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.
Using machine learning techniques, MIT researchers have detected “X particles” produced by the Large Hadron Collider, reports Rahul Rao for Popular Science. “The results tell us more about an artifact from the very earliest ticks of history, writes Rao. “Quark-gluon plasma filled the universe in the first millionths of a second of its life, before what we recognize as matter—molecules, atoms, or even protons or neutrons—had formed.”
Scientists have discovered “X-particles” in the aftermath of collisions produced in the Large Hadron Collider, which could shed light on the structure of these elusive particles, reports Becky Ferreira for Vice. “X particles can yield broader insights about the type of environment that existed in those searing and turbulent moments after the Big Bang,” writes Ferreira.
Prof. David Mindell writes for Forbes about the premise behind his new book, “The Work of the Future: Building Better Jobs in the Age of Intelligent Machine,” which he wrote with Prof. David Autor, and Elisabeth Reynolds, former director of MIT’s Industrial Performance Center. The new book “concerns demographic shifts in the United States that will generate consistent labor shortages for a generation; the continued profusion of information technology and mobile phones into legacy sectors such as logistics, construction, and transportation; technology-enabled remote work, conferencing, and training; and a long-term need for improved training, reeducation, and upskilling among low – and middle – skill workers,” writes Mindell.
Prof. David Autor speaks with New York Times columnist Peter Coy about the new book he wrote with Prof. David Mindell and Elisabeth Reynolds, “The Work of the Future: Building Better Jobs in an Age of Intelligent Machines.” Autor explains that: “Most people’s fear of technology is really a fear of capitalism, what the markets will do with the technology. You can’t make a lot of progress if you’re making people poorer at the same time.”
A new study by MIT researchers finds people are more likely to interact with a smart device if it demonstrates more humanlike attributes, reports Brian Heater for TechCrunch. The researchers found “users are more likely to engage with both the device — and each other — more when it exhibits some form of social cues,” writes Heater. “That can mean something as simple as the face/screen of the device rotating to meet the speaker’s gaze.”
STAT reporters Katie Palmer and Casey Ross spotlight how Prof. Regina Barzilay has developed an AI tool called Mirai that can identify early signs of breast cancer from mammograms. “Mirai’s predictions were rolled into a screening tool called Tempo, which resulted in earlier detection compared to a standard annual screening,” writes Palmer and Ross.
In an article for The Wall Street Journal about next generation technologies that can create and quantify personal health data, Laura Cooper spotlights Prof. Dina Katabi’s work developing a noninvasive device that sits in a person’s home and can help track breathing, heart rate, movement, gait, time in bed and the length and quality of sleep. The device “could be used in the homes of seniors and others to help detect early signs of serious medical conditions, and as an alternative to wearables,” writes Cooper.