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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.”
MIT startup ReadySet, co-founded by Alana Marzoev PhD ’18 and Jon Gjengset PhD ’20, provides database infrastructure to help developers build real-time applications, reports Kyle Wiggers for TechCrunch. “Rather than rebuilding these same broken systems, developers need solutions that slot into their existing infrastructure and achieve limitless read scaling,” says Marzoev. “With ReadySet, we aim to make the process of globally caching… query results as streamlined and automated as caching images in a content delivery system.”
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.
New research by Professor Erik Demaine, lecturer Zachary Abel, robotics engineer Martin Demaine and their colleagues explores whether it is possible to “take any polyhedral (or flat-sided) shape that’s finite (like a cube, rather than a sphere or the endless plane) and fold it flat using creases," writes Rachel Crowell for Quanta Magazine. “By moving finite to infinite ‘conceptual’ slices, they created a procedure that, taken to its mathematical extreme, produced the flattened object they were looking for,” Crowell explains.
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.
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.”
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."
Forbes contributor Rick Miller spotlights “In Pursuit of the Perfect Portfolio: The Stories, Voices, and Key Insights of the Pioneers Who Shaped the Way We Invest,” a new book by Prof. Andrew Lo and Prof. Stephen Foerster of the University of Western Ontario. The book “provides historical perspective on the development of modern investment theory and practice,” writes Miller.
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.”
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.
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.