Artificial intelligence

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.

Researchers from MIT and journalists from STAT conducted a months long investigation and found that “subtle shifts in data fed into popular health care algorithms — used to warn caregivers of impending medical crises — can cause their accuracy to plummet over time, raising the prospect AI could do more harm than good in many hospitals,” reports Casey Ross for STAT.

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.”