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.
MIT President L. Rafael Reif and Special Presidential Envoy for Climate John Kerry discussed the urgency of addressing climate change during the Climate Grand Challenges Showcase event, reports Chris Lisinski for the State House News Service. “Climate change has been called a ‘super wicked’ problem. In Boston, that might sound like a local way of saying ‘really hard,’ but this phrase is actually a technical term,” Reif said. “It describes any enormously complex societal problem that has no single right answer and no clear finish line as well as multiple stakeholders with conflicting priorities and no central authority empowered to solve it.”
Bloomberg News reporter Janet Wu speaks with President L. Rafael Reif and Special Presidential Envoy for Climate John Kerry during the Institute’s Climate Grand Challenges showcase event. “If you can capture the emissions -- literally, genuinely -- then you’re reducing the problem,” said Kerry of the importance of eliminating greenhouse gas emissions.
MIT announced five projects "targeting the world's toughest climate riddles" that were selected following a rigorous two-year competition, reports Benjamin Kail for Boston Business Journal. “Climate Grand Challenges represents a whole-of-MIT drive to develop game-changing advances to confront the escalating climate crisis, in time to make a difference,” says President L. Rafael Reif.
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.”
Prof. Jessika Trancik speaks with Wall Street Journal reporter Nidhi Subbaraman about the dramatic drops in costs to manufacture and sell renewable technologies. Subbaraman notes that Trancik’s research shows that “the steep drop in solar and lithium-ion battery technology was enabled by market expansion policies as well as investment in research and development by governments and the private sector.”
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.
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."
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.
With the announcement of the new MIT Morningside Academy for Design, MIT is looking to create “a hub of resources for the next generation of designers, integrating areas of study such as engineering and architecture in the process,” reports Dana Gerber for The Boston Globe. “This is really going to give us a platform to connect with the world around problems that communities are facing,” explained Prof. John Ochsendorf, who will serve as the academy’s founding director.
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.”
Profs. Ruonan Han and Qing Hu speak with Popular Science reporter Rahul Rao about their work with terahertz waves. “There’s a laundry list of potential applications,” says Hu of the promise of terahertz waves.
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.”