Artificial intelligence

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 Dynamics founder Marc Raibert ’77 has been selected as one of The Boston Globe’s Tech Power Players 50 for his work in artificial intelligence and robotics, reports Hiawatha Bray for The Boston Globe. Raibert recalls how his fascination with developing robot legs was cultivated at MIT. “I went to a presentation where someone showed a very slow-moving legged robot,” said Raibert. “I thought, wow, people and animals aren’t anything like that. ... People and animals have such fantastic locomotion. That was a thing to try to emulate and achieve.”

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.

Jerry Ting, co-founder and CEO of Evisort, found inspiration for the AI contracts provider company after working with fellow co-founder Amine Anoun SM ’17, reports Alexandra Sternlicht for Forbes. Ting “realized that firms bill hundreds of dollars per hour for lawyers to simply read documents” writes Sternlicht. “And like most startup founders, he imagined a better way.”

Eureka Robotics, an automation company based in Singapore, has developed their products based on research from MIT and Nanyang Technological University, reports Catherine Shu for TechCrunch. “It [Eureka Robotics] focuses on robotic software and systems to automate tasks that require High Accuracy and high Agility (HAHA),” writes Shu. “Its robots are used for precision handling, assembly, inspection, drilling and other tasks.”

A team of MIT scholars and journalists are underscoring that artificial intelligence could advance colonialism in a three-part series supported by the MIT Knight Science Journalism Fellowship Program and the Pulitzer Center, reports Ellen McGirt for Fortune. “While it would diminish the depth of past traumas to say the A.I. industry is repeating this violence [plunder and slavery] today, it is now using other, more insidious means to enrich the wealth and powerful at the great expense of the poor,” says the team.

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. 

MIT researchers have developed a new technique that uses deep learning to improve the process of drug discovery, reports Jonathan Vanian for Fortune. “The technique addresses a common problem that researchers face when using A.I. to develop novel molecular structures: life sciences experts can often face challenges synthesizing A.I.-created molecular structures,” writes Vanian. 

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.”

STAT reporter Katie Palmer spotlights Principal Research Scientist Leo Anthony Celi’s research underscoring the importance of improving the diversity of datasets used to design and test clinical AI systems. “The biggest concern now is that the algorithms that we’re building are only going to benefit the population that’s contributing to the dataset,” says Celi. “And none of that will have any value to those who carry the biggest burden of disease in this country, or in the world.”

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."