7mph,” writes Vincent.
Gizmodo reporter Andrew Liszewski writes that CSAIL researchers developed a new AI system to teach the MIT mini cheetah how to adapt its gait, allowing it to learn to run. Using AI and simulations, “in just three hours’ time, the robot experienced 100 days worth of virtual adventures over a diverse variety of terrains,” writes Liszewski, “and learned countless new techniques for modifying its gait so that it can still effectively loco-mote from point A to point B no matter what might be underfoot.”
TechCrunch reporter Brian Heater spotlights MIT startup Strio.AI, which is aimed at bringing autonomous picking and pruning to strawberry crops.
STAT has named Noubar Afeyan ’87, Cornelia Bargmann PhD ’87, Prof. Regina Barzilay and Prof. Sangeeta N. Bhatia to their list of trailblazing researchers working in the life sciences. “Many of the STATUS List are well-known as change makers; others are largely unheralded heroes. But all have compelling stories to tell,” writes STAT.
Prof. Julie Shah speaks with The Economist about her work developing systems to help robots operate safely and efficiently with humans. “Robots need to see us as more than just an obstacle to maneuver around,” says Shah. “They need to work with us and anticipate what we need.”
Physics World reporter Tim Wogan writes that MIT researchers used machine learning techniques to identify a mysterious “X” particle in the quark–gluon plasma produced by the Large Hadron Collider. “Further studies of the particle could help explain how familiar hadrons such as protons and neutrons formed from the quark–gluon plasma believed to have been present in the early universe,” writes Wogan.