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.
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."
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.”
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.”
Graduate student Matt Groh speaks with Scientific American reporter Sarah Vitak about his team’s work studying whether human detection or artificial intelligence is better at identifying deepfakes and misinformation online. “One of the things that we would suggest for the future development of these systems is trying to figure out ways to explain why the AI is making a decision,” says Groh.
TechCrunch reporter Brian Heater spotlights MIT startup Strio.AI, which is aimed at bringing autonomous picking and pruning to strawberry crops.
Indian Express reporter Sethu Pradeep writes that MIT researchers have developed a low-energy security chip designed to prevent side channel attacks on smart devices. “It can be used in any sensor nodes which connects user data,” explains graduate student Saurav Maji. “For example, it can be used in monitoring sensors in the oil and gas industry, it can be used in self-driving cars, in fingerprint matching devices and many other applications.”
Forbes contributor Patrick Rishe spotlights the 2022 MIT Sloan Sports Analytics Conference, which addressed equity analytics, the Rooney rule, sports marketing in the metaverse, and the future of AI in sports. “Advancements in technology and tracking granular layers of fan behavior at (and away from) sports venues are giving brands deeper insights on connecting a particular partnership with real consumer purchase intentions,” writes Rishe.
Axios reporter Erin Broadwin spotlights Dimagi, a digital tool for health workers in remote areas that was started by researchers from the MIT Media Lab and the Harvard-MIT Health Sciences and Technology program.