MIT scientists have created a new tool that can improve robotic wearables, reports Danica D’Souza for Mashable. “The tool provides a pipeline for digital creating pneumatic actuators – devices that power motion with compressed air in many wearables and robotics,” writes D’Souza.
CSAIL researchers have developed a robotic glove that utilizes pneumatic actuation to serve as an assistive wearable, reports Brian Heater for TechCrunch. “Soft pneumatic actuators are intrinsically compliant and flexible, and combined with intelligent materials, have become the backbone of many robots and assistive technologies – and rapid fabrication with our design tool can hopefully increase ease and ubiquity,” says graduate student Yiyue Luo.
Graduate student Anna Waldman-Brown writes for Wired about the future of automation technology and how it can impact labor dynamics in the future. “While some scholars believe that our fates are predetermined by the technologies themselves, emerging evidence indicates that we may have considerable influence over how such machines are employed within our factories and offices – if we can only figure out how to wield this power,” writes Waldman-Brown.
TechCrunch reporter Kyle Wiggers spotlights how MIT researchers have developed a new computer vision algorithm that can identify images down to the individual pixel. The new algorithm is a “vast improvement over the conventional method of ‘teaching’ an algorithm to spot and classify objects in pictures and videos,” writes Wiggers.
Fast Company reporter Connie Lin spotlights how Algorand, an MIT startup founded by Prof. Silvio Micali, dimmed the lights in Times Square on April 21 to help conserve energy and demonstrate how cryptocurrency could reduce energy consumption. Algorand has developed a carbon-negative blockchain protocol and “utilizes a pure proof-of-stake consensus mechanism to verify authentic transactions.”
Boston Globe reporter Robert Weisman spotlights how researchers at the MIT AgeLab are “designing prototypes of ‘smart homes’ for older residents, equipped with social robots, voice-activated speakers that give medication reminders, motion sensors embedded in carpets to detect falls, and intelligent doorbells that double as security cameras.”
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.”
The MIT AI Hardware Program seeks to bring together researchers from academia and industry to “examine each step of designing and manufacturing the hardware behind AI-powered technologies,” reports Emily Bamforth for EdScoop. “This program is about accelerating the development of new hardware to implement AI algorithms so we can do justice to the capabilities that computer scientists are developing,” explains Prof. Jesús del Alamo.
MIT researchers have developed reconfigurable, self-assembling robotic cubes embedded with electromagnets that allow the robots to easily change shape, reports John Koetsier for Forbes. “If each of those cubes can pivot with respect to their neighbors you can actually reconfigure your first 3D structure into any other arbitrary 3D structure,” explains graduate student Martin Nisser.
The MIT AI Hardware Program is aimed at bringing together academia and industry to develop energy-optimized machine-learning and quantum-computing systems, reports Katyanna Quach for The Register. “As progress in algorithms and data sets continues at a brisk pace, hardware must keep up or the promise of AI will not be realized,” explains Professor Jesús del Alamo. “That is why it is critically important that research takes place on AI hardware."
TechCrunch reporters Christine Hall, Anita Ramaswamy, Connie Loizos and Mary Ann Azevedo spotlight Sribuu, an AI-powered personal financial advisor in Indonesia, co-founded by Nadia Amalia ’20. The company is aimed at helping “users make better money decisions with our wealth management tools and give personalized saving advice based on their financial habits,” they write.
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.
Fortune reporter Jeremy Kahn spotlights a study co-authored by Prof. Marzyeh Ghassemi exploring issues associated with “explainable” AI systems that are being applied in fields such as healthcare, finance and government. The researchers explain that those using such systems “might have misunderstood the capabilities of contemporary explainability techniques—they can produce broad descriptions of how the AI system works in a general sense but, for individual decisions, the explanations are unreliable or, in some instances, only offer superficial levels of explanation.”