Computer Science and Artificial Intelligence Laboratory (CSAIL)

MIT researchers have discovered a hardware vulnerability in Apple’s M1 chips that can allow attackers to break through its security defenses, reports Carly Page for TechCrunch. “Researchers from MIT’s Computer Science and Artificial Intelligence Laboratory, however, have created a novel hardware attack, which combines memory corruption and speculative execution attacks to sidestep the security feature,” writes Page.

Researchers at MIT and Harvard Medical School have created an artificial intelligence program that can accurately identify a patient’s race based off medical images, reports Tony Ho Tran for The Daily Beast. “The reason we decided to release this paper is to draw attention to the importance of evaluating, auditing, and regulating medical AI,” explains Principal Research Scientist Leo Anthony Celi.

The Boston Globe honored a number of MIT faculty and alumni in their Tech Power Players 50, a list of the “most influential – and interesting – people in the Massachusetts technology scene.” MIT honorees include Professor Yet-Ming Chiang, Senior Lecturer Brian Halligan, Professor Tom Leighton, Professor Silvio Micali, Katie Rae (CEO and managing partner for The Engine), and Professor Daniela Rus (director of CSAIL and deputy dean of research for the MIT Schwarzman College of Computing). 

Boston Globe reporter Aaron Pressman spotlights the work of Prof. Silvio Micali, who has been honored as one of The Boston Globe’s Tech Power Players 50 for his work in computer science and cryptography. “Micali decided to come up with a more elegant version of the underlying [cryptocurrency] technology, the public database of transactions known as the blockchain,” writes Pressman. “He formed a new startup, Algorand, to pursue a blockchain that would go far beyond bitcoin while reducing costs and electricity usage and speeding up transaction processing.”

An international team of scientists, including researchers from MIT and Harvard, have found that an artificial intelligence program trained to read X-rays and CT scans can successfully predict a person’s race with 90 percent accuracy, reports Hiawatha Bray for The Boston Globe. "The research effort was born when the scientists noticed that an AI program for examining chest X-rays was more likely to miss signs of illness in Black patients," writes Bray.

MIT scientists have created a new tool that can improve robotic wearables, reports Danica D’Souza for Mashable. “The tool provides a pipeline for digital creating pneumatic actuators – devices that power motion with compressed air in many wearables and robotics,” writes D’Souza.

Prof. Silvio Micali speaks with Los Angeles Times reporter Laurence Darmiento about his predictions for the future of crypto. “The moment the blockchain starts to be used for transactions, the few blockchains that are really capable of transacting at a very low cost, they’re going to emerge, in my opinion,” says Micali. “When traditional finance starts getting on the blockchain, you’re going to see the blockchains that are really used in a massive and transactional way are going to accelerate.”

CSAIL researchers have developed a robotic arm equipped with a sensorized soft brush that can untangle hair, reports Douglas Belkin for The Wall Street Journal. “The laboratory brush is outfitted with sensors that detect tension," writes Belkin. “That tension reads as pain and is used to determine whether to use long strokes or shorter ones.”

CSAIL researchers have developed a robotic glove that utilizes pneumatic actuation to serve as an assistive wearable, reports Brian Heater for TechCrunch. “Soft pneumatic actuators are intrinsically compliant and flexible, and combined with intelligent materials, have become the backbone of many robots and assistive technologies – and rapid fabrication with our design tool can hopefully increase ease and ubiquity,” says graduate student Yiyue Luo.

TechCrunch reporter Devin Coldewey spotlights how MIT researchers have developed a machine learning technique for proposing new molecules for drug discovery that ensures suggested molecules can be synthesized in a lab. Coldewey also features how MIT scientists created a new method aimed at teaching robots how to interact with everyday objects.

During the AI Cures Conference, Prof. Regina Barzilay spoke with Food and Drug Administration senior staff fellow Amir Khan about how the agency intends to regulate artificial intelligence in medicine, reports Casey Ross for STAT.  “’My thinking is that models should be regulated based on their functionality, and not necessarily on the input data they use,” said Barzilay. 

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.

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.”